Patent Publication Number: US-2013229935-A1

Title: Method and apparatus for cooperation strategy selection in a wireless communication system

Description:
CROSS-REFERENCE 
     This application is a divisional of U.S. Non-Provisional Application Ser. No. 12/568,269, filed Sep. 28, 2009, and entitled “METHOD AND APPARATUS FOR COOPERATION STRATEGY SELECTION IN A WIRELESS COMMUNICATION SYSTEM”, which claims the benefit of U.S. Provisional Application Ser. No. 61/102,282, filed Oct. 2, 2008, and entitled “COOPERATION STRATEGY SELECTION IN NETWORK MIMO SYSTEMS,” the entirety of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     I. Field 
     The present disclosure relates generally to wireless communications, and more specifically to techniques for managing cooperative use of respective entities in a wireless communication environment. 
     II. Background 
     Wireless communication systems are widely deployed to provide various communication services; for instance, voice, video, packet data, broadcast, and messaging services can be provided via such wireless communication systems. These systems can be multiple-access systems that are capable of supporting communication for multiple terminals by sharing available system resources. Examples of such multiple-access systems include Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, and Orthogonal Frequency Division Multiple Access (OFDMA) systems. 
     As the demand for high-rate and multimedia data services rapidly grows, there has been an effort toward implementation of efficient and robust communication systems with enhanced performance. For example, in recent years, users have started to replace fixed line communications with mobile communications and have increasingly demanded great voice quality, reliable service, and low prices. In addition to mobile telephone networks currently in place, a new class of small base stations has emerged, which can be installed in the home of a user and provide indoor wireless coverage to mobile units using existing broadband Internet connections. Such personal miniature base stations are generally known as access point base stations, or, alternatively, Home Node B (HNB) or Femto cells. Typically, such miniature base stations are connected to the Internet and the network of a mobile operator via a Digital Subscriber Line (DSL) router, cable modem, or the like. 
     Wireless communication systems can be configured to include a series of wireless access points, which can provide coverage for respective locations within the system. Such a network structure is generally referred to as a cellular network structure, and access points and/or the locations they respectively serve in the network are generally referred to as cells. 
     Further, in a multiple-in-multiple-out (MIMO) communication system, multiple sources and/or destinations (e.g., corresponding to respective antennas) can be utilized for the transmission and reception of data, control signaling, and/or other information between devices in the communication system. The use of multiple sources and/or destinations for respective transmissions in connection with a MIMO communication system has been shown to yield higher data rates, improved signal quality, and other such benefits over single-input and/or single-output communication systems in some cases. One example of a MIMO communication system is a Network MIMO (N-MIMO) or Coordinated Multipoint (CoMP) system, in which a plurality of cells can cooperate to exchange information with one or more receiving devices, such as user equipment units (UEs) or the like. In such a wireless network implementation, it would be desirable to implement various improved techniques for selecting cooperation strategies and/or projected data rates for respective network users in order to enhance performance gains realized for the respective users via CoMP communication. 
     SUMMARY 
     The following presents a simplified summary of various aspects of the claimed subject matter in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its sole purpose is to present some concepts of the disclosed aspects in a simplified form as a prelude to the more detailed description that is presented later. 
     According to an aspect, a method is described herein. The method can comprise identifying a set of network users; obtaining information relating to signal qualities identified by respective network users and interference levels observed by the respective network users; and computing per-user projected rates for network multiple-in-multiple-out (N-MIMO) communication with the respective network users based at least in part on obtained information relating to the signal qualities and interference levels of the respective network users. 
     A second aspect described herein relates to a wireless communications apparatus, which can comprise a memory that stores data relating to a set of user equipment units (UEs). The wireless communications apparatus can further comprise a processor configured to identify information relating to signal qualities and interference levels associated with respective UEs and to compute per-UE projected rates for Coordinated Multipoint (COMP) communication with the respective UEs based at least in part on identified information relating to the respective UEs. 
     A third aspect relates to an apparatus, which can comprise means for obtaining channel state information from one or more terminals; means for estimating carrier and interference levels associated with the one or more terminals based on the channel state information; and means for calculating projected rates for the one or more terminals as a function of estimated carrier and interference levels associated with the one or more terminals. 
     A fourth aspect relates to a computer program product, which can comprise a computer-readable medium that comprises code for causing a computer to obtain channel state information from one or more UEs; code for causing a computer to estimate carrier and interference levels associated with the one or more UEs based on the channel state information; and code for causing a computer to calculate projected rates for the one or more UEs as a function of estimated carrier and interference levels associated with the one or more UEs. 
     A fifth aspect described herein relates to a method operable in a wireless communication system. The method can comprise identifying an amount of processing loss incurred in communication with at least one associated base station and reporting the amount of processing loss as feedback to the at least one associated base station. 
     A sixth aspect described herein relates to a wireless communications apparatus that can comprise a memory that stores data relating to at least one serving network node. The wireless communications apparatus can further comprise a processor configured to determine a processing loss associated with communication to the at least one serving network node and to report the processing loss as feedback to the at least one serving network node. 
     A seventh aspect relates to an apparatus operable in a wireless communication system. The apparatus can comprise means for identifying feedback information relating to device implementation loss associated with the apparatus and means for submitting the feedback information to one or more serving base stations. 
     An eighth aspect described herein relates to a computer program product, which can include a computer-readable medium that comprises code for causing a computer to identify at least one serving network node; code for causing a computer to determine a processing loss associated with communication to the at least one serving network node; and code for causing a computer to report the processing loss as feedback to the at least one serving network node. 
     A ninth aspect described herein relates to a method operable in a wireless communication system. The method can comprise identifying information relating to an extent of interference nulling capability of an associated receiver and reporting identified information relating to the extent of interference nulling capability of the associated receiver to at least one serving network node. 
     A tenth aspect described herein relates to a wireless communications apparatus, which can comprise a memory that stores data relating to at least one serving network node. The wireless communications apparatus can further comprise a processor configured to generate an indicator of interference nulling capability of a receiver associated with the wireless communications apparatus and to report the indicator as feedback to the at least one serving network node. 
     An eleventh aspect relates to an apparatus, which can comprise means for identifying feedback information relating to receiver nulling capability of the apparatus and means for submitting the feedback information to one or more serving base stations. 
     A twelfth aspect relates to a computer program product, which can comprise a computer-readable medium that comprises code for causing a computer to identify at least one serving network node; code for causing a computer to generate an indicator of interference nulling capability of a receiver associated with the apparatus; and code for causing a computer to report the indicator as feedback to the at least one serving network node. 
     To the accomplishment of the foregoing and related ends, one or more aspects of the claimed subject matter comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects of the claimed subject matter. These aspects are indicative, however, of but a few of the various ways in which the principles of the claimed subject matter can be employed. Further, the disclosed aspects are intended to include all such aspects and their equivalents. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a system for performing marginal utility and projected rate computation within a cooperative wireless communication environment in accordance with various aspects. 
         FIG. 2  illustrates an example cooperative communication deployment that can implement various aspects described herein. 
         FIG. 3  is a block diagram of a system for estimating a per-user projected rate based on user processing loss in accordance with various aspects. 
         FIG. 4  is a block diagram of a system for estimating a per-user projected rate based on receiver nulling capability of respective users in accordance with various aspects. 
         FIG. 5  is a block diagram of a system for optimizing per-user marginal utility computations in accordance with various aspects. 
         FIG. 6  is a block diagram of a system for selecting a cooperation strategy for communication between respective cell sites and respective terminal devices in a wireless communication system in accordance with various aspects. 
         FIGS. 7-9  are flow diagrams of respective methodologies for calculating per-user projected rates associated with a cooperative network transmission scheme. 
         FIGS. 10-11  are flow diagrams of respective methodologies for identifying and communicating feedback relating to a cooperative network transmission deployment. 
         FIGS. 12-13  are block diagrams of respective apparatus that facilitate initialization and use of respective cooperation strategies within a wireless communication environment. 
         FIG. 14  illustrates an example system that facilitates cooperative multipoint communication in accordance with various aspects described herein. 
         FIG. 15  illustrates an example wireless communication system in accordance with various aspects set forth herein. 
         FIG. 16  is a block diagram illustrating an example wireless communication system in which various aspects described herein can function. 
         FIG. 17  illustrates an example communication system that enables deployment of access point base stations within a network environment. 
     
