Patent Publication Number: US-9414235-B2

Title: Method and apparatus for spectrum allocation in wireless networks

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of pending U.S. patent application Ser. No. 11/557,176, filed on Nov. 7, 2006, entitled “METHOD AND APPARATUS FOR SPECTRUM ALLOCATION IN WIRELESS NETWORKS,” which is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to the field of communication networks and, more specifically, to dynamic spectrum allocation in wireless networks. 
     BACKGROUND OF THE INVENTION 
     The radio spectrum resources in most countries around the world are statically partitioned into blocks allocated for different purposes. The characteristics of spectrum usage, such as type of service (e.g., cellular, public safety, television broadcast), maximum transmit power, and the like are a-priori specified, and are time and location invariant. Disadvantageously, such current spectrum allocation rules were based on antiquated technology assumptions, as well as in the absence of adequate market mechanisms. Furthermore, existing spectrum management processes, involving primary user licensing or property rights on one extreme and complete unlicensed usage, or commons, on the opposite extreme, have numerous serious drawbacks resulting in disadvantageous associated business and operational implications. 
     The business implications of such existing spectrum management processes has led to purpose-built networks involving capital-intensive steps of acquiring licensed spectrum, deploying network infrastructure, and offering and operating end-user services over the network infrastructure using the licensed spectrum. Furthermore, the process of acquiring licensed spectrum tends to be a slow, expensive process, especially due to the “big player syndrome” in which only very large service providers can compete. Disadvantageously, such business implications result in slow innovation of both networks and services (e.g., as evident in the inordinate delays in the introduction of Third Generation (3G) wireless networks). 
     The operational implications of such existing spectrum management processes include: (1) although a majority of spectrum is licensed for use, a large portion of the licensed spectrum is highly underutilized (e.g., public safety spectrum, military and government spectrum, and certain ultra high frequency (UHF) television spectrum) and (2) several licensed spectrum bands (e.g., cellular and personal communications services (PCS) bands) are utilized, however, utilization varies dramatically over space and time. By contrast, unlicensed spectrum bands have experienced unfettered network deployment due to low cost technology. In other words, spectrum is access limited rather than throughput limited. 
     SUMMARY OF THE INVENTION 
     Various deficiencies in the prior art are addressed through the invention of a method and apparatus for dynamically allocating available spectrum within a region comprising a plurality of base stations. The method includes obtaining a plurality of spectrum demands associated with at least a portion of the base stations, each spectrum demand requesting a portion of the available spectrum, and determining a plurality of spectrum allocations satisfying at least a portion of the spectrum demands. The spectrum allocations are determined by processing the spectrum demands using network information such as base station information associated with the base stations and region information such as terrain information associated with the region. The region information may include signal propagation information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The teachings of the present invention can be readily understood by considering the following detailed description in conjunction with the accompanying drawings, in which: 
         FIG. 1  depicts a high-level block diagram of a communication network; 
         FIG. 2  depicts a plurality of time periods in which candidate spectrum demands are processed for determining spectrum allocations; 
         FIG. 3  depicts a method according to one embodiment of the present invention; 
         FIG. 4  depicts a method according to one embodiment of the present invention; 
         FIG. 5  depicts a high-level block diagram of a region including base stations associated with multiple providers and an associated conflict graph representative of various conflicts within the region; 
         FIG. 6  depicts a method according to one embodiment of the present invention; 
         FIG. 7  depicts a high-level block diagram of a communication network including a plurality of regions managed by a respective plurality of networked spectrum broker systems; and 
         FIG. 8  depicts a high-level block diagram of a general-purpose computer suitable for use in performing the functions described herein. 
     
    
    
