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首页> 外文期刊>IEEE Transactions on Signal Processing >Sum Rate Maximization for Non-Regenerative MIMO Relay Networks
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Sum Rate Maximization for Non-Regenerative MIMO Relay Networks

机译:非再生MIMO中继网络的总速率最大化

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A multiple-antenna amplify-and-forward (AF) two-hop interference network with multiple links and multiple relays is considered. In this paper, we optimize the transmit precoders, the receiver decoders, and the relay AF matrices to maximize the achievable sum rate. First, the total signal to total interference plus noise ratio (TSTINR) maximization approach is proposed to approximate the sum rate maximization problem as a lower bound. Under individual user and individual relay transmit power constraints, an efficient alternating direction algorithm is proposed to maximize the TSTINR. Then, we modify our TSTINR model as well as the algorithm to guarantee multiple data stream transmission, by requiring the precoding matrices to have a certain number of orthogonal columns. We propose the stream selection for preprocessing, and prove that the stream selection problem to maximize the sum rate is NP-hard. Simulation results show that our proposed stream selection TSTINR model achieves much higher sum rate compared to the existing model with the same computational cost; the proposed algorithm solves the problems efficiently, and the computation time is significantly reduced.
机译:考虑具有多个链路和多个中继的多天线放大转发(AF)两跳干扰网络。在本文中,我们优化了发送预编码器,接收器解码器和中继AF矩阵,以使可实现的总和率最大化。首先,提出了总信号与总干扰加噪声比(TSTINR)最大化的方法,以将和率最大化问题近似为下限。在个体用户和个体中继发射功率约束下,提出了一种有效的交变方向算法来最大化TSTINR。然后,通过要求预编码矩阵具有一定数量的正交列,我们修改TSTINR模型以及保证多数据流传输的算法。我们提出了用于预处理的流选择,并证明最大化总和率的流选择问题是NP难的。仿真结果表明,与现有模型相比,本文提出的流选择TSTINR模型具有更高的求和率,且计算量相同。该算法有效解决了问题,大大减少了计算时间。

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