    
    
     DETAILED DESCRIPTION 
     Various aspects of the claimed subject matter are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It may be evident, however, that such aspect(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. 
     As used in this application, the terms “component,” “module,” “system,” and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, an integrated circuit, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems by way of the signal). 
     Furthermore, various aspects are described herein in connection with a wireless terminal and/or a base station. A wireless terminal can refer to a device providing voice and/or data connectivity to a user. A wireless terminal can be connected to a computing device such as a laptop computer or desktop computer, or it can be a self contained device such as a personal digital assistant (PDA). A wireless terminal can also be called a system, a subscriber unit, a subscriber station, mobile station, mobile, remote station, access point, remote terminal, access terminal, user terminal, user agent, user device, or user equipment (UE). A wireless terminal can be a subscriber station, wireless device, cellular telephone, PCS telephone, cordless telephone, a Session Initiation Protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device having wireless connection capability, or other processing device connected to a wireless modem. A base station (e.g., access point or Node B) can refer to a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminals. The base station can act as a router between the wireless terminal and the rest of the access network, which can include an Internet Protocol (IP) network, by converting received air-interface frames to IP packets. The base station also coordinates management of attributes for the air interface. 
     Moreover, various functions described herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc (BD), where disks usually reproduce data magnetically and discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Various techniques described herein can be used for various wireless communication systems, such as Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single Carrier FDMA (SC-FDMA) systems, and other such systems. The terms “system” and “network” are often used herein interchangeably. A CDMA system can implement a radio technology such as Universal Terrestrial Radio Access (UTRA), CDMA2000, etc. UTRA includes Wideband-CDMA (W-CDMA) and other variants of CDMA. Additionally, CDMA2000 covers the IS-2000, IS-95 and IS-856 standards. A TDMA system can implement a radio technology such as Global System for Mobile Communications (GSM). An OFDMA system can implement a radio technology such as Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM®, etc. UTRA and E-UTRA are part of Universal Mobile Telecommunication System (UMTS). 3GPP Long Term Evolution (LTE) is an upcoming release that uses E-UTRA, which employs OFDMA on the downlink and SC-FDMA on the uplink. UTRA, E-UTRA, UMTS, LTE and GSM are described in documents from an organization named “3rd Generation Partnership Project” (3GPP). Further, CDMA2000 and UMB are described in documents from an organization named “3rd Generation Partnership Project 2” (3GPP2). 
     Various aspects will be presented in terms of systems that can include a number of devices, components, modules, and the like. It is to be understood and appreciated that the various systems can include additional devices, components, modules, etc. and/or can not include all of the devices, components, modules etc. discussed in connection with the figures. A combination of these approaches can also be used. 
     Referring now to the drawings,  FIG. 1  illustrates a system  100  for performing marginal utility and projected rate computation within a cooperative wireless communication environment in accordance with various aspects. As  FIG. 1  illustrates, system  100  can include one or more network cells (e.g., Node Bs, Evolved Node Bs (eNBs), base stations, access points, etc.)  110 , which can communicate with respective user equipment units (UEs, also referred to as mobile stations, terminals, user devices, etc.)  120 . In one example, respective cells  110  can correspond to and/or provide communication coverage for any suitable coverage area, such as an area associated with a macro cell, a femto cell (e.g., an access point base station or Home Node B (HNB)), and/or any other suitable type of coverage area. 
     In accordance with one aspect, a given UE  120  can communicate with any suitable number of network cells  110 . For example, a UE  120  can conduct one or more uplink (UL, also referred to as reverse link (RL)) communications to a cell  110 , and respective cells  110  can conduct one or more downlink (DL, also referred to as forward link (FL)) communications to the UE  120 . In one example, system  100  can utilize one or more network multiple-in-multiple-out (Network MIMO or N-MIMO), cooperative multipoint (COMP), and/or other techniques, by which a single UE  120  is enabled to communicate with a plurality of disparate cells  110  and/or sectors thereof. Additionally or alternatively, communication between a cell  110  and a UE  120  can result in a strong dominant interference to other nearby cells  110  and/or UEs  120 . For example, if a UE  120  is located at the edge of an area corresponding to a cell  110  serving the UE  120 , communication between the UE  120  and its serving cell can cause interference to one or more other cells  110  within range of the UE  120  with which the UE  120  is not communicating under various circumstances. This can occur, for example, in a system that includes femto cells if a UE  120  is located within the coverage area of a femto cell, which in turn is embedded into the coverage area of a macro cell. 
     In accordance with another aspect, respective cells  110  in system  100  can coordinate pursuant to one or more cooperation strategies in order to increase data rates associated with communication with a given UE  120  and/or to reduce interference caused to other cells  110  and/or UEs  120  in system  100 . In one example, respective cells  110  in system  100  can be operable to utilize a plurality of cooperation techniques for transmission to one or more UEs  120 , such as coordinated silencing (CS), joint transmission (JT) via inter-eNodeB (inter-cell) packet sharing, coordinated beamforming (CBF), and/or any other suitable cell cooperation technique(s) as generally known in the art. In another example, various operational aspects of system  100  such as respective cell cooperation techniques to be utilized for communication, cells  110  to be utilized for such cooperation techniques, and respective UEs  120  to be served via cooperative communication, can be based at least in part on marginal utility calculations performed by one or more cells  110  (e.g., via a utility computation module  112 ) and/or any other suitable metric. 
     In one example, utility associated with a given UE  120  can be defined in terms of channel quality, user priority level, or the like. In accordance with one aspect, utility computation module  112  can compute one or more channel quality metrics for a given UE  120  by estimating and/or otherwise assessing a channel component and an interference component of respective signals observed by the UE  120 . Utility computation module  112  can utilize, for example, a channel predictor  114 , an interference predictor  116 , and/or any other suitable component(s) in accordance with various aspects herein to obtain channel and/or interference estimates for a given UE  120 . In one example, information relating to channel quality and/or interference levels observed by a UE  120  can be reported from the UE  120  to a computing cell  110  via a channel/interference feedback module  122  and/or any other suitable means. 
     In accordance with a further aspect, a cell  110  in system  100  can utilize a projected rate computation module  118 , which can be utilized in addition to and/or in lieu of utility computation module  112  to calculate per-user projected rates for respective associated UEs  120 . In one example, a projected rate associated with a given UE  120  can correspond to an anticipated channel quality (e.g., given in terms of a Channel Quality Indicator or CQI) for the UE  120  based on various forms of cooperative transmission to the UE  120  (e.g., transmission by the computing cell alone, cooperative transmission by the computing cell and one or more other cells, etc.). Additionally or alternatively, a projected rate as computed by projected rate computation module  118  for a given UE  120  can correspond to an estimated data rate for the UE  120  based on a combination of conventional data rate projection techniques and signal/interference component estimates obtained via channel predictor  114  and/or interference predictor  116 . Specific examples of techniques that can be utilized to compute projected rates for respective UEs  120  are provided in further detail herein. 
     In one example, projected rate computation module  118  can leverage a general form for computing projected per-UE rates based on various factors. These factors can include, for example, propagation channels for respective links involved in a utilized cooperation strategy (e.g., taking into account power and bandwidth resources allocated per link); channel prediction accuracy based on projected downlink estimation error at respective UEs  120  and corresponding feedback delay; anticipated interference levels from cooperative and non-cooperative network nodes (e.g., cells  110  and/or UEs  120 ), taking into account spatial interference structures as applicable; and/or any other suitable factors. In one example, respective UEs  120  in system  100  can provide information relating to downlink estimation errors, feedback delay, UE processing loss, interference nulling capability, and/or other information relating to the operational capabilities of the respective UEs  120  to respective cells  110  via a UE capability feedback module  124  and/or any other suitable means. Various examples of information relating to UE capabilities that can be reported by UEs  120  in system  100 , as well as techniques by which such information can be processed by respective cells  110  in system  100 , are described in further detail herein. 
     In accordance with one aspect, respective cells  110  in system  100  can perform marginal utility and/or projected rate computations for a given UE  120  based on various requirements for channel state information at the transmitter (CSIT). CSIT requirements can vary, for example, based on a cooperation strategy employed by respective cells  110  with respect to a given UE  120 . By way of specific example, it can be appreciated that CSIT requirements associated with iterative signal processing and/or CBF can differ substantially between CSIT requirements for CS. In one example, a cell  110  can utilize an assumption of accurate CSIT at moderate to high post-processing carrier to interference (C/I) levels in order to employ first order approximation of an associated CSIT effect. Additionally or alternatively, in the event that a substantially high error effect (e.g., due to spatial error) is encountered, CS can be favored by cell  110  over more complex signal processing techniques. In accordance with one aspect, a threshold at which CS is selected over such techniques can be based on an empirical measure of channel prediction, as described in further detail herein. 
     In accordance with another aspect, projected rate calculation as performed by projected rate computation module  118  can proceed based on a channel structure as shown in diagram  200  in  FIG. 2 . As diagram  200  illustrates, various cooperative network nodes (e.g., located within a predetermined geographic area) can be utilized to conduct N-MIMO transmission to a set of users, while various non-cooperative nodes (e.g., located outside the predetermined geographic area) can cause interference to the cooperative network nodes and/or their served users in some cases. 
     In one example, based on the structure shown in diagram  200 , the following example definitions and derivations can be made by projected rate computation module  118  to facilitate projected rate calculation for a given user. The following examples assume frequency flat fading and single-user precoded MIMO communication wherein streams are treated as separate UEs; however, it should be appreciated that the definitions and derivations described herein could be extended to any suitable network model. For example, an extension to selective fading could be facilitated by ignoring error correlation across resources. 
     First, a matrix H can be defined as a M RX ×M channel matrix across all transmitter and receiver antennas (e.g., corresponding to network nodes and UEs, respectively) within a strategy S, wherein M RX  and M TX  correspond to the number of receiver antennas and the number of transmitter antennas, respectively. In one example, multiple transmitter (or receiver) antennas per node (or UE) can be allowed. Further, a M TX ×M UE  transmit beamforming matrix W and a M RX ×M UE  receive beamforming matrix Z can be defined, wherein M UE  represents the number of UEs for which rates are computed. In addition, I u  can be defined as receiver interference covariance contributed to a u-th UE by non-cooperative nodes. In one example, the above definitions can be leveraged to define a projected rate for a u-th UE at time t as 
     