     To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. 
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention provides spectrum management using dynamic spectrum allocation. The present invention implements a coordinated network in which allocation of spectrum within a region is dynamically controlled by a spectrum broker system utilizing regional spectrum demand aggregation and centralized spectrum management decisions. The present invention, utilizing dynamic wireless spectrum allocation methods, offers time-bounded access to a band of spectrum to various entities (e.g., to radio infrastructure providers (RIPs), network service providers (NSPs), and the like). In one embodiment, the present invention provides dynamic spectrum allocation in wireless networks employing Coordinated Dynamic Spectrum Access (C-DSA). 
     The present invention may utilize one of a plurality of demand processing models (e.g., batched, online, and the like) and one of a plurality of spectrum pricing models (e.g., merchant mode, simple bidding, iterative bidding, and the like) for providing spectrum management in various wireless network infrastructure configurations (e.g., shared base stations with collocated antennas, non-shared base stations with collocated antennas, non-shared base stations with non-collocated antennas, and the like). The present invention utilizes various spectrum management functions (e.g., scope, access fairness, stickiness, spectrum utilization, and the like, as well as various combinations thereof) for identifying and providing spectrum allocations. 
     In one embodiment, spectrum allocations may be determined by processing spectrum demands for satisfying spectrum allocation constraints. In one embodiment, spectrum allocation constraints may include demand model constraints determined using a spectrum demand model and conflict representation constraints. The spectrum demand model may include spectrum demand parameters (e.g., amount of spectrum requested, base stations requesting spectrum, and the like, as well as various combinations thereof) and spectrum demand constraints (e.g., scope constraints, timing constraints, and the like, as well as various combinations thereof). The conflict representation constraints may be determined using a representation of conflicts between base stations, which may include co-cell provider conflicts, co-located cross-provider conflict, remote cross-provider conflict, or a soft-handoff conflict. 
     Although primarily depicted and described herein as performing spectrum allocation within a single region, in one embodiment, the present invention may perform spectrum allocation across multiple regions. Although primarily depicted and described herein within the context of a macro-cellular network utilizing CDMA, spectrum allocation utilizing the present invention may be extended for use in cellular networks utilizing various other access technologies (e.g., TDMA, OFDM, and the like), as well as various other wireless networks (e.g., fixed wireless networks, mobile wireless networks, wireless mesh networks, and the like, as well as various combinations thereof). 
       FIG. 1  depicts a high-level block diagram of a communication network. Specifically, communication network  100  of  FIG. 1  includes a region  101  including a first plurality of base stations (BSs)  104   A1 - 104   A4  (collectively, BSs  104   A ) controlled by a first network management system (NMS)  110   A  using a respective plurality of communication links (CLs)  108   A1 - 108   A4  (collectively, CLs  108   A ), a second plurality of base stations (BSs)  104   B1 - 104   B4  (collectively, BSs  104   B ) controlled by a second network management system (NMS)  110   B  using a respective plurality of communication links (CLs)  108   B1 - 108   B4  (collectively, CLs  108   B ). As depicted in  FIG. 1 , BSs  104   A  and  104   B  are collectively denoted as BSs  104  and NMSs  110   A  and  110   B  are collectively denoted as NMSs  110 . 
     As depicted in  FIG. 1 , communication network  100  includes a spectrum broker system (SBS)  130  in communication with a communication network (CN)  120  using a communication link (CL)  122 . As depicted in  FIG. 1 , CN  120  communicates with NMSs  110   A  and  110   B  using a respective plurality of communication links (CLs)  112   A - 112   B  (collectively, CLs  112 ). In one embodiment, SBS  130  may be owned by a spectrum service provider (SSP), such as a private-owned company, a government-owned company, a regulatory authority (e.g., FCC), and the like. Although not specifically depicted, CN  120  may include any communication network for supporting communications between SBS  130  and NSMs  110 . For example, CN  120  may be a packet-based network (e.g., an Internet Protocol (IP) network). 
     As depicted in  FIG. 1 , NMSs  110   A  and  110   B  control BSs  104   A  and  104   B , respectively. As depicted in  FIG. 1 , BSs  104  communicate with respective pluralities of wireless terminals. Specifically, BSs  104   A1 - 104   A4  communicate with respective pluralities of wireless terminals (WTs)  102   A1 - 102   A4  (collectively, WTs  102   A ) using respective pluralities of wireless communication links (WCLs)  103   A1 - 103   A4  (collectively, WTs  103   A ), and BSs  104   B1 - 104   B4  communicate with respective pluralities of wireless terminals (WTs)  102   B1 - 102   B4  (collectively, WTs  102   B ) using respective pluralities of wireless communication links (WCLs)  103   B1 - 103   B4  (collectively, WTs  103   B ). The WTs  102   A  and  102   B  are collectively denoted as WTs  102 . As depicted in  FIG. 1 , WTs  102  may include any devices operable for supporting wireless communications, including computers, phones, and the like. 
     As depicted in  FIG. 1 , wireless networks located within region  101  comprise cellular wireless networks. In one embodiment, wireless networks located within region  101  may be implemented using any cellular wireless technology, such as Code Division Multiple Access 2000 (CDMA2000) (e.g., 1×RTT, EV-DO, EV-DV, and the like), IMT-2000 (e.g., UMTS, GSM/CPRS/EDGE, and the like), WiMAX, TDMA, OFDM, and the like, as well as various combinations thereof. Although depicted and described herein with respect to cellular wireless networks, in one embodiment, the present invention may perform dynamic spectrum allocation in various other wireless networks such as fixed wireless networks, mesh wireless networks, and the like, as well as various combinations thereof. 
     In general, spectrum requirements vary across such technologies. For example, CDMA networks based on 1×RTT require 1.23 MHz of spectrum per carrier frequency, and within this class CDMA 3×RTT requires 3.75 MHz of spectrum per carrier frequency, while UMTS networks require 5 MHz of spectrum per carrier frequency. Furthermore, since different providers may deploy and operate such technologies differently, spectrum requirements may vary across providers within such technology classes. In one embodiment, since spectrum requirements vary across such technologies, as well as varying implementations of such technologies by different service providers, spectrum allocation may be performed in a manner for accounting for such spectrum requirement differences. 
     As depicted in  FIG. 1 , NMSs  110  operate as network management systems adapted for controlling various radio access network (e.g., BSs  104 ) functions. In one embodiment, NSMs  110  may control services provided over respective radio access networks. In one embodiment, NSMs  110  may control infrastructure of respective radio access networks. In one embodiment, at least a portion of NMSs  110  may include Radio Access Network Managers (RANMANs). In one embodiment, NMSs  110  may be located within network operations centers (not depicted) of respective service providers operating NMSs  110 . As depicted in  FIG. 1 , NMSs  110   A  and  110   B  aggregate spectrum demands associated with BSs  104   A  and  104   B , respectively. The NMSs  110  provide the aggregated spectrum demands to SBS  130  for use in determining spectrum allocations. 
     In one embodiment, spectrum demands comprise actual spectrum demands. In one embodiment, actual spectrum demands aggregated by NMSs  110  include actual spectrum requests received from associated BSs  104 . In one embodiment, actual spectrum requests received from BSs  104  include actual spectrum requests received by BSs  104  from associated WTs  102 . In one such embodiment, WTs  102  may use bidirectional control channels for signaling bandwidth requirements to respective BSs  104 . In one embodiment, respective control portions of BSs  104  may aggregate the actual spectrum requests received by BSs  104  from associated WTs  102  for transmission to associated NMSs  110 . In one embodiment, instantaneous bandwidth demands from WTs  102  may be aggregated in order to determine respective total spectrum demands associated with BSs  104 , respectively. 
     In one embodiment, aggregation of instantaneous bandwidth demands for determining a total spectrum demand may be performed by a spectrum estimator. In one embodiment, the spectrum estimator is operable for relating capacity requests to required spectrum amounts required for satisfying the capacity requests. In one embodiment, implementation of a spectrum estimator may be technology specific, requiring knowledge of modulation, scheduling, in-field measurements, and the like, as well as various combinations thereof. For example, assuming ten end users using wireless terminals in communication with a base station request 1 Mbps each, and a 1.25 MHz CDMA channel can support only two such end users, a spectrum estimator may determine that up to five CDMA channels must be configured at the base station for supporting the ten end users. 
     In one embodiment, spectrum demands comprise predicted spectrum demands. In one embodiment, predicted spectrum demands aggregated by NMSs  110  include predicted spectrum demands received from associated BSs  104 . In one embodiment, respective control portions of BSs  104  may predict spectrum demands. In one such embodiment, respective control portions of BSs  104  may predict expected end user demands in order to determine predicted spectrum demands. In one embodiment, predicted spectrum demands aggregated by NMSs  110  include predicted spectrum demands determined by NMSs  110 , respectively. In one embodiment, predicted spectrum demands may be estimated using historical spectrum demand information, historical spectrum utilization information, and the like, as well as various combinations thereof. 
     As depicted in  FIG. 1 , SBS  130  controls access to spectrum for a given geographical region R (illustratively, region  101 ). In one embodiment, region R under control of SBS  130  may include BSs  102  of a plurality of different service providers. For example, as depicted in  FIG. 1 , NMS  110   A  and BSs  104   A  belong to a first service provider and NMSs  110   B  and BSs  104   B  belong to a second service provider. In one embodiment, BSs  104  may be owned by network service providers (NSPs) providing services (e.g., voice, Internet access, telemetry, and the like, as well as various combinations thereof) to end users (illustratively, TDs  102 ). In this embodiment, NSPs perform radio infrastructure control functions typically provided by radio infrastructure providers (RIPs). In one embodiment, NMSs  110  and BSs  104  may be owned by respective RIPs. In this embodiment, NSPs may be customers of RIPs, utilizing radio infrastructure of the RIPs for providing various services. 
     In one embodiment, a region includes one RIP which owns the radio infrastructure in the region. In one embodiment, base stations deployed in the region may serve multiple homogenous NSPs (e.g., NSPs utilizing CDMA). In one embodiment, base stations deployed in the region may serve multiple heterogeneous NSPs (e.g., NSPs utilizing CDMA, TDMA, OFDM, and the like). In one embodiment, a physical base station may be modeled as a plurality of logical base stations. In one such embodiment, the physical base station may be modeled as a plurality of logical base stations such that a logical base station is assigned at various granularities (e.g., one logical base station for each NSP, one logical base station for each group of end users, one logical base station per end user, and the like, as well as various combinations thereof. 
