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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks
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Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks

机译：多用户和多通道底层MISO认知无线电网络中的联合波束成形，功率和信道分配

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摘要

In this paper, we consider joint beamforming, power, and channel allocation in a multiuser and multichannel underlay multiple-input–single-output (MISO) cognitive radio network (CRN). In this system, the primary users' spectrum can be reused by secondary-user transmitters (SU-TXs) to maximize spectrum utilization, whereas intrauser interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a nonconvex mixed-integer nonlinear programming problem, we propose a solution that consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and a semidefinite relaxation approach. In the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that the beamforming and power and channel allocation with SA algorithm can achieve a close-to-optimal sum rate while having lower computational complexity compared with the beamforming and power and channel allocation with the GA algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum rate compared with the existing zero-forcing beamforming.
机译：在本文中，我们考虑了在多用户和多通道底层多输入单输出（MISO）认知无线电网络（CRN）中的联合波束成形，功率和信道分配。在该系统中，次要用户发射机（SU-TX）可以重用主要用户的频谱，以最大程度地利用频谱，而通过在每个SU-TX上进行波束成形，可以最大程度地减少用户内部干扰。将联合优化问题表述为非凸混合整数非线性规划问题后，我们提出了一个由两个阶段组成的解决方案。在第一阶段，通过将原始问题转换为具有引入的最佳辅助变量和半确定松弛方法的凸形，在给定的信道分配下得出功率分配和波束成形矢量的可行解决方案。在第二阶段，提出了两种显式搜索算法，即遗传算法（GA）和基于模拟退火（SA）的算法，以确定次优的信道分配。仿真结果表明，与遗传算法的波束赋形，功率和信道分配相比，SA算法的波束赋形和功率和信道分配可以达到接近最佳的总和率，同时具有较低的计算复杂度。此外，与现有的零强迫波束成形相比，我们提出的分配方案在可实现的总速率上有显着改善。

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