       
         
           
             
               
                 
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     Subsequently, if projected rate computation module  118  operates under an assumption that the channel estimate as provided above is a minimum mean square error (MMSE) estimate, projected rate computation module  118  can utilize the MMSE approximation to account for channel estimation error together with scheduling delay. This can be done by, for example, including mismatch introduced by simplifications and/or imperfect tuning, as defined as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
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     Subsequently, projected rate computation module  118  can approximate a projected rate based on expected information rate conditioned on CSIT as follows: 
     
       
         
           
             
               
                 
                   
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             ; 
           
         
       
       
         
           
             
               
                 C 
                 u 
               
               = 
               
                 
                    
                   
                     
                       
                         Z 
                         ⋒ 
                       
                       u 
                     
                      
                     
                       H 
                       ⋒ 
                     
                      
                     
                       
                         W 
                         ⋒ 
                       
                       u 
                     
                   
                    
                 
                 2 
               
             
             ; 
           
         
       
       
         
           
             
               I 
               u 
             
             = 
             
               
                 
                   
                     Z 
                     ⋒ 
                   
                   u 
                 
                  
                 
                   
                     I 
                     ⋒ 
                   
                   u 
                 
                  
                 
                   
                     Z 
                     ⋒ 
                   
                   u 
                   * 
                 
               
               + 
               
                 
                   ∑ 
                   
                     
                       u 
                       ′ 
                     
                     ≠ 
                     u 
                   
                 
                  
                 
                   
                     
                        
                       
                         
                           
                             Z 
                             ⋒ 
                           
                           u 
                         
                          
                         
                           H 
                           ⋒ 
                         
                          
                         
                           
                             W 
                             ⋒ 
                           
                           
                             u 
                             ′ 
                           
                         
                       
                        
                     
                     2 
                   
                   . 
                 
               
             
           
         
       
     
     In the above derivations and definitions, {umlaut over (H)}, {umlaut over (W)}, and {umlaut over (Z)} are empirical counterparts of H, W, and Z obtained by replacing H with {umlaut over (H)}, and Ï u  is an estimate of I u . 
     As further shown in the above derivations, approximate conditional expected information rate calculation can be conducted by projected rate computation module  118  by utilizing weights ä l  that are computed based on by-products of the C/I calculations described above. In addition, ρ l,l  as used above corresponds to estimation error covariance matrices that depend on an exact form of the estimate, which can be utilized to allow for simple closed form approximations in some cases. In one example, the weights ä l  can be computed using the following: 
     
       
         
           
             
               
                 
                   a 
                   ⋒ 
                 
                 l 
               
               = 
               
                 
                   1 
                   
                     
                       C 
                       ⋒ 
                     
                     u 
                   
                 
                  
                 
                   ( 
                   
                     
                       
                          
                         
                           
                             W 
                             ⋒ 
                           
                           
                             l 
                             , 
                             u 
                           
                         
                          
                       
                       2 
                     
                     - 
                     
                       
                         
                           
                             C 
                             ⋒ 
                           
                           u 
                         
                         
                           
                             I 
                             ⋒ 
                           
                           u 
                         
                       
                        
                       
                         
                            
                           
                             
                               W 
                               ⋒ 
                             
                             
                               l 
                               , 
                               
                                 
                                   u 
                                   ′ 
                                 
                                 ≠ 
                                 u 
                               
                             
                           
                            
                         
                         2 
                       
                     
                     - 
                     
                       
                         2 
                         
                           
                             I 
                             ⋒ 
                           
                           u 
                         
                       
                        
                       
                         
                           ∑ 
                           
                             
                               u 
                               ′ 
                             
                             ≠ 
                             u 
                           
                         
                          
                         
                           Re 
                            
                           
                             { 
                             
                               
                                 
                                   W 
                                   ⋒ 
                                 
                                 
                                   l 
                                   , 
                                   u 
                                 
                               
                                
                               
                                 
                                   W 
                                   ⋒ 
                                 
                                 
                                   l 
                                   , 
                                   
                                     u 
                                     ′ 
                                   
                                 
                                 * 
                               
                                
                               
                                 
                                   X 
                                   ⋒ 
                                 
                                 
                                   u 
                                   , 
                                   
                                     u 
                                     ′ 
                                   
                                 
                                 * 
                               
                                
                               
                                 
                                   X 
                                   ⋒ 
                                 
                                 
                                   u 
                                   , 
                                   
                                     u 
                                     ′ 
                                   
                                 
                               
                             
                             } 
                           
                         
                       
                     
                   
                   ) 
                 
               
             
             , 
           
         
       