     In the context of the SSP associated with SBS  130 , a general advantage of this embodiment is that SBS  130  may maintain a single terrain propagation model for all RIPs supported by SBS  130 . In the context of the NSPs supported by SBS  130 , this embodiment has several advantages. The wireless endpoints of all NSPs are controlled using the same power control algorithm supported by the associated base station, thereby enabling sharing of a carrier frequency and CDMA codes among different NSPs within a cell. Furthermore, co-existence of multiple access technologies across the base station enables more efficient spectrum allocation and, therefore, more efficient spectrum utilization. 
     For example, assuming that there are ten wireless endpoints in the cell, of which six wireless endpoints require ˜300 Kbps packet downloads and four wireless endpoints require standard voice traffic, the associated RIP can get 1.25 MHz spectrum and operate an EV-DO channel and a single 200 KHz GSM carrier for supporting the remaining voice customers in an 8-slot TDMA system. A homogenous single base station would have either reduced per-endpoint throughput by sharing 1.25 MHz among all endpoints or by allocating two 1.25 MHz channels. In other words, sharing of base stations among NSPs allows efficient use of spectrum across different NSP demands and end user demands. 
     In one embodiment, a region includes collocated antennas supporting multiple RIP base stations (i.e., although the antenna tower is shared, base stations of different RIPs operate independently). An example of this configuration is a town or city which requires all NSPs operating in the area to use a common tower for the associated antennas. In one such embodiment, within the context of CDMA networks, although a nearly identical radio environment is visible to all NSPs, due to the cross-provider conflict, only a subset of the user population is visible to the CDMA power control algorithms associated with the respective RIPs. 
     The cross provider conflict may be demonstrated using an example network having a first RIP with wireless endpoints located far from the base station and a second RIP with wireless endpoints located near the base station, the power control algorithm for the first RIP would require the associated wireless endpoints to transmit at high powers, interfering with transmissions from wireless terminals associated with the second RIP (and therefore transmitting at lower powers). From this example, the cross provider conflict requires that same carrier frequencies be assigned to base stations of two different NSPs if the base stations have overlapping regions of coverage. In this configuration, even if design changes can be made for assigning different pilot channels, synchronization channels, paging channels, and power channels to different RIPs in the same carrier channel, the cross provider conflict constraint must be strictly observed. 
     In one embodiment, a region includes non-collocated antennas and non-collocated base stations (i.e., the infrastructure of different providers is deployed independently). While the cross provider conflict applies to such regions, the cross provider conflict may be partially relaxed under certain circumstances. In one such embodiment, if base stations of different RIPs are sufficiently separated in radio distance (as predicted by terrain propagation models) interference induced by assigning the same carrier to different RIPs may be managed (e.g., by accounting for the interference as an added penalty). This embodiment allows more aggressive spectrum reuse across RIPs within the region. In one embodiment, such a region configured using non-collocated antennas and non-collocated base stations may require each RIP operating in the region to provide a separate terrain propagation map for base station placement. 
     Although primarily described herein with respect to macro-cellular networks, in one embodiment, the present invention may be used to allocate spectrum in regions including macro-cellular networks having embedded micro-cellular networks. For example, micro-cellular networks may include in-building networks, hotspot networks, and the like. In one such embodiment, in the context of CDMA networks, mobile nodes in the micro-cells are invisible to the macro-cell power control. If the power levels in the micro-cells are sufficiently low, and signal leak out of structures into the associated macro-cell is not significant, same carriers can be reused in the micro-cell. The nature of the network infrastructure for which spectrum is being allocated may be considered in developing spectrum management algorithms for SBS  130 . 
     In one embodiment, SBS  130  controls access to a portion of available spectrum (i.e., portion of overall spectrum available for dynamic allocation). In one embodiment, the portion of the overall spectrum available for allocation by SBS  130  may be referred to as a Coordinated Access Band (CAB). In one embodiment, the owner of SBS  130  may own the CAB. In one embodiment, for example, portions of spectrum which may be included within CAB include Specialized Mobile Radio (SMR) (851-854/806-809 MHz, 861-866/816-821 MHz), public safety bands (764-776, 794-806 MHz), and unused broadcast UHF TV channels (450-470 MHz, 470-512 MHz (channels 14-20), 512-698 MHz (channels 21-51), 698-806 MHz (channels 52-69). Although specific portions of overall spectrum have been identified, the present invention may be utilized for allocating any portion of the overall spectrum. 
     As described herein, SBS  130  controls the CAB spectrum, granting time-bound portions of the spectrum to spectrum requesters (illustratively, NMSs  110 ). As described herein, spectrum allocations may specify spectrum allocation parameters such as extent of spatial region for spectrum, maximum transmission power, spectrum allocation exclusivity, and the like, as well as various combinations thereof. In one such embodiment, spectrum allocation may be represented as a spectrum lease. In this embodiment, compliant use of the allocated spectrum requires that the holder of the spectrum lease satisfy various spectrum lease conditions (i.e., spectrum utilization conditions) such as meeting power budget constraints, releasing the allocated spectrum upon termination of the spectrum lease, and the like, as well as various combinations thereof. 
     In one embodiment, spectrum allocation may be performed by freely sharing the entire CAB band. In one embodiment, spectrum allocation may be performed by splitting the CAB band into a plurality of CAB sub-bands. In one embodiment, one or more CAB sub-bands may be assigned to each network class (e.g., at least one CAB sub-band for CDMA networks, at least one CAB sub-band for each TDMA network, at least one CAB sub-band for each OFDM network, and the like). In one such embodiment, sub-band boundaries between each of the CAB sub-bands may vary in response to spectrum demand characteristics (i.e., CAB sub-band boundaries are not a priori fixed). In one embodiment, SBS  130  controls a contiguous band of B MHz spectrum that is divided into N contiguous channels having C MHz (such that N=B/C). 
     In one embodiment, SBS  130  determines spectrum allocations using aggregated demands from NMSs  110 . In one embodiment, SBS  130  determines spectrum allocations using base station location information (i.e., using respective geographical locations of BSs  104 ). In one embodiment, SBS  130  determines spectrum allocations using propagation characterization information (i.e., signal propagation characteristics, radio interference environment characteristics, and the like, as well as various combinations thereof, associated with the region controlled by SBS  130 ). In one embodiment, SBS  130  determines spectrum allocations using a conflict graph which captures various constraints associated with spectrum allocation (e.g., co-cell separation, co-located cross-provider conflict, remote cross-provider conflict, and the like, as well as various combinations thereof). In one embodiment, the conflict graph formulation may be solved using one of a plurality of allocation formulations. 
     In one embodiment, SBS  130  determines spectrum allocations using base station information. In one embodiment, base station information associated with BSs  104  may be registered by respective NMSs  110  and provided to SBS  130 . In one embodiment, base station information includes base station location information. In one such embodiment, base station location of base station BS i  may be denoted as BS(x i , y i ). In one embodiment, base station information includes base station capability information. In one embodiment, base station capability information is maintained for each BS  104 . In one embodiment, base station capability information includes the number of radio transmit-receiver (TRX) (radio interface) units (denoted as K i ), maximum power P max (i) per TRX, minimum frequency band and maximum frequency band per TRX, and the like, as well as various combinations thereof. 
     In one embodiment, SBS  130  determines spectrum allocations using terrain information. In one embodiment, terrain information includes geographical information associated with the region (e.g., locations, extent, and other parameters associated with mountains, valleys, and the like, as well as other geographical information). In one embodiment, terrain information may include signal propagation information. In one embodiment, signal propagation information may include signal propagation characterization information (i.e., information for characterizing the propagation of signals within the region, which may include geographical information, atmospheric information, and the like, as well as other information). In one embodiment, for example, terrain information may include geographical information associated with the geographical terrain of the region. 
     In one embodiment, signal propagation characterization information includes (or, alternatively, is used in conjunction with, applied as input to, and the like) at least one empirical radio propagation model for frequencies under consideration for spectrum allocation. In one embodiment, signal propagation characterization information may be adapted for use in estimating regions of interference, respective extents of interference in such identified regions of interference, and the like, as well as various combinations thereof. For example, for a given transmit power at a given base station location BS(x i , y i ) within region  101 , SBS  130  estimates received power at all other base station locations within region  101 . In one such embodiment, using such information, SBS  130  estimates regions of interference and associated extent of interference between at least a portion of the pairs of TRXs in region  101 . 
     In one embodiment, signal propagation characterization information includes at least one of a static characterization of the radio interference environment or a dynamic characterization of the radio interference environment in the associated region for which SBS  130  operates as a spectrum allocation manager. In one embodiment, in which a dynamic characterization of the radio interference environment is performed, such characterizations may be performed using at least one of coordinated spectrum sensing (e.g., performed by associated WTs  102 ), radio interference sensing in the data path, and the like, as well as various combinations thereof. In one embodiment, SBS  130  may rely on dynamic characterization of radio interference environment for improved spectrum allocation, enforcement of spectrum allocation terms, and the like, as well as various combinations thereof. 
     In one embodiment, SBS  130  determines spectrum allocations using at least one spectrum pricing model (e.g., merchant mode, simple bidding, iterative bidding, and the like). In one embodiment, depending upon the spectrum pricing model utilized by SBS  130 , SBS  130  may process spectrum demands for determining spectrum allocations (e.g., merchant mode) or spectrum offers (e.g., simple bidding or iterative bidding). In an embodiment, in which SBS  130  processes spectrum demands for determining spectrum offers, SBS  130  communicates spectrum offers to NSMs  110 . In one such embodiment, NMSs  110  process the spectrum offers and provide spectrum offer responses to SBS  130 . In this embodiment, SBS  130  processes the spectrum offer responses from NSMs  110  for determining spectrum allocations. As described herein, irrespective of the pricing model used by SBS  130 , SBS  130  ultimately provides spectrum allocations to NSMs  110 . 
