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首页> 外文期刊>IEEE Transactions on Signal Processing >Multimode Transmission for Multiuser MIMO Systems With Block Diagonalization
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Multimode Transmission for Multiuser MIMO Systems With Block Diagonalization

机译:具有块对角化的多用户MIMO系统的多模传输

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摘要

A low-complexity multimode transmission technique for downlink multiuser multiple-input–multiple-output (MIMO) systems with block diagonalization (BD) is proposed. The proposed technique adaptively configures the number of data streams for each user by adjusting its number of active receive antenna and switching between single-stream beamforming and multistream spatial multiplexing, as a means to exploit the multimode switching diversity. We consider a highly loaded system where there are a large number of users, hence a subset of users need to be selected. Joint user and antenna selection has been proposed as a multiuser multimode switching technique, where the optimal subset of receive antennas and users are chosen to maximize the sum throughput. The brute-force search, however, is prohibitively complicated. In this paper, two low-complexity near-optimal user/antenna selection algorithms are developed. The first algorithm aims at maximizing a capacity lower bound, derived in terms of the sum Frobenius norm of the channel, while the second algorithm greedily maximizes the sum capacity. We analytically evaluate the complexity of the proposed algorithms and show that it is orders of magnitude lower than that of the exhaustive search. Simulation results demonstrate that the proposed algorithms achieve up to 98% of the sum throughput of the exhaustive search, for most system configurations, while the complexity is substantially reduced.
机译:提出了一种具有块对角化(BD)的下行多用户多输入多输出(MIMO)系统的低复杂度多模传输技术。提出的技术通过调整每个用户的活动接收天线的数量并在单流波束成形和多流空间复用之间进行切换来自适应地配置每个用户的数据流数量,以此作为一种利用多模切换分集的方法。我们考虑有大量用户的高负载系统,因此需要选择一部分用户。已经提出了联合用户和天线选择作为多用户多模式切换技术,其中选择接收天线和用户的最佳子集以使总吞吐量最大化。但是,蛮力搜索极其复杂。本文提出了两种低复杂度的接近最优的用户/天线选择算法。第一种算法旨在最大化根据信道的Frobenius范数和得出的容量下限,而第二种算法则贪婪地最大化总容量。我们分析性地评估了所提出算法的复杂度,并表明它比穷举搜索要低几个数量级。仿真结果表明,对于大多数系统配置而言,所提出的算法可实现穷举搜索总吞吐量的98%,同时大大降低了复杂度。

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