System and method for heterogenous spectrum sharing between commercial cellular operators and legacy incumbent users in wireless networks

Described herein are systems and methods for telecommunications spectrum sharing between multiple heterogeneous users, which leverage a hybrid approach that includes both distributed spectrum sharing, spectrum-sensing, and use of geo-reference databases.

FIELD

This disclosure relates to a system and method for wireless communication spectrum sharing.

BACKGROUND

The proliferation of smartphones and other mobile devices has placed heavy data traffic demands on cellular networks, with cellular network operators facing difficult challenges in meeting this demand given their existing spectrum allocations. Recent policy shifts at the federal levels and Department of Defense indicate that sharing existing federal spectrum with commercial users may be a viable option for meaningful increase of spectrum for Long Term Evolution (LTE) fourth generation (4G) cellular technologies. Making more spectrum available will certainly provide opportunities for mobile broadband capacity gains, but only if those resources can be efficiently accessed such that secondary users can proactively share the same bands as the primary users (e.g., federal incumbent users). Effectively grafting pre-emptible spectrum into a cellular network is challenging. Past approaches to coexistence with primary users centered on spectrum sensing-based dynamic spectrum access (DSA) techniques and database driven DSA techniques.

Spectrum sensing DSA approaches entail the use of sensing devices to scan a frequency band of interest to identify unused spectrum where secondary access is possible without impacting primary user operations. The main approaches can be categorized as internal (co-located) sensing, external sensing, use of beacons, and database driven techniques, and have included algorithms for matched filtering, energy detection, cyclostationarity, radio identification based sensing, waveform based sensing, etc. So-called cooperative spectrum sensing increases sensing accuracy by fusing data from multiple nodes and thus takes advantage of spatial diversity. General types of cooperative spectrum sensing include centralized, distributed, external, or device centric (local) sensing.

Database driven DSA is a sub-class of the sensing based DSA approach. The database driven approach is classified into two general categories: geo-location based and interference based. Both are similar in principle that they provide a database to the secondary users (cellular network operators) which helps them transmit in licensed bands while ensuring they do not interfere with primary users in the given band. These databases can be stored at eNodeBs or at the network level and have different levels of granularity. In the past, these databases only provided coarse resolution of historical spectrum use by primary users. A type of database called a radio environment map (REM), which can contain interference information, have been utilized to help in deploying secondary networks. However, each of these approaches has shortcomings, especially when mobile users, hidden nodes, and interaction with the incumbent or primary users are considered.

Therefore a need exists for an improved system and method for temporal and geographical spectrum sharing between a commercial cellular operator and a government incumbent operator.

SUMMARY OF THE INVENTION

Described herein are systems and methods for spectrum sharing between multiple heterogeneous users, which leverage a hybrid approach that includes both distributed spectrum sensing and use of geo-reference databases. A hybrid approach can be thought of as a combination of dynamic spectrum sensing with use of a radio environment map (REM) with local sensing information. This approach allows opportunistic access to government spectrum bands in a controlled manner, utilizing both DSA sensing and database interaction, to maximize fallow spectrum while simultaneously minimizing the potential for interference to incumbent users.

As a general overview, commercial cellular operators seeking additional temporary frequency allocations perform an analysis incorporating spectrum sensing to identify potential primary users. This sensing can occur at the base station, at the end user, or at a new network component in communication with at least one base station. Cellular operators send a request query to a federal database server connected to databases having information on temporal and geographical spectrum assignments and any potential interference. Incumbent federal spectrum users typically include government entities. After an interference analysis, the federal database server assigns temporary spectrum allocations that are unique in time and geographic location. Allocation of the assigned spectrum bands is performed using a two-tier approach to allocate resources to users. A first tier allocation process allocates resources to cell zones, and a second tier process allocates resources to users in their respective cell zones.