     As described herein, in one embodiment, spectrum pricing for use in spectrum allocation may utilize one of a plurality of spectrum pricing modes including merchant mode, simple bidding, iterative bidding, and the like, as well as various combinations thereof. In a merchant mode spectrum pricing embodiment, spectrum price is determined by the network (e.g., by SBS  130 ) based upon current spectrum demand and spectrum utilization. In a merchant mode model, spectrum price is advertised on a “take it or leave it” basis and is allocated on a “first come first serve” basis. In other words, in a merchant mode model, no negotiation between SBS  130  and the provider is performed. The merchant mode model is generally appropriate when spectrum demand is less than available spectrum. 
     In a simple bidding spectrum pricing embodiment, an initial spectrum price is determined by the network (e.g., SBS  130 ) based upon current spectrum demand and spectrum utilization; however, as opposed to the merchant mode spectrum pricing embodiment, in the simple bidding spectrum pricing embodiment, the spectrum consumer (i.e., provider initiating the spectrum demand) may include within a spectrum demand a requested spectrum price that the spectrum consumer is willing to pay. In a simple bidding model, the network (e.g., SBS  130 ) collects all spectrum demands including the respective requested spectrum prices for all locations in the region. If available spectrum exceeds spectrum demand, each of the spectrum demands may be fulfilled. If spectrum demand exceeds available spectrum, since a portion of the spectrum demands cannot be fulfilled, machine-driven auctions may be conducted in order to decide the winning bidders to which the available spectrum is allocated. In one embodiment, a winning bid is binding for the originator of the spectrum demand. In one embodiment, a combinatorial auction may be used. 
     In an iterative bidding spectrum pricing embodiment, similar to a simple bidding spectrum pricing embodiment, an initial spectrum price is determined by the network (e.g., SBS  130 ). In the simple bidding spectrum pricing embodiment, however, cases may arise in which spectrum allocation fails for a winning spectrum bid. In such cases, iterative bidding spectrum pricing may be performed. In one embodiment, iterative bidding includes reiterating the simple bidding process to account for the identified conflict in spectrum allocation. In one embodiment, iterative bidding includes additional negotiation between network and spectrum consumer to allow the spectrum consumer to either accept a portion of the requested spectrum or release at least a portion of the spectrum demands initiated by the spectrum consumer. In such embodiments, an additional bidding iteration may be performed in which the network reconsiders spectrum demands (bids) that were rejected in earlier bidding rounds. In one embodiment, iterative bidding spectrum pricing may require an additional bidding protocol, as well as a bidding scheme with convergence guarantees and tight time constraints. 
     As depicted in  FIG. 1 , NMSs  110  receive spectrum allocations from SBS  130 . In one embodiment, spectrum allocations identify transmission frequencies that may be utilized by respective BSs  104  associated with NSMs  110 , as well as the time period over which the transmission frequencies may be utilized. In one further embodiment, spectrum allocations may identify transmission powers that may be utilized by respective BSs  104  associated with NSMs  110  (i.e., for controlling the respective sizes of WCAs  106  associated with BSs  104 ). By controlling transmission frequencies and powers of BSs  104 , cross-provider conflict may be controlled. In one embodiment, NMSs  110  implement the spectrum allocations received from SBS  130  by signaling respective BSs  104  associated with NMSs  110 . 
       FIG. 2  depicts a plurality of time periods in which candidate spectrum demands are processed for determining spectrum allocations. As depicted in  FIG. 2 , candidate spectrum demands (i.e., spectrum demands considered for possible allocation) are determined for each of a plurality of fixed-length time periods (n−2) through (n+3). As depicted in  FIG. 2 , candidate spectrum demands determined in one time period are considered for allocation in an immediately subsequent time period. For example, as depicted in  FIG. 2 , candidate spectrum demands determined in time period (n−2) are processed for possible allocation in subsequent time period (n−1). For purposes of clarity, each candidate spectrum demand and spectrum allocation is represented using a vertical arrow. 
     Although depicted and described with respect to processing of candidate spectrum demands in one time period for allocation in a subsequent time period, various other spectrum demand processing models may be implemented. In one embodiment, candidate spectrum demands associated with one time period (e.g., time period (n−2)) are processed for allocation in a subsequent time period other than the immediately subsequent time period (e.g., n, (n+1), and other subsequent time periods). In one embodiment, candidate spectrum demands associated with a plurality of adjacent time periods (e.g., n and (n+1)) may be processed for allocation in a subsequent time period (e.g., (n+2), (n+3), and the like). In one embodiment, processing of candidate spectrum demands for allocation may be performed using a sliding window implementation. 
     In one embodiment, candidate spectrum demands may be determined by processing received spectrum demands. For example, SBS  130  may process spectrum demands received from NMSs  110  for determining candidate spectrum demands. In one embodiment, processing of candidate spectrum demands for determining spectrum allocations may be performed using base station information and signal propagation characterization information. In one embodiment, processing of candidate spectrum demands for determining spectrum allocations may be performed using infrastructure models, demand models, stickiness models, access fairness models, pricing models, conflict graphs, conflict graph processing algorithms, and the like, as well as various combinations thereof. 
     As described herein, in one embodiment, SBS  130  determines spectrum allocations by processing spectrum demands. In one embodiment, SBS  130  may perform online processing of spectrum demands (i.e., each spectrum demand is processed as it is received). In one such embodiment, if the received spectrum demand is admitted, the spectrum demand may be configured in the appropriate base station(s) independent of future spectrum demands. In one embodiment, SBS  130  may perform batched processing of spectrum demands. In one such embodiment, spectrum requests received within a window (e.g., window i of T units) may be combined and processed together. In this embodiment, spectrum demands that are admitted in the system and allocated are activated in a subsequent window (e.g., window (i+1)). 
     In general, batched processing of spectrum demands provides numerous advantages in spectrum allocation and management. In one embodiment, batched processing of spectrum demands guarantees a fixed, maximum latency for a spectrum demand. In one embodiment, batched processing of spectrum demands allows correlation and aggregation of temporally and spatially clustered spectrum demands to optimize spectrum allocation. In one embodiment, batched processing of spectrum demands enables allocation and de-allocation of spectrum at fixed intervals, thereby enabling network elements and wireless endpoints to predict transitions and allow higher level protocols to gracefully adapt to possible connectivity disruptions. 
     In one embodiment, allocation of spectrum in response to a spectrum demand depends on the scope of the spectrum demands (denoted as demand scope). A provider request for spectrum is typically for a particular base station location, however, depending on the multiple access technology used by the provider, interference considerations cause spectrum allocated in one cell to impact spectrum allocations in adjacent cells. In TDMA networks with conservative system configurations, in order to avoid interference, carrier frequencies allocated in a base station of one cell cannot be reused in adjacent cells. In CDMA networks, carrier frequencies allocated in a base station of one cell may be reused in adjacent cells, multi-path interference may be exploited constructively, and a soft handoff capability may be supported by tracking pilot signals from adjacent base stations. 
     In one embodiment, such spectrum demand considerations may be represented using a demand scope parameter. In one embodiment, the demand scope parameter may be represented using a two-tuple (denoted as (x,y), where x and y are parameter attributes). In one embodiment, demand scope (x,y) may represent a number of adjacent base stations taken into account during spectrum allocation. In CDMA networks, demand scope (x,y) defines a region over which the same frequency must be reused. In GSM networks, demand scope (x,y) defines a region over which the same frequency cannot be reused. Although described herein with respect to a two-tuple, a demand scope parameter may be implemented using fewer or more parameter attributes. Although described herein with respect to a single demand scope parameter, in one embodiment, a plurality of demand scope parameters may be used for characterizing demand scope. 
     In one embodiment, in a system utilizing batched processing of spectrum demands, a simple spectrum allocation model generates spectrum leases valid for a time duration of T units; however, small lease durations may result in frequent spectrum demands and, therefore, significant disruption in network services provided to wireless endpoints. In one embodiment, in order to prevent such disruptions, a provider may request spectrum for a duration that is multiples of T units (i.e., a larger the multiple produces longer spectrum availability and, therefore, better service continuity). In order to represent this concept, a number of consecutive time slots in which a provider may continue to use spectrum allocated in response to an accepted spectrum demand may be denoted as stickiness. 
     In a system utilizing stickiness, in a high spectrum demand situation, if spectrum is allocated for an extended period of time (i.e., allocated demand sticks in the system), spectrum capacity may be monopolized such that providers requesting spectrum may be denied an opportunity to access the requested spectrum. In one embodiment, spectrum allocation may be performed in a manner that ensures that no single provider obtains prolonged access to spectrum without paying a proportionately higher price for the prolonged spectrum access. In one such embodiment, this concept is denoted as access fairness. In an embodiment utilizing stickiness and access fairness to allocate spectrum, contention between stickiness and access fairness is moderated. 
     In one embodiment, in which stickiness is used to allocate spectrum, stickiness may be modeled as one of: deterministic sticky or probabilistic sticky. In a deterministic sticky model, a spectrum demand is guaranteed to be sticky for the requested number of timeslots N. The deterministic sticky model is a form of advanced reservation. In a probabilistic sticky model, continuation of a demand in a future time slot is probabilistic. In one embodiment, SBS  130  performs a coin toss algorithm to decide if the demand is reconsidered during selection of the spectrum allocations to be configured in the next timeslot. In one embodiment, the stickiness probability is determined as a function of the number of consecutive timeslots already used, the maximum number of timeslots requested, and the system utilization and service provider utilization in the previous (n−1) timeslots. In one such embodiment, the stickiness probability function may be implemented as (Eq. 1): 
     
