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首页> 外文会议>2018 International Conference on Engineering Technology and their Applications >A Practical Weighted Sum Rate Maximisation for Multi-stream Cellular MIMO Systems
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A Practical Weighted Sum Rate Maximisation for Multi-stream Cellular MIMO Systems

机译：多流蜂窝MIMO系统的实用加权求和速率最大化

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摘要

The problem of weighted sum-rate maximisation in a multi-cell multi-user downlink system is NP-hard and complicated to solve even for Multiple Input Single Output scheme (MISO). In this work, a low complexity suboptimal distributed algorithm is proposed for joint optimisation of beamforming and power allocation. The algorithm is based on two independent phases. In the first one, Regularised Block-Diagonalisation beam-forming is proposed (multi-stream transmission). In the second phase an approximated Dual decomposition and sub-gradient methods are used. Where the objective problem transformed into a sequence of successive convex approximations. Then, using Broadcast/Multiple access channel duality (BC/MAC), each base station computes optimal values of its local variables (virtual uplink power and beamforming) for fixed virtual noise levels (complicating variable). The Alternating-Direction Method of Multipliers (ADMM) is used for updating the virtual noise vectors once the per base station optimisations are finished. Base station optimisations and the sub-gradient method are carried out in an iterative approach for a fixed number of iterations. Finally, power can be allocated by leveraging optimal virtual uplink Signal-to-Interference plus Noise Ratio (SINR). Numerical results prove that the process can converge to a steady-state with in limited number of iterations and the network sum rate is closed to (only 1bpsk/Hz difference at 15dB SINR) the centralised scheme performance.
机译：在多小区多用户下行链路系统中，加权求和速率最大化的问题是NP难题，即使对于多输入单输出方案（MISO）也难以解决。在这项工作中，提出了一种用于波束成形和功率分配联合优化的低复杂度次优分布式算法。该算法基于两个独立的阶段。在第一个中，提出了规则块对角化波束成形（多流传输）。在第二阶段，使用了近似的双重分解和次梯度方法。目标问题转化为一系列连续凸逼近的序列。然后，使用广播/多路访问信道对偶（BC / MAC），每个基站针对固定的虚拟噪声水平（复杂变量）计算其局部变量（虚拟上行链路功率和波束成形）的最佳值。一旦每个基站的优化完成，乘法器的交替方向方法（ADMM）用于更新虚拟噪声矢量。基站优化和次梯度方法以迭代方式执行，用于固定数量的迭代。最后，可以通过利用最佳虚拟上行链路信号干扰加噪声比（SINR）来分配功率。数值结果证明，该过程可以在有限的迭代次数下收敛到稳态，并且网络总速率接近集中方案的性能（在15dB SINR时只有1bpsk / Hz的差异）。

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