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Joint Optimization of Source Power Allocation and Distributed Relay Beamforming in Multiuser Peer-to-Peer Relay Networks

机译:多用户对等中继网络中源功率分配和分布式中继波束成形的联合优化

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In this paper, we consider the joint optimization of the source power allocation and relay beamforming weights in distributed multiuser peer-to-peer (MUP2P) relay networks applying the amplify-and-forward (AF) protocol. We adopt a quality-of-service (QoS) based approach, in which the total power transmitted from all sources and relays is minimized while guaranteeing the prescribed QoS requirement of each source-destination pair. The QoS is modeled as a function of the receive signal-to-interference-plus-noise ratio (SINR) at the destinations. Unlike the existing contributions, the transmitted powers of the sources and the beamforming weights of the relays are optimized jointly in this paper. Introducing an appropriate transformation of variables, the QoS based source power allocation and distributed relay beamforming (PADB) problem can be equivalently transformed into a difference of convex (DC) program, which can be efficiently solved with local optimality using the constrained concave convex procedure (CCCP). Based on this procedure, we also propose an iterative feasibility search algorithm (IFSA) to find an initial feasible point of the DC program. The analytic study of the proposed solution confirms that it converges to a local optimum of the PADB problem. Numerical results show that our solution outperforms (in terms of the total transmitted power) the alternating optimization procedure and the exact penalty based DC algorithm. In addition, the proposed IFSA outperforms the alternating optimization algorithm in finding feasible points of the DC program (i.e., the equivalence of the PADB problem).
机译:在本文中,我们考虑了采用放大转发(AF)协议的分布式多用户对等(MUP2P)中继网络中源功率分配和中继波束成形权重的联合优化。我们采用基于服务质量(QoS)的方法,在保证每个源-目的地对规定的QoS要求的同时,将从所有源和中继传输的总功率最小化。 QoS被建模为目的地的接收信号干扰加噪声比(SINR)的函数。与现有的贡献不同,本文共同优化了源的发射功率和继电器的波束成形权重。引入适当的变量转换后,基于QoS的源功率分配和分布式中继波束成形(PADB)问题可以等效地转换为凸差(DC)程序,这可以通过使用约束凹凸程序来局部最优地解决( CCCP)。基于此过程,我们还提出了一种迭代可行性搜索算法(IFSA),以找到DC程序的初始可行点。对提出的解决方案的分析研究证实,它收敛于PADB问题的局部最优。数值结果表明,我们的解决方案优于交替优化程序和基于精确惩罚的DC算法(在总发射功率方面)。此外,在查找DC程序的可行点(即PADB问题的等价性)方面,拟议的IFSA优于替代优化算法。

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