Downlink spatial intercell interference cancellation (ICIC) is considered for mitigating other-cell interference using multiple transmit antennas. A principle question we explore is whether it is better to do ICIC or simply standard single-cell beamforming. We explore this question analytically and show that beamforming is preferred for all users when the edge SNR (signal-to-noise ratio) is low ( @math dB), for example in an urban setting. At medium SNR, a proposed adaptive strategy, where multiple base stations jointly select transmission strategies based on the user location, outperforms both while requiring a lower feedback rate than the pure ICIC approach. The employed metric is sum rate, which is normally a dubious metric for cellular systems, but surprisingly we show that even with this reward function the adaptive strategy also improves fairness. When the channel information is provided by limited feedback, the impact of the induced quantization error is also investigated. It is shown that ICIC with well-designed feedback strategies still provides significant throughput gain.
In practice, the major challenges for network MIMO concern complexity and overhead. For example, the requirement for CSI grows in proportion to the number of BS antennas, the number of BSs, and the number of users. The complexity of joint processing also grows with the network size. To limit the complexity and CSI requirements, cluster-based coordination is one approach @cite_26 @cite_52 @cite_12 @cite_42 . To reduce the complexity, distributed decoding and beamforming for network MIMO systems were proposed in @cite_36 @cite_46 @cite_0 . In @cite_17 @cite_51 , BS coordination with hybrid channel knowledge was investigated, where each BS has full information of its own CSI and statistical information of other BSs' channels. Limited backhaul capacity @cite_47 @cite_5 and synchronization @cite_37 @cite_32 have also been treated to some extent. A WiMAX based implementation of network MIMO was done in @cite_9 , for both uplink and downlink in the indoor environment.
@cite_47 @cite_26 @cite_37 @cite_36 @cite_9 @cite_42 @cite_52 @cite_32 @cite_0 @cite_5 @cite_46 @cite_51 @cite_12 @cite_17
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It has recently been shown that multi-cell cooperations in cellular networks, enabling distributed antenna systems and joint transmission or joint detection across cell boundaries, can significantly increase capacity, especially that of users at cell borders. Such concepts, typically implicitly assuming unlimited information exchange between base stations, can also be used to increase the network fairness. In practical implementations, however, the large amounts of received signals that need to be quantized and transmitted via an additional backhaul between the involved cells to central processing points, will be a non-negligible issue. In this paper, we thus introduce an analytical framework to observe the uplink performance of cellular networks in which joint detection is only applied to a subset of selected users, aiming at achieving best possible capacity and fairness improvements under a strongly constrained backhaul between sites. This reveals a multi-dimensional optimization problem, where we propose a simple, heuristic algorithm that strongly narrows down and serializes the problem while still yielding a significant performance improvement. Cooperative transmission by base stations (BSs) can significantly improve the spectral efficiency of multiuser, multi-cell, multiple input multiple output (MIMO) systems. We show that contrary to what is often assumed in the literature, the multiuser interference in such systems is fundamentally asynchronous. Intuitively, perfect timing-advance mechanisms can at best only ensure that the desired signal components -but not also the interference components -are perfectly aligned at their intended mobile stations. We develop an accurate mathematical model for the asynchronicity, and show that it leads to a significant performance degradation of existing designs that ignore the asynchronicity of interference. Using three previously proposed linear preceding design methods for BS cooperation, we develop corresponding algorithms that are better at mitigating the impact of the asynchronicity of the interference. Furthermore, we also address timing-advance inaccuracies (jitter), which are inevitable in a practical system. We show that using jitter-statistics-aware precoders can mitigate the impact of these inaccuracies as well. The insights of this paper are critical for the practical implementation of BS cooperation in multiuser MIMO systems, a topic that is typically oversimplified in the literature. This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local message passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single-user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to have full frequency re-use and achieve near-optimal performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells. It is well known that multiple-input multiple-output (MIMO) techniques can bring numerous benefits, such as higher spectral efficiency, to point-to-point wireless links. More recently, there has been interest in extending MIMO concepts to multiuser wireless systems. Our focus in this paper is on network MIMO, a family of techniques whereby each end user in a wireless access network is served through several access points within its range of influence. By tightly coordinating the transmission and reception of signals at multiple access points, network MIMO can transcend the limits on spectral efficiency imposed by cochannel interference. Taking prior information-theoretic analyses of network MIMO to the next level, we quantify the spectral efficiency gains obtainable under realistic propagation and operational conditions in a typical indoor deployment. Our study relies on detailed simulations and, for specificity, is conducted largely within the physical-layer framework of the IEEE 802.16e Mobile WiMAX system. Furthermore, to facilitate the coordination between access points, we assume that a high-capacity local area network, such as Gigabit Ethernet, connects all the access points. Our results confirm that network MIMO stands to provide a multiple-fold increase in spectral efficiency under these conditions. A clustered base transceiver station (BTS) coordination strategy is proposed for a large cellular MIMO network, which includes full intra-cluster coordination-to enhance the sum rate-and limited inter-cluster coordination-to reduce interference for the cluster edge users. Multi-cell block diagonalization is used to coordinate the transmissions across multiple BTSs in the same cluster. To satisfy per-BTS power