     
     wherein {umlaut over (X)}={umlaut over (Z)}{umlaut over (H)}{umlaut over (W)}. Further, in some cases the summation in the above equation can be neglected at high C/I levels where, e.g., {umlaut over (X)} u,u′≠u →0. 
     With further reference to system  100  in  FIG. 1 , projected rate computation module  118 , with the aid of and/or independently of utility computation module  112 , can facilitate calculation of a per-user projected rate based on various factors. By way of a first example, projected rate computation module  118  can utilize a unified projected rate calculation rule, which can be based on a first order expansion of the average information rate and/or any other suitable parameters. In accordance with one aspect, a projected rate computed by projected rate computation module  118  pursuant to a unified projected rate calculation rule can be configured to be a sufficiently accurate approximation for JT, CBF, and/or other suitable operations, while additionally being sufficiently accurate for CS and/or similar operations. In one example, channel estimation error of silenced nodes associated with a projected rate computed using a unified calculation rule can be substantially large; however, it can be appreciated that such error can be mitigated via low receive power seen from the silenced nodes at a corresponding UE  120 . In another example, a unified calculation rule as described above can be modified for high mobility cases (e.g., with or without correlated antennas). 
     By way of a second example, projected rate computation module  118  can perform ambient interference assessment in the context of projected rate calculation. In one example, ambient interference associated with a UE  120  can be determined based on a number of cells  110  serving the UE  120 . For example, in the event that more than one cell  110  serves a UE  120 , ambient interference associated with the UE  120  can be determined by projected rate computation module  118  under an assumption of full power transmission from all non-cooperative nodes within range of the UE  120 . Alternatively, loading indicators associated with respective cells  110  (e.g., provided as feedback from a UE  120  within range of the cells  110  and/or by the cells  110  themselves over a backhaul link) can be utilized by projected rate computation module  118  such that interference can be discounted from nodes indicated as unloaded. In another example, in the event that a single cell  110  serves a given UE  120 , the UE  120  can report an ambient interference estimate back to projected rate computation module  118  via conventional (e.g., traffic) CQI feedback and/or by any other suitable means. 
     By way of a third example, projected rate computation module  118  can leverage various CSIT estimation considerations in determining a per-user projected rate. For example, projected rate computation module  118  can track long-term statistics of respective channels in time, frequency, space, or the like. By doing so, it can be appreciated that projected rate computation module  118  can improve channel estimation and extrapolation accuracy for flat channels in time, frequency, space, or the like. Further, it can be appreciated that such tracking can enable projected rate computation module  118  to exploit spatial correlation of co-located antennas to, for example, facilitate beamforming gains for high mobility and/or low C/I UEs. In another example, projected rate computation module  118  can utilize a simple model fitting (e.g., a one-tap separable infinite impulse response (IIR) model or the like) to approximate correlations in time, frequency, space, and so on. 
     In accordance with one aspect, projected rate computation module  118  can further take processing or implementation loss of a given UE  120  (e.g., a parameter Γ u  corresponding to a u-th UE as provided in the above derivations) into account in determining a projected rate for the UE  120 . This is illustrated in further detail by diagram  300  in  FIG. 3 . In conventional systems involving a single cell communicating with a single UE, CQI information is generally reported from the UE to the cell as a maximum supportable data rate as opposed to interference parameters for respective non-serving cells. However, it can be appreciated that such a generalized report does not provide information regarding processing loss incurred by the UE as a result of, for example, channel implementations utilized by the UE, soft decoding techniques leveraged by the UE, or other such causes. Instead, such processing or implementation loss is absorbed into the generalized CQI reports provided by the UE. Further, it can be appreciated that processing loss associated with a given UE does not scale for N-MIMO communication (e.g., for an increasing number of serving cells), as strategy selection is generally performed by the network. 
     In the context of N-MIMO or CoMP communication, one or more cells  110  associated with a given UE  120  can facilitate selection of respective cells  110  to be involved in a given communication with the UE  120 , and as a result channel information and interference can be observed at the UE  120  from a plurality of different cells  110 . Subsequently, in a manner similar to that described above with respect to system  100 , carrier and interference estimates corresponding to various cells  110  and corresponding UEs  120  can be obtained, which can be leveraged by projected rate computation module  118  to map respective UEs  120  to projected data rates. Accordingly, projected rate computation module  118  can utilize information relating to processing loss of various UEs  120  to aid in determining per-user data rates corresponding to respective UEs  120 . 
     In accordance with one aspect, information relating to user processing or implementation loss can be made known to projected rate computation module  118  and/or utility computation module  112  in various manners. As a first example, processing loss can be defined as a UE-specific parameter, which can be provided as feedback from respective UEs  120  via an associated processing loss indicator module  312  and/or other suitable means. Thus, for example, UE  120  can be configured to provide long-form feedback to cell  110  that includes one or more bits relating to a processing loss associated with UE  120  (e.g., in dB and/or any other suitable unit(s)) via processing loss indicator module  312 , such that the processing loss information can subsequently be utilized by cell  110  in making scheduling decisions. As a second example, a maximum processing loss can be defined within system  300  for respective UEs and/or groups of UEs (e.g., per UE category) via a minimum performance specification (MPS) for system  300  or the like. A maximum processing loss obtained in this manner can serve as a limit on processing loss reported by a given UE  120 , or alternatively a maximum processing loss defined for a given UE  120  or UE category to which the UE  120  belongs can be utilized by projected rate computation module  118  as a default or assumed processing loss for the UE  120  in performing projected rate calculation. 
     In accordance with an additional aspect, respective UEs  120  in a wireless communication system can be configured to include receiver interference nulling capability and/or similar capability, which can be taken into account by projected rate computation module  118  in determining a projected rate for the respective UEs  120 . This is illustrated in further detail by diagram  400  in  FIG. 4 . 
     As illustrated in system  400 , a UE  120  can include a receiver nulling module  412  and/or other similar mechanisms, which can be utilized by UE  120  to filter and/or otherwise eliminate interference from one or more network nodes. By way of specific example, a UE  120  equipped with two or more receive antennas and corresponding receiver nulling capability via a receiver nulling module  412  can be located in a network environment that includes a serving macro cell and a non-serving femto cell (e.g., a restricted access cell that UE  120  does not have permission to access) that is sufficiently proximate to UE  120  to cause the femto cell to be a dominant interferer for UE  120 . In such an example, in the event that no additional dominant interferers are present and channels associated with the serving macro cell and non-serving femto cell are substantially static, UE  120  can leverage receiver nulling module  412  in order to build a filter to null interference caused by the non-serving femto cell. As a result, it can be appreciated that receiver interference nulling capability of a UE  120  can substantially change post-processing C/I levels associated with the UE  120  due to the fact that the interference nulling in this manner enables high channel quality to be maintained with minimal cooperation between cells in a dominant interferer scenario. Thus, in accordance with one aspect, a projected rate computation module  118 , a utility computation module  112 , and/or other suitable components of a network cell  110  can account for receiver nulling gains in determining per-user projected rates, thereby substantially improving strategy selection performance. 
     In accordance with one aspect, information relating to receiver nulling capability of a UE  120  can be made known to respective cells  110  in system  400  in various manners. For example, UE capabilities can be defined in terms of receiver interference nulling, which can be provided to a given cell  110  via a receiver nulling indicator  414  and/or other suitable means of a UE  120 , based on which a projected rate computation module  118  can account for the specific nulling capabilities of the UE  120  (e.g., in terms of a maximum amount of interference, number of interferers, etc., capable of being nulled) in determining a projected rate for UE  120 . Defining and facilitating feedback of receiver nulling capability in this manner can, in one example, assume spatial receiver MMSE with a maximum processing loss enforced through performance tests or the like. 
     As an alternative example, minimum nulling requirements can be defined within system  400  for respective UEs and/or groups of UEs (e.g., per UE category) via network-wide requirements associated with respective UEs  120 . By way of example, a UE  120  with n receive antennas that is capable of n-th order MIMO can be mandated to support receiver interference nulling. More particularly, RX nulling capability for a given UE  120  can be mandated via an MPS and/or other suitable specification based on UE category or the like. For instance, a UE  120  which is capable of supporting n-th order MIMO can be mandated to be capable of nulling (n-1) dominant interferers. More generally, respective UEs  120  can be mandated such that a UE  120  can be configured to simultaneously support m MIMO streams and null up to k dominant interferers, where (m+k)&lt;n. This can be achieved by, for example, implementing minimum mean square error (MMSE) receiver techniques at a given UE  120 . 
     A minimum nulling capability level obtained in this manner can serve as a floor on nulling capability reported by a given UE  120 , or alternatively respective cells  110  can assume that respective UEs  120  comply with the mandated nulling requirements and base scheduling decisions on the mandated requirements. 
     Referring next to  FIG. 5 , a block diagram of a system  500  for optimizing per-user marginal utility computations in accordance with various aspects is illustrated. As shown in  FIG. 5 , system  500  can include one or more cells  110 , which can communicate with respective associated UEs  120  as generally described herein. In one example, cell  110  can include a utility computation module  112 , which can facilitate calculation of marginal utility parameters associated with respective UEs  120  in connection with selection of a cooperation strategy to be utilized for one or more UEs  120 . As system  500  further illustrates, a cell  110  can further include a utility optimization module  512 , which can be utilized to optimize utility parameters calculated by utility computation module  112 . 
     In accordance with one aspect, utility optimization module  512  can perform transmit processing optimization via strategy utility maximization. This can be achieved using, for example, one or more iterative utility maximization algorithms (e.g., algorithms similar to iterative pricing), wherein an iterative search is performed at respective network nodes (e.g., cells  110 , sectors within cells  110 , etc.) for respective candidate cooperation strategies. In one example, utility optimization module  512  can take into account various cooperation technique constraints, which can be, for example, reflected in constraints on the beam coefficients of various nodes. In another example, utility optimization module  512  can utilize first order extension to update respective beam weights at each iteration until convergence. In various implementations, convergence can be made dependent on an algorithm starting point. 
     The algorithm starting point can, in accordance with another aspect, be selected in a variety of manners. For example, a starting point can be selected via zero-forcing (ZF) across respective cooperating nodes, maximum ratio combining (MRC) and/or MMSE-based approaches, or the like. In one example, power allocation techniques can be applied in addition to ZF and/or MRC. 
     In accordance with another aspect, utility optimization module  512  can utilize iterative processing, which can be conducted as follows. It should be appreciated that iterative processing as performed based on the following discussion can utilize a substantial portion of the assumptions and notations utilized above with respect to projected rate calculation. Initially, utility of a marginal strategy can be expressed by utility optimization module  512  as a function of C/I values of respective UEs  120  served by the strategy (e.g., based on single-user precoded MIMO, wherein streams are treated as separate UEs  120 ), or 
     