       
         
           
             
               
                 
                   
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     In this embodiment, as spectrum demand is satisfied for an increasing number of consecutive timeslots, the probability of the spectrum demand continuing to being satisfied drops as a function of the maximum number of timeslots N and the number of timeslots allocated up to the current time. In this embodiment, if the spectrum demand has already been satisfied for (n−1) timeslots, during which the allocated spectrum has experienced significant utilization, it is desirable for SBS  130  to continue to satisfy the spectrum demand. This characteristic is implemented using the numerator of Eq. 1, which is an increasing function of system and demand specific utilization. In one embodiment, stickiness probability may alternatively be referred to as favorability rating. 
     In one embodiment, in which fairness is used to allocate spectrum, fairness may be modeled using one or more of a plurality of fairness criteria. In one embodiment, max-min fairness, which attempts to maximize the allocation of the spectrum demand receiving the poorest allocation, may be utilized. In one embodiment, weighted max-min fairness may be used to allocate spectrum. In one such embodiment, favorability ratings (e.g., stickiness probability as described herein) may be used as the weights in the weighted max-min fairness implementation. 
     In one embodiment, spectrum management requires tracking of the utilization of allocated spectrum. This is generally necessary to ensure that large providers do not use financial power to demand large portions of available spectrum in order to starve small providers from obtaining spectrum allocations. In one such embodiment, since it is critical that a provider demonstrate use of allocated spectrum, providers may be required to demonstrate that use of the allocated spectrum satisfies a threshold. In one embodiment, SSP may be required to monitor allocated spectrum utilization associated with spectrum allocated to providers. In one embodiment, providers allocated spectrum may be required to report allocated spectrum utilization to the SSP. In one such embodiment, providers must provide past allocated spectrum utilization information for use in allocation of spectrum in response to future demands (i.e., future spectrum demands). 
     In one embodiment, spectrum allocation is performed using spectrum demand modeled using one of a plurality of spectrum demand models. In one embodiment, each spectrum demand is modeled as an n-tuple including at least one of a plurality of parameters including: (1) base station location (e.g., the location of the base station at which the demand will be configured); (2) requested spectrum amount (e.g., the amount of required spectrum; (3) spectrum allocation duration (e.g., the length of time for which the spectrum is scheduled to be used, which may be referred to as sticky length); (4) provider identifier (e.g., an identifier associated with the provider making the spectrum demand); (5) price bid; (6) allocated spectrum utilization (e.g., a spectrum utilization history associated with the provider); and (7) demand scope (e.g., the spatial region over which the allocated spectrum is scheduled to be instantiated); and the like, as well as various combinations thereof. 
     In one embodiment, in which a spectrum demand model includes a requested spectrum amount parameter, the requested spectrum amount parameter may be specified as a range [d min , d max ] such that at least d min  amount of spectrum (e.g., d min  channels) and at most d max  amount of spectrum (e.g., d max  channels) is allocated to a provider. In one embodiment, in which a spectrum demand model includes a requested spectrum amount parameter, the requested spectrum amount parameter may be specified as a minimum value [d min ] (i.e., an “all-or-nothing” demand model in which a provider requests only d min  amount of spectrum. Although primarily described herein with respect to a specific spectrum demand model including specific spectrum demand model parameters, various other spectrum demand models may be defined and utilized for allocating spectrum. 
       FIG. 3  depicts a method according to one embodiment of the present invention. Specifically, method  300  of  FIG. 3  includes a method for allocating spectrum in response to spectrum demands. Although described with respect to processing and allocation of spectrum demands in adjacent time periods, respectively, method  300  may be repeated for each pair of adjacent time periods, as well as various other combinations of time periods. Although depicted as being performed serially, those skilled in the art will appreciate that at least a portion of the steps of method  300  may be performed contemporaneously, or in a different order than presented in  FIG. 3 . The method  300  begins at step  302  and proceeds to step  304 . 
     At step  304 , base station information is obtained. In one embodiment, base station information includes base station location information, characteristic information, and the like, as well as various combinations thereof. At step  306 , terrain information is obtained. In one embodiment, terrain information includes signal propagation information. In one embodiment, signal propagation information includes information adapted for characterizing the propagation of signals within the region. In one embodiment, signal propagation characterization information includes radio propagation models, radio interference environment characterizations, and the like, as well as various combinations thereof. At step  308 , spectrum demand information is obtained. At step  310 , candidate spectrum demands are determined. In one embodiment, candidate spectrum demands may be determined using the method depicted and described with respect to  FIG. 4 . 
     At step  312 , spectrum allocations are determined. In one embodiment, spectrum allocations may be determined by processing the base station location information, signal propagation characterization information, and candidate spectrum demands using various spectrum allocation algorithms. In one such embodiment, spectrum allocation algorithms may utilize various infrastructure models, demand models, stickiness models, access fairness models, pricing models, and the like, as well as various combinations thereof. At step  314 , spectrum allocations are communicated to the network. In one embodiment, spectrum allocations include spectrum allocation information (e.g., allocated transmission frequencies, allocated transmission powers, spectrum allocation durations (e.g., one time period or multiple time periods), and the like) required for utilizing the respective spectrum allocations. 
     At step  316 , the network is reconfigured according to the spectrum allocations. In one embodiment, the network is reconfigured according to the spectrum allocation information. For example, in one embodiment, network reconfiguration includes reconfiguring base stations to use transmission frequencies, transmission powers, and the like, over specific time intervals. In one embodiment, a network management system may provide spectrum allocation information to a controller portion of a base station. In this embodiment, the controller portion of the base station may signal transceiver portions of the base station for utilizing the spectrum allocations according to spectrum allocation information. For example, as depicted in  FIG. 1 , SBS  130  communicates spectrum allocations to NMSs  110  which reconfigure BSs  104  for utilizing the spectrum allocations. At step  318 , method  300  ends. 
       FIG. 4  depicts a method according to one embodiment of the present invention. Specifically, method  400  of  FIG. 4  includes a method for determining candidate spectrum demands for use in determining spectrum allocations. Although depicted as being performed serially, those skilled in the art will appreciate that at least a portion of the steps of method  400  may be performed contemporaneously, or in a different order than presented in  FIG. 4 . The method  400  begins as step  402  (at which point the current time period is denoted as time period n) and proceeds to step  404 . 
     At step  404 , a spectrum allocation map (denoted as SAM(n)) is obtained for a current time period. In one embodiment, spectrum allocation map SAM(n) identifies portions of available spectrum allocated during current time period n. In one embodiment, spectrum allocation map SAM(n) includes spectrum allocations for each base station in the region, and may be denoted as SAM(x i , y i ,n) (where x i , y i  denotes base station location). At step  406 , a free spectrum map (denoted as FSM(n)) is obtained for a current time period. In one embodiment, free spectrum map FSM(n) identifies portions of available spectrum unallocated during current time period n. In one embodiment, free spectrum map FSM(n) includes unallocated spectrum for each base station in the region, and may be denoted as FSM(x i , y i , n) (where x i , y i  denotes base station location). 
     At step  408 , a new demand set (denoted as D new (n)) is determined. In one embodiment, new demand set D new (n) is determined by aggregating spectrum demands received from the network (illustratively, from NMSs  110 ) during the current time period. In one embodiment, in which spectrum demands received during a current time period are processed during the current time period for determining spectrum allocations for an immediately following time period (i.e., next time period (n+1)), new demand set D new (n) includes spectrum demands received over only a portion of the current time period (denoted as n−Δ) where Δ denotes a portion of current time period n during which spectrum demand processing is performed for determining spectrum allocations for next time period (n+1). 
     At step  410 , a stick demand set (denoted as D sticky (n)) is determined. In one embodiment, sticky demand set D sticky (n) is determined by identifying current spectrum allocations (i.e., allocated in the current time period n) configured to continue into at least the next time period (i.e., which have been allocated for at least the next time period (n+1)). At step  412 , a candidate demand set (denoted as D cand (n+1)) is determined. The candidate demand set D cand (n+1) includes spectrum demands processed during current time period n for determining spectrum allocations in next time period (n+1). In one embodiment, candidate demand set D cand (n+1) is determined using new demand set D new (n) and sticky demand set D sticky (n)). In one embodiment, candidate demand set D cand (n+1) is determined as a union of new demand set D new (n) and sticky demand set D sticky (n)) (i.e., D new (n) U D sticky (n)). 
     At step  414 , spectrum allocations for next time period (n+1) are determined using candidate demand set D candidate (n+1) for next time period (n+1). In one embodiment, spectrum allocations may be determined by processing candidate demand set D candidate (n+1) using a spectrum allocation algorithm. In one embodiment, the spectrum allocation algorithm may include an algorithm for processing a conflict graph. In one such embodiment, the spectrum allocation algorithm may include one of a maximum service algorithm, a minimum penalty algorithm, and the like, as well as various combinations thereof. Although not depicted, spectrum allocations may be implemented within the network in the next time period, as depicted and described herein with respect to  FIG. 2  and  FIG. 3 . 
     At step  416 , an expired demand set (denoted as D expired (n)) is determined. The expired demand set D expired (n) includes spectrum demands expiring at the end of current time period n. At step  418 , spectrum allocation map SAM(n) utilized for processing spectrum demands in current time interval n is updated to form spectrum allocation map SAM(n+1) adapted for processing spectrum demands in next time interval (n+1). At step  420 , spectrum allocation map SAM(n) and free spectrum map FSM(n) utilized for processing spectrum demands in current time interval n are updated to form spectrum allocation map SAM(n+1) and free spectrum map FSM(n+1), respectively, adapted for processing spectrum demands in next time interval (n+1). At step  422 , current time period n becomes next time period (n+1) as denoted by n=n+1. From step  422 , method  400  returns to step  404  where method  400  continues from time period to time period. 
     As described herein, in one embodiment, spectrum allocation within a region may be determined using a spectrum allocation algorithm. In one embodiment, the spectrum allocation algorithm may process spectrum demands using various infrastructure models, demand models, stickiness models, access fairness models, pricing models, and the like, as well as various combinations thereof. In one embodiment, spectrum allocation within a region may be performed subject to various constraints. In one such embodiment, in which spectrum allocation is performed within a region of a homogenous CDMA network, spectrum allocation (e.g., assignment of spectrum channels to TRXs associated with base stations within a region) may be performed subject to at least one of: (1) co-cell separation conflict constraints, (2) co-located cross-provider conflict constraints, (3) remote cross-provider conflict constraints, and (4) soft-handoff conflict constraints. 
     In one embodiment, the co-cell separation constraint requires that the same channel cannot be assigned to two different TRXs within a base station. In one embodiment, the co-located cross-provider conflict constraint requires that co-located base stations associated with different providers that share antenna infrastructure cannot be assigned the same carrier. In one embodiment, the remote cross-provider conflict constraint requires that the same channel cannot be assigned to different TRXs in remote base stations corresponding to different providers if the TRXs are within interference range of each other (however, this constraint may be violated if limited to be below a limit l max ). In one embodiment, the soft-handoff constraint requires that, given a base station of a provider, the same channel should preferably be assigned to all base stations within the scope of the given base station. 
     In one embodiment, in which spectrum allocation is performed within a region of a homogenous CDMA network in which CDMA is utilized by each provider for providing wireless service, constraints such as co-cell separation, co-located cross-provider conflict, remote cross-provider conflict, soft-handoff constraints, and the like, as well as various combinations thereof may be represented using a conflict graph. An exemplary region having base stations associated with multiple different providers, and an associated conflict graph generated for representing various constraints associated with the exemplary region, is depicted and described herein with respect to  FIG. 5 . 
       FIG. 5  depicts a high-level block diagram of a region including base stations associated with multiple providers and an associated conflict graph representative of various conflicts within the region. Specifically,  FIG. 5  includes a region  501  having a plurality of quadrants  502   1 - 502   4  (collectively, quadrants  502 ). As depicted in  FIG. 5 , region  501  includes a plurality of base stations (BSs)  504   1 - 504   10  (collectively, BSs  504 ). The BSs  504   1 ,  504   2 , and  504   3  are located within quadrant  502   1 , BSs  504   4 ,  504   5 , and  504   10  are located within quadrant  502   2 , BSs  504   6 ,  504   7 , and  504   8  are located within quadrant  502   3 , and BS  504   9  is located within quadrant  502   4 . The BSs  504   1 ,  504   8 , and  504   10  belong to a first provider (provider A), BSs  504   2 ,  504   5 ,  504   6 , and  504   9  belong to a second provider (provider B), and BSs  504   3 ,  504   4 , and  504   7  belong to a third provider (provider C). 
     As depicted in  FIG. 5 , region  501  may be processed for producing a conflict graph  510  representative of various potential conflicts between BSs  504 . As depicted in  FIG. 5 , conflict graph  510  includes a plurality of base station representations (denoted as vertices)  514   1 - 514   10  representative of BSs  504   1 - 504   10 , respectively. In one embodiment, pairs of vertices v, w belonging to different providers that cannot be assigned the same portion of spectrum (e.g., channel) may be considered to be coupled by a conflict edge (v,w) between the vertices. As depicted in  FIG. 5 , conflict graph  510  includes a plurality of conflict edges  512  between the following vertex pairs: (1,2), (1,3), (1,4), (2,3), (2,6), (3,10), (4,5), (4,6), (4,9), (4,10), (5,10), (6,8), (7,8), (7,9), and (8,9). 
     As depicted in  FIG. 5 , vertices corresponding to the same provider do not share conflict edges. As depicted in  FIG. 5 , since BSs  504   1 ,  504   2 , and  504   3  are located within quadrant  502   1  (and may be determined to be within interference range of each other), and belong to different providers, vertices  514   1 ,  514   2 , and  514   3  share conflict edges. As depicted in  FIG. 5 , although BSs  504   1  and  504   9  belong to different providers, BSs  504   1  and  504   9  are located within different quadrants (and may be determined to be outside of the interference range of each other), and, as such, vertices  514   1  and  514   9  do not share conflict edges. In one embodiment, such determinations as to whether base stations are located within interference range of each other may be performed using a terrain propagation model for the portion of spectrum (e.g., frequency) under consideration. 
     Although not specifically described, conflict graph  510  includes other conflict edges representative of various constraints. In one embodiment, such constraints comprise hard constraints that cannot be violated during spectrum allocation. In continuation of the previous example, such constraints apply to edges (1,3), (7,8) and (5,10) and like edges which capture co-located cross-provider conflict. Although such constraints primarily apply to a configuration in which co-located base stations of different providers sharing antenna infrastructure, similar constraints may be identified for various other configurations (e.g., single provider per region, non-co-located base stations and non-co-located antennas, and the like, as well as various combinations thereof). For example, in non-co-located base station and non-co-located antenna configurations, such constraints may render spectrum allocation infeasible. 
     Although depicted and described herein (illustratively, with respect to  FIG. 5 ) as a graphical representation, in one embodiment, a conflict graph representative of various conflicts may be represented mathematically without an associated graphical representation. In one embodiment, a conflict graph G may be defined in which each base station is represented by a single node or vertex v in the graph G=(V,E), |V|=n, |E|=m. In one embodiment, pairs of nodes v, w belonging to different providers that cannot be assigned the same channel may be considered to be coupled by an edge (v,w) between the nodes. In other words, conflict graph G represents configurations in which base stations of different providers are physically separated or co-located. 
     In continuation of the conflict graph representation, m(v) may denote the total number of channels assigned to the base station corresponding to node v, and f may denote an assigned channel. A variable x vf  may be defined such that x vf =1 if assigned channel f is within the set of available channels at node v (denoted as F(v)) (i.e., if f ∈ F(v)) and x vf =0 otherwise (i.e., if f ∉ F(v)). In this representation, a summation of x vf  values over all assigned channels f within the set of available channels at node v (i.e., over f ∈ F(v)) is equal to m(v) for all nodes v ∈ V. In this representation, if node v is assigned channel f and node w is assigned channel v, and an edge vw exists, the distance between nodes may be denoted in terms of the assigned channels as |f−g|. Using this notation, associated with every edge vw is a set T vw  including all forbidden distances such that for all d ∈ T vw ={0, 1, . . . , d(vw)−1}, |f−g|≧d. 
     In one embodiment, since hard constraints may render channel assignment infeasible, at least a portion of such hard constraints may be replaced with associated soft constraints which can be violated if required. In one such embodiment, for a pair of nodes v and w, a penalty function p vw (f,g)=F v ×F w →  may be defined which captures interference levels when frequencies f and g are assigned to nodes v and w, respectively. In a CDMA network, p vw  may be defined such that p vw (f,g)=c co (vw,f) if f=g and x vf =l(f,g) otherwise (i.e., if f≠g). In one such embodiment, terrain propagation models may be used in predicting p vw (f). In one embodiment, such soft constraints represent the remote cross-provider conflict. 
     Although primarily described herein with respect to a specific conflict graph representation, various other conflict graphs may be defined for representing various combinations of constraints associated with spectrum allocation methodologies. For example, in one embodiment, an alternative representation of a conflict graph may be defined in which each TRX at a base station is represented by a single vertex (as opposed to representing a base station using a single vertex). In this embodiment, assuming base station BS i  has K i  TRX units, base station BS i  is represented by K i  nodes. In one such embodiment, since the co-cell separation constraint requires that a conflict graph include an edge between all K i  interfaces, the conflict graph of this region may be quite large. 
     In one embodiment, a representation of various combinations of constraints (e.g., a conflict graph) may be processed for determining spectrum allocations within a region. In one embodiment, representations of various combinations of constraints may be processed using a feasibility DSA (F-DSA) formulation adapted for determining spectrum allocations. In one embodiment, representations of various combinations of constraints may be processed using a maximum service DSA (MAXSERV-DSA) formulation adapted for determining spectrum allocations. In one embodiment, representations of various combinations of constraints may be processed using a minimum penalty DSA (MINPEN-DSA) formulation adapted for determining spectrum allocations. Although specific formulations may be described herein, various other formulations may be used for determining dynamic spectrum allocations. 
     In one embodiment, a feasibility DSA (F-DSA) formulation may be used for determining spectrum allocation solutions. The F-DSA formulation satisfies specific constraints (as represented in Eqs. 2, 3, and 4 below). The first constraint (Eq. 2) requires that the total number of channels assigned to node v equal demand d requested by node v. The second constraint (Eq. 3) requires that if two nodes v and w are assigned frequencies f and g, respectively, the penalty must be less than the maximum tolerable threshold p max , accounting for the soft cross-provider conflict constraint. In the third criteria (Eq. 4), which accounts for the hard cross-provider conflict constraint, T vw  represents the set of blocked distance for edge e=(v,w). The set T vw  may alternatively be computed using the requirement that p vw (f,g) not exceed p max  for a given (f,g). The F-DSA formulation does not optimize any criteria, and may not account for scope constraint desirable in CDMA systems. 
     