Efficient space-time spectrum utilization can thus be achieved between a primary user (denoted by PU, i.e., the government incumbent operator) and a secondary user (denoted by SU, i.e., the cellular operator) while maintaining the interference experienced by the primary user below a particular threshold. Further, a low-latency protocol for interaction between a spectrum server and a federal database server ensures that the spectrum server is notified in almost real time, if required to preempt a prior spectrum allocation. Parameters for a spectrum lease request are determined with an objective of maximizing the network utility derived from the requested spectrum. The spectrum server honors a set of transmission restrictions associated with the allocated primary spectrum by incorporating the restrictions into the spectrum allocation algorithms at the spectrum server and at an eNodeB level. The two-tier approach for resource allocation among users is utilized which results in a slightly suboptimal but more tractable solution compared to a joint resource allocation scheme. A first tier allocation process allocates resources to cell zones, and a second tier process allocates resources to users in their respective cell zones.

The resource allocation process at the spectrum server attempts to maintain an acceptable QoS (quality of service) for users while still satisfying the capacity demand of the users within the cells of the cellular network. To this end, a fractional frequency reuse (FFR) scheme is proposed in which each cell is divided into zones: a center cell zone and three cell-edge sector zones. This process takes as feedback a novel metric called the average demand factor from each eNodeB under the spectrum server's control. This metric models the rate requirement of capacity deficient users at cell-edge zones and thus ensures fairness in allocation of resources to each of the four zones. The calculation of the fractional utility metric is unique in a way that makes it easy to incorporate federal restrictions and local sensing decision variables.

The eNodeB level resource allocation method allocates resources to individual users within each zone. The eNodeB calculates instantaneous demand factors of each user within the cell and stores these values. The demand factors are averaged over time and users and fed back to the spectrum server whenever needed for operation of the resource allocation process. This leads to appreciable reduction in communication overhead. The fairness among the users is introduced using a weighted linear utility function. User allocation is achieved by solving a linear integer programming problem instead of a typical non-linear integer programming problem, thereby significantly reducing complexity while settling for a slightly suboptimal (in terms of fairness) solution. The time-interval between two executions of the eNodeB level allocation process is flexible and thus allows the process to run at different time scales, as desired.

DETAILED DESCRIPTION

As an overview, the systems and methods described herein present an approach for enabling spectrum sharing between a government user and a commercial cellular operator. The main components of the system include the commercial radio access network, the commercial packet core, a spectrum server having interaction with a federal database server controlled by a government entity, and sensors that provide real-time spectrum sensing and interference monitoring. In general, a commercial network is able to make requests for additional spectrum resources to support the demands of end users. A spectrum server will make requests to a federal database server that has knowledge of the incumbent users provided from a master government file and knowledge of interference with respect to the incumbent users. In conjunction with the database knowledge, information regarding real-time spectrum sensing performed locally at commercial base stations is used to award spectrum resource allocation to the commercial base stations. This system and method assume that the commercial end users, such as handsets, and base stations are capable of operating on the available government spectrum. The awarded government spectrum resource is provided to the commercial cellular operator for use by the base stations to supplement their existing spectrum. These awards may include temporal, geographical, and operational constraints such as maximum power transmission. It is envisioned that this additional spectrum can provide additional resources for low priority data. In the event that the incumbent government user requires the awarded spectrum, the commercial cellular operator will be required to vacate the awarded allocation. The novel contributions of this system include the method for requesting resources, determining how much and which band to request, and the process for allocating awarded resources to base stations and end users.

Referring toFIG. 1, an exemplary system100for spectrum allocation is shown in which a commercial cellular operator has deployed an LTE architecture. System100includes a radio access network (RAN)102, represented here by LTE E-UTRAN, which is comprised of individual enodeB's104a-c, interconnected through the X2 interface106. The RAN102connects to an evolved packet core represented by a mobile management entity (MME)110. The connection is enabled through the S1 interface108. In this exemplary system, end user elements, such as handsets, which are either mobile or static, interact with the RAN102. Data schedulers, residing in the enodeB's, are used to coordinate transmissions of voice or packet data to the end user elements, denoted as secondary users. Sensors122perform spectrum sensing at the base stations to determine the possible presence of federal incumbent users, whose presence may not be known with certainty in advance due to their mobility, and/or failure to update a federal database.