       
         
           
             
               
                 
                   U 
                   t 
                 
                  
                 
                   ( 
                   S 
                   ) 
                 
               
               = 
               
                 
                   ∑ 
                   
                     u 
                     ∈ 
                     
                       Y 
                        
                       
                         ( 
                         S 
                         ) 
                       
                     
                   
                 
                  
                 
                   
                     p 
                     
                       u 
                       , 
                       t 
                     
                   
                    
                   
                     
                       R 
                       
                         u 
                         , 
                         t 
                       
                     
                      
                     
                       ( 
                       S 
                       ) 
                     
                   
                 
               
             
             , 
             
               
                 where 
                  
                 
                     
                 
                  
                 
                   
                     R 
                     
                       u 
                       , 
                       t 
                     
                   
                    
                   
                     ( 
                     S 
                     ) 
                   
                 
               
               = 
               
                 I 
                  
                 
                   ( 
                   
                     
                       1 
                       
                         Γ 
                         u 
                       
                     
                      
                     
                       
                         C 
                         u 
                       
                       
                         I 
                         u 
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     as noted above. Based on this expression, utility optimization module  512  can calculate derivatives of the strategy utility with respect to the C/I values of the involved UEs  120  as 
     
       
         
           
             
               
                 ∂ 
                 
                   
                     U 
                     t 
                   
                    
                   
                     ( 
                     S 
                     ) 
                   
                 
               
               
                 ∂ 
                 
                   ( 
                   
                     
                       C 
                       u 
                     
                     / 
                     
                       I 
                       u 
                     
                   
                   ) 
                 
               
             
             , 
             
               
                 u 
                 ∈ 
                 
                   Y 
                    
                   
                     ( 
                     S 
                     ) 
                   
                 
               
               = 
               
                 
                   
                     p 
                     
                       u 
                       , 
                       t 
                     
                   
                   
                     Γ 
                     u 
                   
                 
                  
                 
                   
                     
                       I 
                       ′ 
                     
                      
                     
                       ( 
                       
                         
                           1 
                           
                             Γ 
                             u 
                           
                         
                          
                         
                           
                             C 
                             u 
                           
                           
                             I 
                             u 
                           
                         
                       
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
     Accordingly, based on a notation that defines U t (S(W)) as the utility associated with strategy S based on transmit beamforming matrix W, utility optimization module  512  can determine a proper selection of Z subject to H and W by solving for a variable Φ u  in the following: 
     
       
         
           
             
               
                 
                   U 
                   t 
                 
                  
                 
                   ( 
                   
                     S 
                      
                     
                       ( 
                       
                         W 
                         + 
                         
                           Δ 
                            
                           
                               
                           
                            
                           W 
                         
                       
                       ) 
                     
                   
                   ) 
                 
               
               = 
               
                 
                   
                     U 
                     t 
                   
                    
                   
                     ( 
                     
                       S 
                        
                       
                         ( 
                         W 
                         ) 
                       
                     
                     ) 
                   
                 
                 + 
                 
                   
                     ∑ 
                     
                       u 
                       ∈ 
                       
                         Y 
                          
                         
                           ( 
                           S 
                           ) 
                         
                       
                     
                   
                    
                   
                     Re 
                      
                     
                       { 
                       
                         
                           W 
                           u 
                           * 
                         
                          
                         
                           H 
                           * 
                         
                          
                         
                           Φ 
                           u 
                         
                          
                         Δ 
                          
                         
                             
                         
                          
                         
                           W 
                           u 
                         
                       
                       } 
                     
                   
                 
                 + 
                 
                   O 
                    
                   
                     ( 
                     
                       
                          
                         
                           Δ 
                            
                           
                               
                           
                            
                           W 
                         
                          
                       
                       2 
                     
                     ) 
                   
                 
               
             
             ; 
           
         
       
       
         
           
             
               Φ 
               u 
             
             = 
             
               
                 
                   2 
                   
                     I 
                     u 
                   
                 
                  
                 
                   
                     ∂ 
                     
                       
                         U 
                         t 
                       
                        
                       
                         ( 
                         S 
                         ) 
                       
                     
                   
                   
                     ∂ 
                     
                       ( 
                       
                         
                           C 
                           u 
                         
                         / 
                         
                           I 
                           u 
                         
                       
                       ) 
                     
                   
                 
                  
                 
                   Z 
                   u 
                   * 
                 
                  
                 
                   Z 
                   u 
                 
               
               - 
               
                 
                   ∑ 
                   
                     
                       u 
                       ′ 
                     
                     ≠ 
                     u 
                   
                 
                  
                 
                   
                     2 
                     
                       I 
                       u 
                     
                   
                    
                   
                     
                       ∂ 
                       
                         
                           U 
                           t 
                         
                          
                         
                           ( 
                           S 
                           ) 
                         
                       
                     
                     
                       ∂ 
                       
                         ( 
                         
                           
                             C 
                             u 
                           
                           / 
                           
                             I 
                             u 
                           
                         
                         ) 
                       
                     
                   
                    
                   
                     
                       C 
                       
                         u 
                         ′ 
                       
                     
                     
                       I 
                       
                         u 
                         ′ 
                       
                     
                   
                    
                   
                     Z 
                     
                       u 
                       ′ 
                     
                     * 
                   
                    
                   
                     
                       Z 
                       
                         u 
                         ′ 
                       
                     
                     . 
                   
                 
               
             
           
         
       
     
     In accordance with a further aspect, upon obtaining a starting point for optimization for a value of W and a corresponding value of Z (e.g., based on ZF, MRC, MMSE, etc.), utility optimization module  512  can iterative conduct the following optimization procedure. First, utility optimization module  512  can calculate C/I levels for respective UEs  120  and update Φ u  accordingly based on the latest values of W and Z. Next, a tentative update of W can be defined according to the utility gradient as W u :=W u +μH*Φ u HW u  for u ε Y(S). Subsequently, utility optimization module  512  can modify W to reflect respective applicable constraints. For example, entries W l,u  in W can be zeroed out if transmitter l is not serving UE u. In addition, W can be scaled to ensure that maximum power constraints of respective transmitters are not exceeded. Upon modifying W, Z can be updated based on the modifications to W. Finally, if the corresponding increase in U t (S(W)) is less than a predefined threshold, or a maximum number of iterations is reached, optimization can complete; otherwise, the above steps can be repeated. 
     In accordance with an additional aspect, utility optimization module  512  can obtain a starting point for the above procedure based on zero-forcing with water filling. More particularly, utility optimization module  512  can start with a normalized zero-forcing scheme  W  having the same transmitter energy for substantially all UEs  120 . Based on this scheme, water filling can be defined as follows: 
     
       
         
           
             
               
                 W 
                 _ 
               
               = 
               
                 
                   
                     
                       H 
                       * 
                     
                      
                     
                       ( 
                       
                         HH 
                         * 
                       
                       ) 
                     
                   
                   
                     - 
                     1 
                   
                 
                  
                 diag 
                  
                 
                   
                     { 
                     
                       
                         ( 
                         
                           HH 
                           * 
                         
                         ) 
                       
                       
                         - 
                         1 
                       
                     
                     } 
                   
                   
                     
                       - 
                       1 
                     
                     / 
                     2 
                   
                 
               
             
             ; 
           
         
       
       
         
           
             W 
             = 
             
               
                 W 
                 _ 
               
                
               
                   
               
                
               diag 
                
               
                 
                   
                     { 
                     
                       
                         P 
                         u 
                       
                     
                     } 
                   
                   
                     u 
                     ∈ 
                     
                       Y 
                        
                       
                         ( 
                         S 
                         ) 
                       
                     
                   
                 
                 . 
               