       
         
           
             
               
                 
                   
                     
                       
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     In one embodiment, a maximum service DSA (MAXSERV-DSA) formulation which maximizes a number of assigned frequencies meeting required demands may be used for determining spectrum allocations. The MAXSERV-DSA formulation satisfies specific constraints (as represented in Eqs. 5, 6, 7 below). In a MAXSERV-DSA formulation, a binary variable z vwf , which represents a configuration in which vertex v and a neighbor of vertex v are both assigned channel f, may be defined such that z vwf =1 if x vf =x wf =1 and z vwf =0 otherwise. In this formulation, a term α(v) may be defined (as shown in Eq. 5) as the sum of z vwf  over scope(v), representing the level to which the soft-handoff constraint is satisfied over the scope. In one embodiment, for each vertex v, scope(v) is a-priori defined based on received spectrum demands. 
     
       
         
           
             
               
                 
                   
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     In one embodiment of the MAXSERV-DSA formulation, n(v) may represent the number of channels assigned to vertex v in response to demand [d min , d max ]. In this embodiment, a first objective is to meet demands of a maximum number of vertices (e.g., RIPs, BSs, TRXs, and the like, as well as various combinations thereof). In this embodiment, a second objective is to meet the soft handoff constraint of the maximum number of vertices. In one embodiment, the MAXSERV-DSA formulation may be characterized using various combinations of objective functions, constraints, criteria, and the like, as well as various combinations thereof. In one such embodiment, the objective functions, constraints, criteria, and the like may be defined as follows (as in Eq. 6 subject to conditions 6.1-6.6): 
     
       
         
           
             
               
                 
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                           . 
                           
                             α 
                             ⁡ 
                             
                               ( 
                               v 
                               ) 
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                   
                       
                   
                   ⁢ 
                   6 
                 
               
             
             
               
                 
                   
                     n 
                     ⁡ 
                     
                       ( 
                       v 
                       ) 
                     
                   
                   ≤ 
                   
                     
                       d 
                       max 
                     
                     ⁡ 
                     
                       ( 
                       v 
                       ) 
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.1 
                 
               
             
             
               
                 
                   
                     d 
                     min 
                   
                   ≤ 
                   
                     n 
                     ⁡ 
                     
                       ( 
                       v 
                       ) 
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.2 
                 
               
             
             
               
                 
                   
                     n 
                     ⁡ 
                     
                       ( 
                       v 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         f 
                         ∈ 
                         
                           F 
                           ⁡ 
                           
                             ( 
                             v 
                             ) 
                           
                         
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       x 
                       vf 
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.3 
                 
               
             
             
               
                 
                   
                     
                       
                         x 
                         vf 
                       
                       + 
                       
                         x 
                         wg 
                       
                     
                     ≤ 
                     1 
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     v 
                     , 
                     
                       w 
                       ∈ 
                       V 
                     
                     , 
                     
                       f 
                       ∈ 
                       
                         F 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                     , 
                     
                       w 
                       ∈ 
                       
                         F 
                         ⁡ 
                         
                           ( 
                           w 
                           ) 
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       
                         p 
                         vw 
                       
                       ⁡ 
                       
                         ( 
                         
                           f 
                           , 
                           g 
                         
                         ) 
                       
                     
                     ≥ 
                     
                       p 
                       max 
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.4 
                 
               
             
             
               
                 
                   
                     
                       
                         x 
                         vf 
                       
                       + 
                       
                         x 
                         wf 
                       
                     
                     ≤ 
                     
                       1 
                       + 
                       
                         z 
                         vwf 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       ∀ 
                       v 
                     
                     , 
                     
                       
                         w 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           s 
                           . 
                           t 
                           . 
                           
