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.
Direct exchanges of the signal samples between the BSs were considered in the context of 3GPP LTE systems in @cite_42 , where distributed cooperation without central control was advocated. Overlapping clusters of BSs were elected based on proximity for uplink MCP in @cite_5 . A greedy algorithm for dynamic clustering was proposed to maximize the uplink sum-rate in @cite_8 . Successive interference cancellation was adopted under limited backhaul traffic in @cite_26 , and cooperative group decoding was considered in a similar setting in @cite_0 . Here, our intention is to concentrate on simple linear processing but address the backhaul traffic volume issue in both distributed and clustered cooperation settings in a consistent framework, and also derive distributed algorithms for scalable implementation.
@cite_26 @cite_8 @cite_42 @cite_0 @cite_5
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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. 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. We consider the interference management problem in a multicell MIMO heterogeneous network. Within each cell there is a large number of distributed micro pico base stations (BSs) that can be potentially coordinated for joint transmission. To reduce coordination overhead, we consider user-centric BS clustering so that each user is served by only a small number of (potentially overlapping) BSs. Thus, given the channel state information, our objective is to jointly design the BS clustering and the linear beamformers for all BSs in the network. In this paper, we formulate this problem from a sparse optimization perspective, and propose an efficient algorithm that is based on iteratively solving a sequence of group LASSO problems. A novel feature of the proposed algorithm is that it performs BS clustering and beamformer design jointly rather than separately as is done in the existing approaches for partial coordinated transmission. Moreover, the cluster size can be controlled by adjusting a single penalty parameter in the nonsmooth regularized utility function. The convergence of the proposed algorithm (to a stationary solution) is guaranteed, and its effectiveness is demonstrated via extensive simulation. When the joint processing technique is applied in the coordinated multipoint (CoMP) downlink transmission, the user data for each mobile station needs to be shared among multiple base stations (BSs) via backhaul. If the number of users is large, this data exchange can lead to a huge backhaul signaling overhead. In this paper, we consider a multi-cell CoMP network with multi-antenna BSs and single antenna users. The problem that involves the joint design of transmit beamformers and user data allocation at BSs to minimize the backhaul user data transfer is addressed, which is subject to given quality-of-service and per-BS power constraints. We show that this problem can be cast into an l0-norm minimization problem, which is NP-hard. Inspired by recent results in compressive sensing, we propose two algorithms to tackle it. The first algorithm is based on reweighted l1-norm minimization, which solves a series of convex l0-norm minimization problems. In the second algorithm, we first solve the l2-norm relaxation of the joint clustering and beamforming problem and then iteratively remove the links that correspond to the smallest transmit power. The second algorithm enjoys a faster solution speed and can also be implemented in a semi-distributed manner under certain assumptions. Simulations show that both algorithms can significantly reduce the user data transfer in the backhaul.