             
           
         
       
     
     Next, utility optimization module  512  can select the total powers P u  allocated per UE according to water filling in a way that maximizes U t (S(W)). For example, it can be appreciated that generalized water filling over {P u } can apply to the sum-utility 
     
       
         
           
             
               
                 U 
                 t 
               
                
               
                 ( 
                 S 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   u 
                   ∈ 
                   
                     Y 
                      
                     
                       ( 
                       S 
                       ) 
                     
                   
                 
               
                
               
                 
                   p 
                   
                     u 
                     , 
                     t 
                   
                 
                  
                 
                   
                     I 
                     ( 
                     
                       
                         
                           P 
                           u 
                         
                         
                           Γ 
                           u 
                         
                       
                        
                       
                         
                           
                              
                             
                               
                                 Z 
                                 u 
                               
                                
                               
                                 HW 
                                 u 
                               
                             
                              
                           
                           2 
                         
                         
                           
                             Z 
                             u 
                           
                            
                           
                             I 
                             u 
                           
                            
                           
                             Z 
                             u 
                             * 
                           
                         
                       
                     
                     ) 
                   
                   . 
                 
               
             
           
         
       
     
     Based on this formulation, I ( . . . ) can be approximated by the unconstrained capacity, and a constraint on the total transmit power can be given by 
     
       
         
           
             
               ∑ 
               
                 u 
                 = 
                 
                   Y 
                    
                   
                     ( 
                     S 
                     ) 
                   
                 
               
             
              
             
               
                 P 
                 u 
               
               . 
             
           
         
       
     