                               
                           
                           ⁢ 
                           w 
                         
                       
                       ∈ 
                       
                         scope 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.5 
                 
               
             
             
               
                 
                   
                     
                       
                         z 
                         vwf 
                       
                       ≤ 
                       
                         x 
                         vf 
                       
                     
                     , 
                     
                       x 
                       wf 
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       ∀ 
                       v 
                     
                     , 
                     
                       
                         w 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           s 
                           . 
                           t 
                           . 
                           
                               
                           
                           ⁢ 
                           w 
                         
                       
                       ∈ 
                       
                         scope 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   6.6 
                 
               
             
           
         
       
     
     In this MAXSERV-DSA formulation, the objective function (Eq. 6) uses a weighting factor w to indicate relative importance of two criteria (i.e., n(v) and α(v)). For example, for a weighting factor w=0.8, the importance of maximizing the number of channels assigned to each vertex v in response to an associated spectrum demand (n(v)) is 80% and the importance of maximizing the number of vertices for which soft-handoff constraint is satisfied (α(v)) is 20%. The first constraint and second constraints (Con. 6.1, 6.2) represent the requirement that n(v) satisfy the demand criteria. The third and fourth constraints (Con. 6.3, 6.4) represent cross-provider constraint (similar to an F-DSA formulation). In one embodiment, the fourth constraint may be simplified by using a formulation (T vw ={0}) accounting only for co-channel interference. The fifth and sixth constraints (Con. 6.5, 6.6) ensure that z vwf  obtains correct values as specified in the first constraint. 
     In one embodiment, a minimum penalty DSA (MINPEN-DSA) formulation which minimizes total interference in a region may be used for determining spectrum allocation solutions. In one embodiment, an objective is to minimize the sum of penalties p vwfg  incurred by all channel assignments associated with each pair of vertices v and w having associated frequencies f and g, respectively. In one such embodiment, two types of interference may be considered: (1) interference resulting from a channel being assigned to a base station for a single provider and (2) interference resulting from a channel being assigned to base stations of different providers with a certain penalty. The total co-channel interference in the region resulting from a channel being assigned to base stations of different providers with a certain penalty may be represented as in Eq. 7. The most general penalty constraint may be characterized as in Eq. 8. The total co-channel interference resulting from a channel being assigned to a base station for a single provider may be represented as in  FIG. 9 . 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       
                         ( 
                         
                           v 
                           , 
                           w 
                         
                         ) 
                       
                       ∈ 
                       E 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         
                           f 
                           ∈ 
                           
                             F 
                             ⁡ 
                             
                               ( 
                               v 
                               ) 
                             
                           
                         
                         , 
                         
                           f 
                           ∈ 
                           
                             F 
                             ⁡ 
                             
                               ( 
                               w 
                               ) 
                             
                           
                         
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         p 
                         vwfg 
                       
                       ⁢ 
                       
                         x 
                         vf 
                       
                       ⁢ 
                       
                         x 
                         wf 
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                   
                       
                   
                   ⁢ 
                   7 
                 
               
             
             
               
                 
                   
                     ∑ 
                     
                       
                         ( 
                         
                           v 
                           , 
                           w 
                         
                         ) 
                       
                       ∈ 
                       E 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         
                           f 
                           ∈ 
                           
                             F 
                             ⁡ 
                             
                               ( 
                               v 
                               ) 
                             
                           
                         
                         , 
                         
                           g 
                           ∈ 
                           
                             F 
                             ⁡ 
                             
                               ( 
                               w 
                               ) 
                             
                           
                         
                         , 
                         
                           
                              
                             
                               f 
                               - 
                               g 
                             
                              
                           
                           ∈ 
                           
                             T 
                             vw 
                           
                         
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         p 
                         vwfg 
                       
                       ⁢ 
                       
                         x 
                         vf 
                       
                       ⁢ 
                       
                         x 
                         wg 
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                   
                       
                   
                   ⁢ 
                   8 
                 
               
             
             
               
                 
                   
                     ∑ 
                     
                       v 
                       ∈ 
                       V 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         w 
                         ∈ 
                         
                           scope 
                           ⁡ 
                           
                             ( 
                             v 
                             ) 
                           
                         
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         p 
                         vwf 
                       
                       ⁢ 
                       
                         x 
                         vf 
                       
                       ⁢ 
                       
                         x 
                         wf 
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                   
                       
                   
                   ⁢ 
                   9 
                 
               
             
           
         
       
     
     In one embodiment, total co-channel interference resulting from a channel being assigned to a base station for a single provider may be handled by power control mechanisms. In one such embodiment, explicit reduction of co-channel interference may not be necessary. In one embodiment, an objective is to minimize a difference between total co-channel interference in the region resulting from a channel being assigned to base stations of different providers with a certain penalty (Eq. 7) and total co-channel interference resulting from a channel being assigned to a base station for a single provider (Eq. 8). In one such embodiment, since terms x vf  x wf  are quadratic, a variable y vwf  may be defined such that y vwf =1 if x vf =x wf =1 and y vwf =0 otherwise. Using this variable, a function for minimizing the difference may be defined as follows (Eq. 10 subject to conditions 10.1-10.3): 
     
       
         
           
             
               
                 
                   min 
                   ( 
                   
                     
                       ( 
                       
                         
                           ∑ 
                           
                             
                               ( 
                               
                                 v 
                                 , 
                                 w 
                               
                               ) 
                             
                             ∈ 
                             E 
                           
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             ∑ 
                             
                               
                                 f 
                                 ∈ 
                                 
                                   F 
                                   ⁡ 
                                   
                                     ( 
                                     v 
                                     ) 
                                   
                                 
                               
                               , 
                               
                                 g 
                                 ∈ 
                                 
                                   F 
                                   ⁡ 
                                   
                                     ( 
                                     w 
                                     ) 
                                   
                                 
                               
                               , 
                               
                                 
                                    
                                   
                                     f 
                                     - 
                                     g 
                                   
                                    
                                 
                                 ∈ 
                                 
                                   T 
                                   vw 
                                 
                               
                             
                           
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           
                             
                               p 
                               vwfg 
                             
                             ⁢ 
                             
                               z 
                               vwfg 
                             
                           
                         
                       
                       ) 
                     
                     - 
                     
                       ( 
                       
                         
                           ∑ 
                           
                             v 
                             ∈ 
                             V 
                           
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             ∑ 
                             
                               w 
                               ∈ 
                               
                                 scope 
                                 ⁡ 
                                 
                                   ( 
                                   v 
                                   ) 
                                 
                               
                             
                           
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           
                             
                               p 
                               vwf 
                             
                             ⁢ 
                             
                               y 
                               vwf 
                             
                           
                         
                       
                       ) 
                     
                   
                   ) 
                 
               
               
                 
                   Eq 
                   . 
                   
                       
                   
                   ⁢ 
                   10 
                 
               
             
             
               
                 
                   
                     
                       ∑ 
                       
                         f 
                         ∈ 
                         
                           F 
                           ⁡ 
                           
                             ( 
                             v 
                             ) 
                           
                         
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       x 
                       vf 
                     
                   
                   = 
                   
                     
                       d 
                       ⁡ 
                       
                         ( 
                         v 
                         ) 
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ∀ 
                       
                         v 
                         ∈ 
                         V 
                       
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   10.1 
                 
               
             
             
               
                 
                   
                     
                       
                         x 
                         vf 
                       
                       + 
                       
                         x 
                         wf 
                       
                     
                     ≤ 
                     
                       1 
                       + 
                       
                         y 
                         vwf 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   for 
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       v 
                       ∈ 
                       V 
                     
                     , 
                     
                       w 
                       ∈ 
                       
                         scope 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                     , 
                     
                       f 
                       ∈ 
                       
                         F 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                     , 
                     
                       F 
                       ⁡ 
                       
                         ( 
                         w 
                         ) 
                       
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   10.2 
                 
               
             
             
               
                 
                   
                     
                       
                         x 
                         vf 
                       
                       + 
                       
                         x 
                         wg 
                       
                     
                     ≤ 
                     
                       1 
                       + 
                       
                         z 
                         vwfg 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       
                         for 
                         ⁢ 
                         
                           
 
                         
                         ( 
                         
                           v 
                           , 
                           w 
                         
                         ) 
                       
                       ∈ 
                       E 
                     
                     , 
                     
                       f 
                       ∈ 
                       
                         F 
                         ⁡ 
                         
                           ( 
                           v 
                           ) 
                         
                       
                     
                     , 
                     
                       g 
                       ∈ 
                       
                         F 
                         ⁡ 
                         
                           ( 
                           w 
                           ) 
                         
                       
                     
                     , 
                     
                       
 
                     
                     ⁢ 
                     
                       
                          
                         
                           f 
                           - 
                           g 
                         
                          
                       
                       ∈ 
                       
                         T 
                         
                           vw 
                           ⁢ 
                           
                               
                           
                         
                       
                     
                   
                 
               
               
                 
                   Con 
                   . 
                   
