基于优化的ADMM稀疏阵列波束图综合
Sparse Array Beampattern Synthesis via Majorization-Based ADMM
摘要：
波束图综合是许多无线应用中的关键问题。随着MIMO天线阵列规模的不断扩大，迫切需要在稀疏阵列上进行波束方向图综合，以降低功耗和硬件成本。在本文中，我们考虑进行波束方向图综合和稀疏阵列的建设。在公式化的问题中，波束方向图的合成设计通过最小化匹配误差的波束方向图模板，和Shannon熵函数的第一次被引入到施加的阵列的稀疏性。然后，对于这个非凸问题，利用交替方向乘子法（ADMM）和优化最小化（MM）提出了一种迭代方法。仿真结果表明，与基准算法相比，该算法在较短的运行时间内实现了阵列稀疏性和波束图匹配误差之间的良好折衷。
英文摘要：
Beampattern synthesis is a key problem in many wireless applications. With the increasing scale of MIMO antenna array, it is highly desired to conduct beampattern synthesis on a sparse array to reduce the power and hardware cost. In this paper, we consider conducting beampattern synthesis and sparse array construction jointly. In the formulated problem, the beampattern synthesis is designed by minimizing the matching error to the beampattern template, and the Shannon entropy function is first introduced to impose the sparsity of the array. Then, for this nonconvex problem, an iterative method is proposed by leveraging on the alternating direction multiplier method (ADMM) and the majorization minimization (MM). Simulation results demonstrate that, compared with the benchmark, our approach achieves a good trade-off between array sparsity and beampattern matching error with less runtime.
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