     Subsequently, utility optimization module  512  can normalize an obtained solution to meet per-transmit antenna power constraints. While the above example is specific to ZF, it should be appreciated that similar techniques could be utilized for MRC, MMSE, and/or any other suitable technique(s). 
     Turning to  FIG. 6 , a block diagram of a system  600  for selecting a cooperation strategy for communication between respective cell sites (e.g., cells  110 ) and respective terminal devices (e.g., UEs  120 ) in a wireless communication system is illustrated. As shown in system  600 , respective cells  110  can include a utility computation module  112  for determining marginal utility associated with respective users as generally described above, based on which a cooperation strategy selector  620  can coordinate respective transmissions between cells  110  and UEs  120  in system  600 . In general, cooperation strategy selector  620  can be utilized by a cell  110  to compute and/or make scheduling decisions relating to node clustering, scheduling, forms of cooperative transmission to be utilized, and so on. To these ends, cooperation strategy selector  620  can include a node selector  622  for scheduling respective nodes to be utilized for communication with a given UE  120 , a cooperation type selector  624  to determine a form of cooperation to utilize for communication with a given UE  120 , and/or other suitable mechanisms. 
     In accordance with one aspect, a cooperation strategy can be selected by cooperation type selector  624  based on factors such as UE mobility (e.g., as determined by a mobility analyzer  612 ), C/I levels associated with respective UEs  120  (e.g., as identified by a channel/interference analyzer  614 ), capabilities of backhaul links between respective cells, or the like. By way of example, cooperation type selector  624  can select CS and/or another similar simple form of cell cooperation in the case of high-mobility UEs and/or rapidly changing channel conditions associated with a given UE  120 . Additionally or alternatively, if mobility of a given UE  120  is determined to be low, or a high degree of antenna correlation is present with respect to the UE  120 , more advanced cooperation techniques such as JT via inter-cell packet sharing (e.g., in the case of a relatively slow backhaul link between cells  110 ) or CBF (e.g., in the case of a relatively fast backhaul link between cells  110 ) can be selected. 
     In accordance with another aspect, a projected rate associated with respective UEs (e.g., as computed in accordance with various examples described above) can be utilized along with factors such as backhaul bandwidth, latency constraints, or the like, to select between respective cooperation techniques. For example, cooperation type selector  624  can rule out a JT technique using backhaul bandwidth and latency uncertainty based on associated a priori and/or long-term backhaul link classifications. In another example, CSIT delivery delay and accuracy, as well as scheduling delay and/or other suitable factors, can be factored in projected rate calculation. 
     By way of specific example, cooperation type selector  624  can utilize a set of cooperation technique selection rules as follows. First, cooperation type selector  624  can rule out a JT technique based on a long-term backhaul link classification. Further, cooperation type selector  624  can consider CBF techniques over JT in the event that a ratio of a combined energy C/I to the best node C/I is below a predefined threshold. In addition, if an associated channel prediction error is above a threshold value, cooperation type selector  624  can consider CS (e.g., in the event that CBF and/or JT are possible). 
     Referring now to  FIGS. 7-11 , methodologies that can be performed in accordance with various aspects set forth herein are illustrated. While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts can, in accordance with one or more aspects, occur in different orders and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all illustrated acts may be required to implement a methodology in accordance with one or more aspects. 
     With reference to  FIG. 7 , illustrated is a methodology  700  for calculating per-user projected rates associated with a cooperative network transmission scheme. It is to be appreciated that methodology  700  can be performed by, for example, a network cell (e.g., cell  110  in system  100 ) and/or any other appropriate network device. Methodology  700  begins at block  702 , wherein a set of users (e.g., UEs  120 ) are identified. Next, at block  704 , information relating to signal quality identified by respective users identified at block  702  and interference levels observed by the respective users is obtained (e.g., by a channel predictor  114  and an interference predictor  116 , respectively). Methodology  700  can then conclude at block  706 , wherein per-user projected rates for the respective users identified at block  702  are computed (e.g., by a projected rate computation module  118 ) based at least in part on the information obtained at block  704 . 
     Turning next to  FIG. 8 , a flow diagram of a methodology  800  for calculating per-user projected rates based on user processing loss information is illustrated. Methodology  800  can be performed by, for example, a base station and/or any other appropriate network entity. Methodology  800  begins at block  802 , wherein an associated wireless terminal is identified. Methodology  800  can then proceed to block  804  and/or block  806  from block  802 . More particularly, at block  804 , an amount of processing loss associated with the wireless terminal identified at block  802  is identified based at least in part on feedback received from the wireless terminal (e.g., via a processing loss indicator module  312 ). Additionally or alternatively, at block  806 , an amount of processing loss associated with the wireless terminal identified at block  802  is identified based at least in part on a processing loss limit associated with the wireless terminal or a user category to which the wireless terminal belongs (e.g., a per-UE or per-UE category maximum processing loss mandated in an associated MPS and/or by any other suitable means). 
     In accordance with one aspect, methodology  800  can perform the acts described at block  804 , the acts described at block  806 , or a combination thereof. For example, a mandated processing loss limit associated with a given wireless terminal identified at block  806  can serve as a cap or a floor on a processing loss obtained from the wireless terminal at block  804 . Additionally or alternatively, a mandated processing loss limit as given at block  806  can be utilized as a default processing loss in the event that no feedback relating to processing loss is received from the wireless terminal. In one example, upon completing the acts described at block  804  and/or block  806 , methodology  800  can conclude at block  808 , wherein a projected rate for the wireless terminal is computed as a function of the amount of processing loss associated with the wireless terminal as identified at block  804  and/or block  806 . 
       FIG. 9  illustrates a methodology  900  for calculating per-user projected rates based on receiver interference nulling indicators. Methodology  900  can be performed by, for example, a wireless network node and/or any other suitable network device. Methodology  900  begins at block  902 , wherein an associated UE is identified. From block  902 , methodology can proceed to block  904 , wherein an extent of interference nulling capability of the UE identified at block  902  is identified based at least in part on feedback received from the UE (e.g., via a receiver nulling indicator module  414 ), and/or to block  906 , wherein an extent of interference nulling capability of the UE is identified based at least in part on a predefined minimum requirement associated with the UE or a UE category corresponding to the UE (e.g., as mandated in an associated network specification and/or otherwise set throughout an associated communication network). 
     In accordance with one aspect, methodology  900  can perform the acts described at block  904 , the acts described at block  906 , or a combination thereof. For example, a mandated minimum nulling capability associated with a given UE as identified at block  906  can serve as a cap or a floor on interference nulling capability feedback received at block  904 . Additionally or alternatively, receiver nulling requirements identified at block  906  can be utilized as a default parameter for the UE identified at block  902  in the event that no feedback relating to interference nulling is received from the UE. In one example, upon completing the acts described at block  904  and/or block  906 , methodology  900  can conclude at block  908 , wherein a projected rate for the UE identified at block  902  is computed as a function of the extent of interference nulling capability indicated by and/or otherwise associated with the UE at block  904  and/or block  906 . 
     Referring next to  FIG. 10 , illustrated is a methodology  1000  for identifying and communicating feedback relating to a cooperative network transmission deployment. It is to be appreciated that methodology  1000  can be performed by, for example, a wireless terminal (e.g., UE  120 ) and/or any other appropriate network device. Methodology  1000  begins at block  1002 , wherein an amount of processing loss incurred in communication with at least one base station (e.g., a cell  110 ) is identified. In one example, an amount of processing loss identified at block  1002  can be based at least in part on a mandated processing loss limit associated with an entity performing methodology  1000 . Thus, for example, either an actual processing loss or a mandated processing loss can be identified at block  1002  under various circumstances. Upon completing the acts described at block  1002 , methodology  1000  can conclude at block  1004 , wherein the amount of processing loss identified at block  1002  is reported as feedback to the at least one base station (e.g., using a processing loss indicator module  312 ). 
     Turning to  FIG. 11 , a flow diagram of another methodology  1100  for identifying and communicating feedback relating to a cooperative network transmission deployment is illustrated. Methodology  1100  can be performed by, for example, a UE and/or any other suitable network device. Methodology  1100  begins at block  1102 , wherein information relating to an extent of interference nulling capability of an associated receiver is identified. In one example, receiver nulling capability as identified at block  1102  can be based at least in part on a system-wide interference nulling specification associated with an entity performing methodology  1100 . Thus, for example, information relating to either actual interference nulling capability or specified and/or mandated interference nulling capability can be identified at block  1102  under various circumstances. Upon completing the acts described at block  1102 , methodology  1100  can conclude at block  1104 , wherein the information identified at block  1102  is reported to at least one serving network node (e.g., using a receiver nulling indicator module  414 ). 
     Referring next to  FIGS. 12-13 , respective apparatuses  1200 - 1300  that can be utilized in accordance with various aspects described herein are illustrated. It is to be appreciated that apparatuses  1200 - 1300  are represented as including functional blocks, which can be functional blocks that represent functions implemented by a processor, software, or combination thereof (e.g., firmware). 
     With reference to  FIG. 12 , an apparatus  1200  that facilitates initialization and use of respective cooperation strategies within a wireless communication environment is illustrated. Apparatus  1200  can be implemented by a network cell (e.g., cell  110 ) and/or another suitable network entity and can include a module  1202  for obtaining channel state information from one or more terminals; a module  1204  for estimating carrier and interference levels associated with the one or more terminals based on the channel state information; and a module  1206  for calculating projected rates for the one or more terminals as a function of the carrier and interference levels associated with the one or more terminals. 
       FIG. 13  illustrates another apparatus  1300  that facilitates initialization and use of respective cooperation strategies within a wireless communication environment. Apparatus  1300  can be implemented by a UE (e.g., UE  120 ) and/or another suitable network device and can include a module  1302  for identifying feedback information relating to device implementation loss and/or receiver nulling capability and a module  1304  for submitting the feedback information to be provided to one or more serving base stations. 
     Referring next to  FIG. 14 , an example system  1400  that facilitates cooperative multipoint communication in accordance with various aspects described herein is illustrated. As  FIG. 14  illustrates, system  1400  can include respective user devices  1430  that can communicate with one or more associated network cells, such as serving cell(s)  1410  and auxiliary cell(s)  1420 . While the names “serving cell” and “auxiliary cell” are used to refer to network cells  1410 - 1420 , it should be appreciated that no functionality of cells  1410 - 1420  is intended to be implied by such naming. For example, an auxiliary cell  1420  can serve a user device  1430  by providing communication coverage for user device  1430  in addition to, or in place of, a serving cell  1410  in some cases. Further, cells  1410 - 1420  can be any of any suitable cell type(s), including, for example, macro cells, femto cells or Home Node Bs (HNBs), pico cells, relays, or the like. 
     In accordance with one aspect, respective serving cells  1410  and auxiliary cells  1420  can cooperate to perform N-MIMO or CoMP communication with one or more user devices  1430 , thereby improving the overall throughput and performance of system  1400  as compared to a conventional wireless communication system in which a user device connects to a single cell (e.g., a closest and/or strongest cell). In one example, CoMP and/or other techniques can be utilized to facilitate cooperation between respective cells  1410 - 1420 , between respective sectors associated with one or more cells  1410 - 1420 , and/or any other suitable network entities. Such cooperation can be facilitated by, for example, a TX/RX coordination module  1412  associated with respective cells  1410 - 1420  and/or any other suitable mechanism(s). Further, TX/RX coordination module  1412  can facilitate cooperation between respective network entities according to any suitable network cooperation strategy(ies), such as fractional frequency reuse, silencing, coordinated beamforming, joint transmission, or the like. 