A proposed spectrum server114interfaces with the RAN through the Operations and Maintenance (OAM) interface109. The spectrum server114connects via secure connection116with a federal portion of the system100, which includes a federal database server118, database112, and database120. From a functional perspective, the spectrum server114aggregates the needs of the base stations, makes requests to the federal database server118, and manages any awarded spectrum back to the individual base station. Federal interference sensors123provide interference sensing on the federal side, such as to detect any interference with federal systems.

More specifically, federal database server118interacts with databases112and120. Database120is a real-time interference reporting database. The federal database server118performs an interference analysis prior to a spectrum allocation, using information from this database. This database also provides information important for revocation of spectrum. For example, if a government radar system starts to sense that it is being interfered with, and it is due to a previous spectrum allocation to a commercial operator, an interference analysis will drive the federal database server to revoke or constrain that previous allocation. The federal database server does not provide information about its operations, rather it will only approve, or disapprove requests and may add constraints to any approved allocation.

Database112includes one or more enhanced Government Master Files, which includes information on temporal and geographical spectrum dynamics. Currently, a primitive Government Master File (GMF) exists which is basically a list of spectrum allocations and their locations. An enhanced version of such a file can also include information of when various users are operating, including such factors as duty cycle, power of the system, antenna beam pattern, and so on. The federal database server can also incorporate this information into allocation decisions. For example, if a spectrum band is typically devoted to the Army but the Army only uses it for special training exercises at certain times, the federal database server can allocate the band for specific other times. Similarly, if a satellite system only transmitted data once every hour for two minutes, then there is a significant period of idle time for that band that could be allocated to a commercial operator.

Regarding operation of this system, novel contributions include the hybrid interaction between spectrum sensing and database driven DSA approaches. Leveraging both techniques yields the ability to characterize the position, directionality, power, and modulation of relevant emitters in a localized region. An additional novel contribution includes the use of radio environment mapping by the spectrum server that integrates current sensing data from base station sensors122and historical spectrum sensing data from eGMF112. Further, incorporating real-time interference knowledge enables the use of initial conservative interference assumptions when allocating government spectrum. These interference assumptions can gradually grow less strict until the spectrum sensing identifies interference. This approach enables online tenability of propagation models and truer interference thresholds, which are difficult to capture in analytical models. This approach is a novel contribution over existing state-of-the-art designs implementing static approaches. The implications include enabling access to significant amount of additional spectrum for commercial use.

Referring to the system inFIG. 2, a spectrum sharing process of system100starts with the network, represented here by the spectrum server114, which can be located in the enhanced packet core (EPC), issuing a lease request204for spectrum allocation to the federal database server118(an exemplary request process is further explained with respect toFIG. 4). The federal database server118makes a decision regarding what, if any, spectrum resources are available by performing an analysis206, including an interference analysis. Any spectrum availability will be further specified with restrictions on time length, location, power, and most importantly, whether or not the commercial user will have unrestricted access or if the commercial user must also perform spectrum sensing and leave the band if it detects a primary user. The federal database server takes into account several different types of information in order to make any awards to the spectrum server114. As mentioned, assessment of any interference to federal incumbent users is one element that affects the decision process for awarding resources to the spectrum server or for changing existing allocation. For example, if a previous request for spectrum was approved by the federal database server, and an updated interference analysis shows that a federal user is being impinged upon, the federal database server may revoke or further restrict an allocation. The overall decision process by the federal database server incorporates broad parameters in the decision such as the need for operational security, fairness in allocating resources to secondary users, and determining dynamic interference mitigation procedures. Upon completion of the analysis, the federal database server returns initial allocations208back to the spectrum server. This allocation may incorporate several constraints, including detection thresholds that define criteria for vacating a band in a specific sub-region.

