Multi-cell cooperative processing with limited backhaul traffic is studied for cellular uplinks. Aiming at reduced backhaul overhead, a sparsity-regularized multi-cell receive-filter design problem is formulated. Both unstructured distributed cooperation as well as clustered cooperation, in which base station groups are formed for tight cooperation, are considered. Dynamic clustered cooperation, where the sparse equalizer and the cooperation clusters are jointly determined, is solved via alternating minimization based on spectral clustering and group-sparse regression. Furthermore, decentralized implementations of both unstructured and clustered cooperation schemes are developed for scalability, robustness and computational efficiency. Extensive numerical tests verify the efficacy of the proposed methods.
This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. We propose a simple scheme that performs Wyner-Ziv compress-and-forward relaying on a per-base-station basis followed by successive interference cancellation (SIC) at the central processor. The proposed scheme has a significantly reduced complexity as compared to joint decoding, resulting in an easily computable achievable rate region. Although suboptimal in general, this paper shows that the proposed per-base-station SIC scheme can achieve the sum capacity of a class of Wyner cellular model to within a constant gap. This paper also establishes that in order to achieve to within a constant gap to the maximum SIC rate with infinite backhaul, the limited-backhaul system must have backhaul capacities that scale logarithmically with the signal-to-interference-and-noise ratios (SINRs) at the base-stations. Further, this paper studies the optimal backhaul rate allocation problem for the per-base-station SIC model with a total backhaul capacity constraint, and shows that the sum-rate maximizing allocation should also have individual backhaul rates that scale logarithmically with the SINR at each base-station. Finally, the proposed per-base-station SIC scheme is evaluated in a practical multicell network to quantify the performance gain brought by multicell processing. 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. Cellular systems in general suffer from co-channel interference, when simultaneous transmissions in other cells use the same physical resources. In order to mitigate such co-channel interference cooperating Base Stations (BSs) can perform joint multi-antenna signal processing across cell borders. This paper describes a concept of distributed cooperation, where BSs communicate directly via a BS-BS interface without central control. A serving BS can serve its terminals on its own or it can request cooperation from one or more supporting BSs. By collecting IQ samples from the supporting BSs' antenna elements, the serving BS can virtually increase its number of receive antennas. Exchanging additional parameters allows applying advanced receiver algorithms, e.g., interference rejection or cancelation. Performance evaluations by means of simulation show the capability of BS cooperation applied to 3GPP LTE in terms of cell and user throughput but it also shows the trade-off in terms of increased backhaul requirement due to BS-BS communication. We consider transmit rate allocation and the associated cooperative decoding strategies in uplink multi-cell networks where base stations (BSs) are connected by capacity-limited backhaul (BH) links. In particular, we propose two cooperative group decoding (CGD) schemes for the BSs with partial decoding results shared via the BH. In parallel CGD, at each stage, each BS locally selects and jointly decodes a group of users by treating the remaining users as noise; then it forwards some partial decoding results to other BSs via the BH. After subtracting the decoded users and the received decoding results from other BSs, each BS repeats the same procedure until all its designated users are decoded. On the other hand, in sequential CGD, each user can be decoded at any BS. At each stage only one BS is selected to decode a group of users, which then forwards the decoding results to other BSs for interference cancellation. The process is repeated until all users are decoded. Numerical results are provided to demonstrate that the proposed CGD schemes offer significant gain in terms of the achievable rates. 