     In one example, coordinated beamforming can be conducted between network nodes associated with respective cells  1410 - 1420  by coordinating transmissions from the respective cells  1410 - 1420  such that if a transmission to a user device  1430  occurs from a given cell  1410  or  1420 , a beam is chosen to serve the user device  1430  by the given cell  1410  or  1420  such that the transmission to the user device  1430  is orthogonal or otherwise substantially mismatched to user devices scheduled on neighboring cells  1410  and/or  1420 . By doing so, it can be appreciated that beamforming gains can be realized for a desired user device  1430  while simultaneously reducing the effects of interference on neighboring network devices. In one example, coordinated beamforming can be facilitated by performing scheduling, beam selection, user selection (e.g., by selecting user devices  1430  having desirable beams that substantially limit interference at neighboring devices), or the like. 
     Additionally or alternatively, joint transmission can be conducted between a plurality of network nodes and a given user device  1430  by, for example, pooling resources designated for transmission to a given user device  1430  and transmitting the pooled resources via multiple distinct network nodes (e.g., nodes corresponding to a serving cell  1410  as well as an auxiliary cell  1420 ). In one example, resource pooling among network nodes corresponding to different cells  1410 - 1420  can be conducted via a backhaul link between the cells  1410 - 1420  and/or any other suitable mechanism. In another example, similar techniques can be utilized for uplink joint transmission, wherein a user device  1430  can be configured to transmit data, control signaling, and/or other appropriate information to multiple network nodes. For example, instead of a first cell transmitting a modulation symbol x to a first user and a second cell transmitting a modulation symbol y to a second user, the cells can cooperate such that the first cell transmits ax+by to one or both of the users and the second cell transmits cx+dy to the same user(s), where a, b, c, and d are coefficients chosen to optimize the signal-to-noise ratio (SNR) of the users, system capacity, and/or any other suitable metric(s). 
     In accordance with one aspect, various aspects of uplink and downlink CoMP communication can be based on feedback provided by respective user devices  1430 . For example, a N-MIMO feedback module  1432  at respective user devices  1430  can be utilized to provide feedback to various cells  1410 - 1420 , which in turn can utilize a user feedback processing module  1414  and/or other suitable means to utilize the feedback in conducting cooperative communication within system  1400 . By way of example, in the case of downlink CoMP communication, a N-MIMO feedback module  1432  at user device(s)  1430  can facilitate channel reporting to respective cells  1410 - 1420  of respective serving cells as well as one or more neighboring non-cooperative cells. By way of another example, in the case of uplink CoMP communication, N-MIMO feedback module  1432  can provide feedback information to respective cells  1410 - 1420  in combination with respectively scheduled uplink transmissions to the cells  1410 - 1420  that can be utilized by the cells  1410 - 1420  to facilitate the removal of interference from the corresponding uplink transmissions. 
     Turning to  FIG. 15 , an exemplary wireless communication system  1500  is illustrated. In one example, system  1500  can be configured to support a number of users, in which various disclosed embodiments and aspects can be implemented. As shown in  FIG. 15 , by way of example, system  1500  can provide communication for multiple cells  1502 , (e.g., macro cells  1502   a - 1502   g ), with respective cells being serviced by corresponding access points (AP)  1504  (e.g., APs  1504   a - 1504   g ). In one example, one or more cells can be further divided into respective sectors (not shown). 
     As  FIG. 15  further illustrates, various access terminals (ATs)  1506 , including ATs  1506   a - 1506   k,  can be dispersed throughout system  1500 . In one example, an AT  1506  can communicate with one or more APs  1504  on a forward link (FL) and/or a reverse link (RL) at a given moment, depending upon whether the AT is active and whether it is in soft handoff and/or another similar state. As used herein and generally in the art, an AT  1506  can also be referred to as a user equipment (UE), a mobile terminal, and/or any other suitable nomenclature. In accordance with one aspect, system  1500  can provide service over a substantially large geographic region. For example, macro cells  1502   a - 1502   g  can provide coverage for a plurality of blocks in a neighborhood and/or another similarly suitable coverage area. 
     Referring now to  FIG. 16 , a block diagram illustrating an example wireless communication system  1600  in which various aspects described herein can function is provided. In one example, system  1600  is a multiple-input multiple-output (MIMO) system that includes a transmitter system  1610  and a receiver system  1650 . It should be appreciated, however, that transmitter system  1610  and/or receiver system  1650  could also be applied to a multi-input single-output system wherein, for example, multiple transmit antennas (e.g., on a base station), can transmit one or more symbol streams to a single antenna device (e.g., a mobile station). Additionally, it should be appreciated that aspects of transmitter system  1610  and/or receiver system  1650  described herein could be utilized in connection with a single output to single input antenna system. 
     In accordance with one aspect, traffic data for a number of data streams are provided at transmitter system  1610  from a data source  1612  to a transmit (TX) data processor  1614 . In one example, each data stream can then be transmitted via a respective transmit antenna  1624 . Additionally, TX data processor  1614  can format, encode, and interleave traffic data for each data stream based on a particular coding scheme selected for each respective data stream in order to provide coded data. In one example, the coded data for each data stream can then be multiplexed with pilot data using OFDM techniques. The pilot data can be, for example, a known data pattern that is processed in a known manner. Further, the pilot data can be used at receiver system  1650  to estimate channel response. Back at transmitter system  1610 , the multiplexed pilot and coded data for each data stream can be modulated (i.e., symbol mapped) based on a particular modulation scheme (e.g., BPSK, QSPK, M-PSK, or M-QAM) selected for each respective data stream in order to provide modulation symbols. In one example, data rate, coding, and modulation for each data stream can be determined by instructions performed on and/or provided by processor  1630 . 
     Next, modulation symbols for all data streams can be provided to a TX processor  1620 , which can further process the modulation symbols (e.g., for OFDM). TX MIMO processor  1620  can then provides N T  modulation symbol streams to N T  transceivers  1622   a  through  1622   t.  In one example, each transceiver  1622  can receive and process a respective symbol stream to provide one or more analog signals. Each transceiver  1622  can then further condition (e.g., amplify, filter, and upconvert) the analog signals to provide a modulated signal suitable for transmission over a MIMO channel. Accordingly, N T  modulated signals from transceivers  1622   a  through  1622   t  can then be transmitted from N T  antennas  1624   a  through  1624   t,  respectively. 
     In accordance with another aspect, the transmitted modulated signals can be received at receiver system  1650  by N R  antennas  1652   a  through  1652   r.  The received signal from each antenna  1652  can then be provided to respective transceivers  1654 . In one example, each transceiver  1654  can condition (e.g., filter, amplify, and downconvert) a respective received signal, digitize the conditioned signal to provide samples, and then processes the samples to provide a corresponding “received” symbol stream. An RX MIMO/data processor  1660  can then receive and process the N R  received symbol streams from N R  transceivers  1654  based on a particular receiver processing technique to provide N T  “detected” symbol streams. In one example, each detected symbol stream can include symbols that are estimates of the modulation symbols transmitted for the corresponding data stream. RX processor  1660  can then process each symbol stream at least in part by demodulating, deinterleaving, and decoding each detected symbol stream to recover traffic data for a corresponding data stream. Thus, the processing by RX processor  1660  can be complementary to that performed by TX MIMO processor  1620  and TX data processor  1616  at transmitter system  1610 . RX processor  1660  can additionally provide processed symbol streams to a data sink  1664 . 
     In accordance with one aspect, the channel response estimate generated by RX processor  1660  can be used to perform space/time processing at the receiver, adjust power levels, change modulation rates or schemes, and/or other appropriate actions. Additionally, RX processor  1660  can further estimate channel characteristics such as, for example, signal-to-noise-and-interference ratios (SNRs) of the detected symbol streams. RX processor  1660  can then provide estimated channel characteristics to a processor  1670 . In one example, RX processor  1660  and/or processor  1670  can further derive an estimate of the “operating” SNR for the system. Processor  1670  can then provide channel state information (CSI), which can comprise information regarding the communication link and/or the received data stream. This information can include, for example, the operating SNR. The CSI can then be processed by a TX data processor  1618 , modulated by a modulator  1680 , conditioned by transceivers  1654   a  through  1654   r,  and transmitted back to transmitter system  1610 . In addition, a data source  1616  at receiver system  1650  can provide additional data to be processed by TX data processor  1618 . 
     Back at transmitter system  1610 , the modulated signals from receiver system  1650  can then be received by antennas  1624 , conditioned by transceivers  1622 , demodulated by a demodulator  1640 , and processed by a RX data processor  1642  to recover the CSI reported by receiver system  1650 . In one example, the reported CSI can then be provided to processor  1630  and used to determine data rates as well as coding and modulation schemes to be used for one or more data streams. The determined coding and modulation schemes can then be provided to transceivers  1622  for quantization and/or use in later transmissions to receiver system  1650 . Additionally and/or alternatively, the reported CSI can be used by processor  1630  to generate various controls for TX data processor  1614  and TX MIMO processor  1620 . In another example, CSI and/or other information processed by RX data processor  1642  can be provided to a data sink  1644 . 
     In one example, processor  1630  at transmitter system  1610  and processor  1670  at receiver system  1650  direct operation at their respective systems. Additionally, memory  1632  at transmitter system  1610  and memory  1672  at receiver system  1650  can provide storage for program codes and data used by processors  1630  and  1670 , respectively. Further, at receiver system  1650 , various processing techniques can be used to process the N R  received signals to detect the N T  transmitted symbol streams. These receiver processing techniques can include spatial and space-time receiver processing techniques, which can also be referred to as equalization techniques, and/or “successive nulling/equalization and interference cancellation” receiver processing techniques, which can also be referred to as “successive interference cancellation” or “successive cancellation” receiver processing techniques. 
       FIG. 17  illustrates an example communication system  1700  that enables deployment of access point base stations within a network environment. As shown in  FIG. 17 , system  1700  can include multiple access point base stations (e.g., femto cells or Home Node B units (HNBs)) such as, for example, HNBs  1710 . In one example, respective HNBs  1710  can be installed in a corresponding small scale network environment, such as, for example, one or more user residences  1730 . Further, respective HNBs  1710  can be configured to serve associated and/or alien UE(s)  1720 . In accordance with one aspect, respective HNBs  1710  can be coupled to the Internet  1740  and a mobile operator core network  1750  via a DSL router, a cable modem, and/or another suitable device (not shown). In accordance with one aspect, an owner of a femto cell or HNB  1710  can subscribe to mobile service, such as, for example, 3G/4G mobile service, offered through mobile operator core network  1750 . Accordingly, UE  1720  can be enabled to operate both in a macro cellular environment  1760  and in a residential small scale network environment. 
     In one example, UE  1720  can be served by a set of Femto cells or HNBs  1710  (e.g., HNBs  1710  that reside within a corresponding user residence  1730 ) in addition to a macro cell mobile network  1760 . As used herein and generally in the art, a home femto cell is a base station on which an AT or UE is authorized to operate on, a guest femto cell refers to a base station on which an AT or UE is temporarily authorized to operate on, and an alien femto cell is a base station on which the AT or UE is not authorized to operate on. In accordance with one aspect, a femto cell or HNB  1710  can be deployed on a single frequency or on multiple frequencies, which may overlap with respective macro cell frequencies. 
     It is to be understood that the aspects described herein can be implemented by hardware, software, firmware, middleware, microcode, or any combination thereof. When the systems and/or methods are implemented in software, firmware, middleware or microcode, program code or code segments, they can be stored in a machine-readable medium, such as a storage component. A code segment can represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment can be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. can be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, etc. 
     For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art. 
     What has been described above includes examples of one or more aspects. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned aspects, but one of ordinary skill in the art can recognize that many further combinations and permutations of various aspects are possible. Accordingly, the described aspects are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. Furthermore, the term “or” as used in either the detailed description or the claims is meant to be a “non-exclusive or.”