The spectrum server114can incorporate spectrum-sensing input210from sensors122at the base stations in the RAN102, both in the lease request and as part of a resource optimization process202. The sensing from the RAN is conveyed at212to the spectrum server114for assimilation into a radio environment map (REM). A REM can be as simple as a table of location, time, and energy detected. It can be used to track historical and geo-located spectrum data. For example, every day at 5 PM at a major office park, cell phone usage peaks as people leave from work to head home. However, at 6 PM, there may be little cell phone usage. As another example, near a construction site there may be interference from high-powered welding during work hours, but little interference during non-work hours. This REM can provide more data for an artificial intelligence decision making algorithm to make more informed decisions. The REM can include items such as location, time, day, strength of signal, whether users are mobile and when, travel directions, and so on. This information can be combined with geographic data such as terrain, tree cover, season of the year (summer=high foliage, autumn/winter=no leaves), elevation, potential tall buildings, and many other possible relevant items that can affect radio signals.

In some embodiments, spectrum sensing is performed using a separate energy detector located at each base station. In other embodiments, spectrum-sensing data is obtained from individual handsets using the Minimize Drive Test (MDT) capability in release 10 of the LTE standards. The MDT capability enables functionality for enodeB's to poll end user devices for received signal strength data. The returned signal strength can be used by the spectrum server to populate REM profiles.

The REM and the spectrum allocation information208are used in a resource optimization process202. The results of the optimization take the form of resource allocation216that is sent back to individual base stations in the RAN102. The network continually performs the sensing/resource allocation process, which refines the distribution within the network. The federal database server118can provide allocation updates at block222, including revoking the original spectrum allocation under a variety of circumstances. These can include expiration of the original allocation, reports of harmful interference or a priority request from other federal users that have pressing needs, analyzed at block220. In all cases, the spectrum server114can make a new allocation request.

In more detail, still referring toFIG. 2, this process flow incorporates the spectrum server114as an intermediate interface between commercial users and disparate government incumbent users. The present method and system enables the interaction between heterogeneous networks encompassed by systems that do not share common packet cores and only use similar spectrum allocations in geographic proximity. Past approaches relied on geographic exclusion zones, limited operational cycles, or highly restrictive frequency allocations. In particular, the interaction between the PU (controlled by the federal database server) and the SU (eNodeB controlled by the spectrum server) to achieve efficient space-time spectrum utilization maintaining the interference experienced by the primary user below a particular threshold is unique to this area. Further novelty involves the use of a unique combination of several network parameters to prepare the spectrum lease. These parameters include available PU bands, average traffic volume, and quality of service (QoS) requirements of active users. These parameters are factored in while preparing the spectrum lease request. This scheme is quite flexible in the sense that it takes into account different possible spectrum lease formats (specified by the federal database sever) while preparing the spectrum lease request. The parameters for the spectrum lease request are determined with an objective of maximizing the network utility derived from the requested spectrum. The spectrum server honors the transmission restrictions associated with the allocated primary spectrum by incorporating this information into the spectrum allocation algorithms at the spectrum server and eNodeB level.

The present method and system provided herein provides a coordination mechanism between heterogeneous systems, which permits spectrum coexistence in real-time without highly restrictive frequency allocations. In particular, the interaction between a PU (Primary User—controlled by federal database server) and the SU (Secondary User—via eNodeB's controlled by spectrum server) to achieve efficient space-time spectrum utilization while maintaining the interference experienced by the primary user below a particular threshold is a new feature. Further novelty involves the use of a unique combination of several network parameters to prepare the spectrum lease. These parameters include available PU bands, average traffic volume, and quality of service (QoS) requirements of active users. These parameters are factored in while preparing the spectrum lease request. This scheme is quite flexible in the sense that it takes into account different possible spectrum lease formats (specified by the federal database server) while preparing the spectrum lease request. The parameters for the spectrum lease request are determined with an objective of maximizing the network utility derived from the requested spectrum. The spectrum server honors the transmission restrictions associated with the allocated primary spectrum by incorporating this information into the spectrum allocation algorithms at the spectrum server and eNodeB level.