constraints, three combined precoder and power allocation algorithms are proposed with different performance and complexity tradeoffs. For inter-cluster coordination, the coordination area is chosen to balance fairness for edge users and the achievable sum rate. It is shown that a small cluster size (about 7 cells) is sufficient to obtain most of the sum rate benefits from clustered coordination while greatly relieving channel feedback requirement. Simulations show that the proposed coordination strategy efficiently reduces interference and provides a considerable sum rate gain for cellular MIMO networks. We investigate the downlink throughput of cellular systems where groups of M antennas - either co-located or spatially distributed - transmit to a subset of a total population of K > M users in a coherent, coordinated fashion in order to mitigate intercell interference. We consider two types of coordination: the capacity-achieving technique based on dirty paper coding (DPC), and a simpler technique based on zero-forcing (ZF) beamforming with per-antenna power constraints. During a given frame, a scheduler chooses the subset of the K users in order to maximize the weighted sum rate, where the weights are based on the proportional-fair scheduling algorithm. We consider the weighted average sum throughput among K users per cell in a multi-cell network where coordination is limited to a neighborhood of M antennas. Consequently, the performance of both systems is limited by interference from antennas that are outside of the M coordinated antennas. Compared to a 12-sector baseline which uses the same number of antennas per cell site, the throughput of ZF and DPC achieve respective gains of 1.5 and 1.75. We consider synchronization techniques required to enhance the cellular network capacity using base station cooperation. In the physical layer, local oscillators are disciplined by the global positioning system (GPS) and over the backbone network for outdoor and indoor base stations, respectively. In the medium access control (MAC) layer, the data flow can be synchronized by two approaches. The first approach uses so-called time stamps. The data flow through the user plane and through copies of it in each cooperative base station is synchronized using a timing protocol on the interconnects between the base stations. The second approach adds mapping information to the data after the user plane processing is almost finalized. Each forward-error encoded transport block, its modulation and coding scheme and the resources where it will be transmitted are multicast over the interconnect network. Interconnect latency is reduced below 1 ms to enable coherent interference reduction for mobile radio channels. In this paper, we consider multicell processing on the downlink of a cellular network to accomplish ldquomacrodiversityrdquo transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beamforming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-backward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs. In this contribution we present new achievable rates, for the non-fading uplink channel of a cellular network, with joint cell-site processing, where unlike previous results, the error-free backhaul network has finite capacity per-cell. Namely, the cell-sites are linked to the central joint processor via lossless links with finite capacity. The cellular network is modeled by the circular Wyner model, which yields closed form expressions for the achievable rates. For this idealistic model, we present achievable rates for cell-sites that use compress-and forward scheme, combined with local decoding, and inter-cell time-sharing. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, already for modest backhaul capacities, supporting the potential gain offered by the joint cell-site processing approach. We consider the problem of multiuser detection in cellular networks. In particular, we present a distributed forward-backward algorithm with local message passing for efficient implementation of the linear minimum mean square error (LMMSE) receiver, for a simple model of a 1D cellular system. The distributed algorithm is based on the well-known interpretation of Kalman smoothing as a linear combination of the forward and backward filtered estimates. We also show that near-optimal performance can be achieved by only relying on information from a local linear segment of the entire array. This results in a limited extent distributed algorithm that greatly reduces processing delay, especially for large networks, yet with little loss in performance. Multi-cell cooperative processing (MCP) has recently attracted a lot of attention because of its potential for co-channel interference (CCI) mitigation and spectral efficiency increase. MCP inevitably requires increased signaling overhead and inter-base communication. Therefore in practice, only a limited number of base stations (BSs) can cooperate in order for the overhead to be affordable. The intrinsic problem of which BSs shall cooperate in a realistic scenario has been only partially investigated. In this contribution linear beamforming has been considered for the sum-rate maximisation of the uplink. A novel dynamic greedy algorithm for the formation of the clusters of cooperating BSs is presented for a cellular network incorporating MCP. This approach is chosen to be evaluated under a fair MS scheduling scenario (round-robin). The objective of the clustering algorithm is sum-rate maximisation of the already selected MSs. The proposed cooperation scheme is compared with some fixed cooperation clustering schemes. It is shown that a dynamic clustering approach with a cluster consisting of 2 cells outperforms static coordination schemes with much larger cluster sizes. This paper explores the idea of cooperative spatial multiplexing for use in MIMO multicell networks. We imagine applying this cooperation for several multiple antenna access-points to jointly transmit streams towards multiple single-antenna user terminals in neighbouring cells. We make the setting more realistic by introducing a constraint on the hybrid channel state information (HCSI), assuming that each transmitter has full CSI for its own channel, but only statistical information about other transmitters’ channels. Each cooperating transmitter then makes guesses about the behaviour of the other transmitters, using the statistical CSI. We show two of several possible transmission strategies under this setting, and include simple optimization at the receiver to improve performance. Comparisons are made with fully cooperative (full CSI) and non-cooperative schemes. Simulation results show a substantial cooperation gain despite the lack of instantaneous information.