Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO sy...Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.展开更多
Multi-cell processing （MCP） is capable of providing significant performance gain, but this improvement is accompanied by dramatic signaling overhead between cooperative base stations. Therefore, balancing the perfor...Multi-cell processing （MCP） is capable of providing significant performance gain, but this improvement is accompanied by dramatic signaling overhead between cooperative base stations. Therefore, balancing the performance gain and overhead growth is crucial for a practical multi-base cooperation scheme. In this paper, we propose a decentralized algorithm to jointly optimize the power allocation and beamforming vector with the goal of maximizing the system performance under the constraint of limited overhead signal and backhaul link capacity. In particular, combined with calculating the transmission beamforming vector according to the local channel state information, an adaptive power allocation is presented based on the result of sum capacity estimation. Furthermore, by utilizing the concept of cell clustering, the proposed framework can be implemented in a practical cellular system without major modification of network architecture. Simulation results demonstrate that the proposed scheme improves the system performance in terms of the sum capacity and cell-edge capacity.展开更多
基金supported in part by National Natural Science Foundation of China No.61171080
文摘Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.
基金supported by the National Basic Research Program of China (2009CB320401)the National Natural Science Foundation of China (61171099)and the National Science and Technology Major Project of China (2012ZX03003-007)
文摘Multi-cell processing （MCP） is capable of providing significant performance gain, but this improvement is accompanied by dramatic signaling overhead between cooperative base stations. Therefore, balancing the performance gain and overhead growth is crucial for a practical multi-base cooperation scheme. In this paper, we propose a decentralized algorithm to jointly optimize the power allocation and beamforming vector with the goal of maximizing the system performance under the constraint of limited overhead signal and backhaul link capacity. In particular, combined with calculating the transmission beamforming vector according to the local channel state information, an adaptive power allocation is presented based on the result of sum capacity estimation. Furthermore, by utilizing the concept of cell clustering, the proposed framework can be implemented in a practical cellular system without major modification of network architecture. Simulation results demonstrate that the proposed scheme improves the system performance in terms of the sum capacity and cell-edge capacity.