Referring toFIG. 3, an exemplary signaling exchange implemented for initialization, registration, allocation request, and validation between the spectrum server114and federal database server118is shown. Database discovery starts with location of a uniform resource identifier that uniquely identifies a specific database server. The initialization process300consists of a pair of messages including an initial service request302and initial response304. This exchange enables the spectrum server to information such as capabilities, regulatory domain, and the desired sequence of protocol operations. The spectrum server114will also obtain authentication parameters from the federal database server118that will enable the spectrum server to prove its authenticity and provide message integrity during the entire protocol operation. After the initialization process, the spectrum server and federal database server exchange another pair of messages as part of the registration process310. The registration exchange establishes operational parameters as required by the spectrum management authority, such as the Federal Communications Commission. Parameters can include owner and/or operator contact information, location and antenna height parameters. This registration process is required upon initial contact or when operational parameters change. The registration message pair consist of a registration request312and a registration response314. The next step after the registration and mutual authentication is a query message process320. The spectrum server sends an available channel query322that includes required parameters, such as geo-location. The federal database server returns an available channel response324that includes an array of available channels (spectrum bands) within the scope of the request and regulatory authority. Information in the array includes the frequency range, availability rating, operating power, and event management. The spectrum server114must then inform the federal database server118via a use channel notify communication326to indicate channels it intends on using at specific enodeB's in the network. The federal database server acknowledges with a use channel response328. Finally, the enodeB's under control of the spectrum server require validation332by the federal database server. By FCC rules, a spectrum server can allocate secondary spectrum after enodeB's are registered in the database. Therefore the federal database server supports the validation by responding with an acknowledgement334.

Referring toFIG. 4, an exemplary process is illustrated for estimating the bandwidth demand of an LTE network and determining the available primary spectrum to obtain with a spectrum lease from the federal database server. This process occurs in the LTE network after the spectrum server114gets the registration response314from the federal database server. and before it sends the channel query322. In general, this requires a determination of the activity of both primary users and secondary users.

At a step401, each eNodeB, during a sensing interval, independently senses the activity of primary users (PU) in all known PU bands. Then at step402, each eNodeB sends soft or hard decisions (as required) regarding the activity to the spectrum server114. Then at step403, for each PU band, the spectrum server optimally combines the sensing decisions from all the eNodeBs and prepares a set of available bands, denoted by A.

At a step404, each secondary user reports its uplink capacity requirements and channel-quality-indicator (CQI) metric to its serving eNodeB which then maps it to the user's bandwidth demand. At a step405, each eNodeB computes the bandwidth required to serve all active users in addition to the currently available spectrum. At step406, each eNodeB reports its bandwidth requirement to the spectrum server, which then computes the additional spectrum to be requested (Wlease) from the federal database406. The spectrum server also computes the time duration (Tlease) for which this additional spectrum needs to be requested based on the requirements of eNodeBs. At a step407, it is determined whether a structured or unstructured lease request is required by the federal database server. In the case of a structured lease request, at a step408, the spectrum server additionally determines a minimum number of available PU bands R (R⊂A) that if granted access to, would completely meet the overall bandwidth requirements. It then sends the spectrum lease request (R; Wlease; Tlease) to the federal database server at a step409. In the case of unstructured lease request, the spectrum server sends (Wlease; Tlease) to the federal database at a step410. In either case, the procedure then terminates at412.FIG. 4thus illustrates an exemplary method for estimating bandwidth demand of an LTE network and identifying a needed request for resources.

FIG. 5is a flow diagram of an exemplary process for resource allocation of frequency bands provided by the federal database server, to the cell center zone or cell-edge zones (sectors) of the eNodeBs. The spectrum server performs this allocation, which is termed a Level1algorithm. This process repeats every TeNBtransmission time interval (TTI). A step501is an initialization step, which requires input from the eNodeB at step502, namely, the average achievable rates for each end user (UE) UE-base station pair, the minimum per-UE capacity requirements, the demand factors of the UEs, and the local sensing decisions for each cell-center zone and cell-edge sector zone. Initialization also requires input from the federal database server at step503, namely, the spectrum bands to be used for allocation and the restrictions on the transmissions within each cell center zone or sector, which are modeled as binary federal decision variables. Further details regarding steps501,502and503are provided with respect toFIG. 8, described below.