                       
                   
                   ⁢ 
                   10.3 
                 
               
             
           
         
       
     
     In this MINPEN-DSA formulation, an objective is to minimize a difference between total co-channel interference in the region resulting from a channel being assigned to base stations of different providers with a certain penalty (Eq. 7) and total co-channel interference resulting from a channel being assigned to a base station for a single provider (Eq. 9). This MINP-DSA formulation is subject to specific conditions representative of various constraints associated with spectrum allocation (denoted as spectrum allocation constraints). The first condition (Con. 10.1) indicates that per-vertex demand is specified as a single spectrum demand quantity instead of a range of spectrum demand quantities. The second condition (Con. 10.2) represents the scope constraint. The third condition (Con. 10.3) represents penalties associated with forbidden distances and co-channel allocations. 
       FIG. 6  depicts a method according to one embodiment of the present invention. Specifically, method  600  of  FIG. 6  includes a method for determining spectrum allocations by processing a conflict graph using a spectrum allocation algorithm. As depicted in  FIG. 6 , one of a plurality of spectrum allocation algorithms (e.g., MAXSERV, MINPEN, and the like) may be used for processing a spectrum demands using a conflict graph. Although depicted as being performed serially, those skilled in the art will appreciate that at least a portion of the steps of method  600  may be performed contemporaneously, or in a different order than presented in  FIG. 6 . The method  600  begins at step  602  and proceeds to step  604 . 
     At step  604 , a base station pair is selected. In one embodiment, the base station pair is selected using a conflict graph. At step  606 , base station information associated with the base station pair is determined. In one embodiment, base station information includes base station location information, base station characteristic information, and the like, as well as various combinations thereof. At step  608 , a co-cell separation constraint is determined for the base station pair. At step  610 , a co-located cross-provider conflict constraint is determined for the base station pair. At step  612 , a remote cross-provider conflict constraint is determined for the base station pair. At step  614 , a soft-handoff constraint is determined for the base station pair. 
     At step  616 , a determination is made as to whether the final base station pair (e.g., from the conflict graph) has been processed. If the final base station pair has not been processed, method  600  proceeds to step  618 . At step  618 , a next base station pair is selected (e.g., from the conflict graph). From step  618 , method  600  returns to step  606 , at which point base station information associated with the selected base station pair is determined. If the final base station pair has been processed, method  600  proceeds to step  620 . Although depicted and described as being processed on a base-station-pair-by-base-station-pair basis, a conflict graph may be processed using various processing approaches. 
     At step  620 , a spectrum allocation algorithm is selected. If a spectrum allocation service maximization algorithm (denoted MAXSERV) is selected, method  600  proceeds to step  622 . If a spectrum allocation penalty minimization algorithm (denoted MINPEN) is selected, method  600  proceeds to step  624 . Although depicted and described as being selected dynamically, in one embodiment, in which only one spectrum allocation algorithm is utilized, step  620  may be omitted. In this embodiment, either step  622  is performed (step  624  is omitted) or step  624  is performed (step  622  is omitted). 
     At step  622 , a spectrum allocation service maximization algorithm is applied for maximizing spectrum allocations satisfying spectrum demands subject to the various constraints determined from the conflict graph. At step  624 , a spectrum allocation penalty minimization algorithm is applied for minimizing penalties of spectrum allocations subject to the various constraints determined from the conflict graph. From steps  622  and  624 , method  600  proceeds to step  626 . At step  626 , spectrum allocations determined using the spectrum allocation algorithm are communicated to the network. At step  628 , method  600  ends. 
     In one embodiment, spectrum allocations determined using one of the DSA formulations (e.g., F-DSA, MAXSERV-DSA, MINPEN-DSA, and the like) may comprise potential spectrum allocation solutions. In one embodiment, an efficient exact solution or heuristic-based method may be used to determine the possible spectrum allocation solution. If n(v) at all nodes satisfies d min ≦n(v)≦d max , the possible spectrum allocation solution satisfies the demands of each of the RIPs. If n(v) at all nodes does not satisfy d min ≦n(v)≦d max , the largest demands d max  for nodes with small favorability ratings may be set to d min +1 values, respectively, and an attempt may be made to obtain a spectrum allocation in which d min ≦n(v) is satisfied for all nodes v. In other words, spectrum allocation of method  600  attempts to aggressively satisfy the minimum demands d min  first, and then attempts to satisfy as much of the maximum demands d max  as possible. 
     Although primarily described herein with respect to spectrum allocation within a region, in one embodiment, spectrum allocation may be coordinated across multiple regions. If non-overlapping portions of a dynamically sharable spectrum are used in adjacent regions, spectrum within each region can be management independently from spectrum in adjacent regions; however, if the same spectrum band is accessed in multiple regions, spectrum allocation within adjacent regions requires coordination of spectrum allocation between the adjacent regions. The regional approach to spectrum management, as depicted and described with respect to  FIG. 1 - FIG. 6 , results in a configuration in which spectrum allocation within each region is performed independent of other regions. A cross-regional approach to spectrum management results in a configuration, as depicted and described with respect to  FIG. 7 , in which spectrum allocation is coordinated across regions. 
       FIG. 7  depicts a high-level block diagram of a communication network including a plurality of regions managed by a respective plurality of networked spectrum broker systems. Specifically, communication network  700  of  FIG. 7  includes a spectrum broker overlay network (SBON)  702  supporting communications between a plurality of spectrum broker systems  704   1 - 704   4  (collectively, SBSs  704 ). As depicted in  FIG. 7 , SBSs  704  manage spectrum allocation within a respective plurality of regions  706   1 - 706   4  (collectively, regions  706 ). As depicted in  FIG. 7 , SBSs  704  communicate with regions  706   1 - 706   4  using a respective plurality of communication links (CLs)  705   1 - 705   4  (collectively, CLs  705 ). For purposes of clarity, BSs, NMSs, communication networks, and the like associated with regions  706  are not depicted. 
     As depicted in  FIG. 7 , possibility of spectrum conflict in the areas of region adjacency may be eliminated through communication between SBSs  704  using associated SBON  702 . For example, consider an area of region adjacency between regions  706   1  and  706   2  (illustratively, the rightmost portion of region  706   1  and leftmost portion of region  706   2 ). In this example, without communication between SBSs  704   1  and  704   2 , spectrum allocations provided to a base station located in the right-most portion of region  706   1  may conflict with spectrum allocations provided to a base station located in the left-most portion of region  706   2 . As depicted in  FIG. 7 , communication between SBSs  704   1  and  704   2  using SBON  702  enables processing for preventing spectrum allocations resulting in spectrum conflicts between base stations located in the area of region adjacency between regions  706   1  and  706   2 . 
     In one embodiment, the available spectrum band may be divided into a plurality of sub-bands, and sub-bands may be a-priori restricted as blocked frequencies in areas of region adjacency. In this embodiment, although a large portion of the available spectrum (e.g., ⅞ th ) is inaccessible in the adjacency regions, each spectrum broker system may a-priori determine and publish any unused sub-bands to peer spectrum broker systems of adjacent regions for use in adjacency regions. In a configuration having a simple grid subdivision of a large area, and a single spectrum broker system per region, a maximum of two sub-bands per square region may be used. 
     In one embodiment, the available spectrum band may be divided into a plurality of sub-bands, however, the sub-bands are not a-priori restricted as blocked frequencies in areas of region adjacency, rather, each spectrum broker system broadcasts actual spectrum allocations (i.e., a list of forbidden channels) to the spectrum broker systems of adjacent regions during each spectrum allocation cycle. The adjacent spectrum broker systems account for the actual spectrum allocations during spectrum allocation for the associated adjacent regions. This configuration requires time synchronization of the spectrum broker systems. 
     In one embodiment, since spectrum conflict is possible if base stations of two or more providers exist in the areas of region adjacency, a requirement may be established that only one provider provide coverage in the areas of region adjacency. In such a configuration, reallocation of the same frequency across the region boundaries results in soft-handoff, whereas allocating different frequencies results in hard-handoff. In either case, no spectrum is lost for dynamic access. In one embodiment, if multiple providers operate in areas of region adjacency, only one of the multiple providers is allowed to activate spectrum in the conflict region at a given time, thereby improving access efficiency for different providers. 
       FIG. 8  depicts a high-level block diagram of a general purpose computer suitable for use in performing the functions described herein. As depicted in  FIG. 8 , system  800  comprises a processor element  802  (e.g., a CPU), a memory  804 , e.g., random access memory (RAM) and/or read only memory (ROM), a spectrum allocation module  805 , and various input/output devices  606  (e.g., storage devices, including but not limited to, a tape drive, a floppy drive, a hard disk drive or a compact disk drive, a receiver, a transmitter, a speaker, a display, an output port, and a user input device (such as a keyboard, a keypad, a mouse, and the like)). 
     It should be noted that the present invention may be implemented in software and/or in a combination of software and hardware, e.g., using application specific integrated circuits (ASIC), a general purpose computer or any other hardware equivalents. In one embodiment, the present spectrum allocation module or process  805  can be loaded into memory  804  and executed by processor  802  to implement the functions discussed herein. As such, spectrum allocation process  805  (including associated data structures) of the present invention can be stored on a computer readable medium or carrier, e.g., RAM memory, magnetic or optical drive or diskette and the like. 
     Although various embodiments which incorporate the teachings of the present invention have been shown and described in detail herein, those skilled in the art can readily devise many other varied embodiments that still incorporate these teachings.