In general, during initialization step501, all the sub-bands are allocated to a set Cinit. For each sub-band UE pair, a utility function for assigning band n to a UE within the cell center zone or to a cell-edge zone (sector) is calculated. The fractional utility gain gives the utility of assigning the n-th band to the cell-edge zones rather than the center. The capacity requirement for each of the cell-edge zones and the cell-center zone are calculated. The sub-bands available for allocation are initialized to a set Z. Two variables modeling increase in allocated capacity of cell-center and cell-edge zones are declared and initialized to zero. The algorithm iterates over each sub-band. At each iteration, a sub-band is allocated to the cell-center zone or a cell-edge zone.

Still referring toFIG. 5, at step504, the algorithm begins for the n=1-st band. At step504, the sectors1with capacity deficiency, (i.e., when the difference between required capacity and the capacity increment variable is greater than zero) are identified for the n-th sub-band, and processing proceeds to a step505. At step505, a determination is made whether there are any sectors with capacity. If so, processing proceeds to step506. If not, processing proceeds to step508. At step506the sector-band pair (1_, n) that gives the maximum fractional utility for assignment of the sub-band is identified and processing proceeds to step507. At step507, it is checked whether maximum fractional utility is negative and the center zone is capacity deficient. If yes, the processing proceeds to step508. If no, processing proceeds to step509. At step508, the sub-band n is assigned to the cell-center zone, the variable modeling capacity increment of the cell-center zone is updated to reflect the assigned capacity of the n-th sub-band and the processing proceeds to step510. At step509, the sub-band n is assigned to sector1*, the capacity increment variable for sector1* is updated to reflect the assigned capacity of the n-th sub-band and the processing proceeds to step510. At step510, the assigned sub-band is removed from the set Z of sub-bands available for allocation and processing proceeds to step511. At step511, a determination is made whether the set Z is empty or if the capacity requirement of center and sector zones is satisfied. If yes, then the process ends at512. If not, then the process loops back to step504and reiterates for n=n+1. The output of the algorithm is the allocation of sub-bands in a cell-center zone or one of each of the three cell-edge zones. This information is then input to the Level2algorithm as described with respect toFIG. 6.

FIG. 5thus describes an exemplary process for allocating resources between the cell center and cell edge users. The spectrum server manages functions that include: determining resource needs of multiple users, making requests to a federal database server, and allocating awarded resources back to users. The resource allocation algorithm at the spectrum server tries to maintain an acceptable QoS for users while still satisfying the capacity demand of the users within the cells. The algorithm divides each cell into a center zone and three sector zones. The algorithm takes as feedback a novel metric called the average demand factor from each eNodeB under the spectrum server's control. The eNodeBs compute this metric for each center and sector zone over multiple users (averaged over multiple transmission time intervals (TTIs)). This metric models the rate requirement of capacity deficient users at cell-edge zones and thus ensures fairness in allocation of resources to each of the four zones. The calculation of the fractional utility metric is also unique in a way that it makes it easy to incorporate the federal and local sensing decision variables.

FIG. 6illustrates an exemplary process for allocation of frequency bands to users within a single cell zone, which runs at each eNodeB, and incorporates a so-called level2algorithm. This process is initialized at a step601, includes as input from block602the allocations of bands to cell center zones and sectors (which are determined from the level1algorithm), and also includes as input the federal resource allocation decision variables from block603(illustrated in block802ofFIG. 8).

The initialization stage fuses the local sensing decisions and federal decision variables to allocate resources to select users within permitted transmission zones. The algorithm begins at block604. The algorithm has two modes—a proportional fairness mode and a linear utility maximization mode. At a step605, a decision is made whether the proportional fairness model is to be used. If proportional fairness is used, processing proceeds to a step606, if not, processing proceeds to a step607. At step606, a linear utility function, utilizing demand factors to enforce fairness, is calculated for each user within the permitted transmission zones of the cell, and processing then proceeds to a step608. At step607, a linear utility function, similar to block606but without the demand factor, is calculated for each user within the permitted transmission zones of the cell, and processing proceeds to step608. At step608, an integer program is solved for linear utility maximization to find the optimal resource allocation in terms of the Boolean variable. From step608, processing proceeds to step609. At step609, the algorithm checks if the allocation of bands from the spectrum server is unchanged. If yes, then processing ends in block610. If no, processing proceeds to step604and repeats with the new allocation. The Level2algorithm runs at every TTI while the Level1spectrum server algorithm runs for a time period constituting multiple transmission intervals. The spectrum server monitors the utility of frequency allocation at each eNodeB and runs the Level1algorithm each time the utility decreases, or until the federal lease expires.

Referring to theFIG. 7, more detail is provided for an exemplary process for a spectrum allocation request between the spectrum server114and the federal database server118such as is more generally described inFIG. 4. This exemplary process is used by the spectrum server to determine the minimal primary user (PU) bands to be requested of the federal spectrum database server. This process starts off with information about available bands, A, input at step701and the average bandwidth required, Wlease, input at step702. Then at a step703, the set of the bands to be requested, R, is initialized as A. Utilizing the knowledge of the duty cycle of PU, the process determines the expected bandwidthWkfor each band in R at a step704. Then at step705, Wdiff, the difference between the total expected bandwidth (if R is requested) and the bandwidth required is calculated. Letting σWrepresent the standard deviation in the required bandwidth in a particular time interval, at step706a check is made whether Wdiff>σW. If no, processing proceeds to step708, and a lease request (R; Wlease; Tlease) is sent to the federal database server, where Tleaseis the lease duration, determined at step709. If true, processing proceeds to step707. At step707, Gk=Wdiff−Wkis computed for each band kin R, and processing then proceeds to a step710. At step710, negative values of Gk are set to −∞ (negative infinity). Then at step711, a band is selected which if removed minimizes Wdiff, and processing proceeds back to step705. In this manner, steps are repeated to successively remove the PU bands until Wdiff<σWis achieved and a lease request can be sent at step708.

FIG. 8illustrates an exemplary process for the initialization phase, or step501,502, and503of the Level1spectrum server algorithm illustrated inFIG. 5. In particular, at a step801(corresponding to step502ofFIG. 5), the spectrum server receives inputs from the eNodeBs within its network. These inputs include: average achievable rate for each physical resource block (PRB) for each cell-center or cell-edge zone over TeNBTTIs; the average demand factors; the average capacity requirements; and the local sensing decisions (as set forth in the REM).FIG. 9provides more detail for these calculations.

At a step802(corresponding to step503ofFIG. 5), the spectrum server takes as input the set of PRBs which can be allocated to UEs and the federal decision variables. The initialization step501ofFIG. 5is detailed in803. All PRBs are initialized to a set Cinit. The utility for assigning the nthPRB to the cell-center zone, Wncand the utility for assigning it to the cell-sector zone1, Wnc(e),lare calculated. These utilities are average rate based utility functions that incorporate the average demand factor for each cell center and sector zone, the federal decision variables and the local sensing decisions from801. A fractional utility for assigning a PRB to a sector as opposed to the center zone is calculated. The capacity requirement for the cell-center and cell sector zones is calculated. The set of PRBs Cinitis assigned to the set Z. Capacity assignment variables gcand g(e),lare initialized to 0. These variables are used in steps508and509respectively. At step804, this process is linked to block504ofFIG. 5.

The advantages of the present method and system include, without limitation, a process that incorporates hybrid interaction between spectrum sensing and database of incumbent users to enable spectrum sharing between heterogeneous networks. Furthermore, the present method and system implements localized radio environment mapping as a support tool for spectrum sharing. Specific embodiments, though not limited to, present novel approaches to identifying the need for spectrum resources, a process for interacting with a spectrum database, allocating awarded spectrum to cell centers and cell edges, and allocating these resources amongst users in a single cell.

In a broad embodiment, the present method and system is a method and system for spectrum sharing between multiple heterogeneous users. The method implements an interaction between a spectrum server, database of incumbent users, and localized spectrum sensing.

While only a few embodiments of the present invention have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the present invention as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.

RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.