Considering the non-convexity of hybrid precoding and the hardware constraints of practical systems, a hybrid precoding architecture, which combines limited-resolution overlapped phase shifter networks with lens array, is investigated. The analogy part is a beam selection network composed of overlapped low-resolution phase shifter networks. In particular, in the proposed hybrid precoding algorithm, the analog precoding improves array gain by utilizing the quantization beam alignment method, whereas the digital precoding schemes multiplexing gain by adopting a Wiener Filter precoding scheme with a minimum mean square error criterion. Finally, in the sparse scattering millimeter-wave channel for the uniform linear array, the proposed method is compared with the existing scheme by computer simulation by using the ideal channel state information and the non-ideal channel state information. It is concluded that the proposed scheme performs better in low signal-to-noise regions and can achieve a good compromise between system performance and hardware complexity.

Wingsuit flying search is a meta-heuristic algorithm that effectively searches for optimal solutions by narrowing down the search space iteratively. However, its performance is affected by the balance between exploration and exploitation. We propose a four-layered hierarchical population structure algorithm, multi-layered chaotic wingsuit flying search (MCWFS), to promote such balance in this paper. The proposed algorithm consists of memory, elite, sub-elite, and population layers. Communication between the memory and elite layers enhances exploration ability while maintaining population diversity. The information flow from the population layer to the elite layer ensures effective exploitation. We evaluate the performance of the proposed MCWFS algorithm by conducting comparative experiments on IEEE Congress on Evolutionary Computation (CEC) benchmark functions. Experimental results prove that MCWFS is superior to the original algorithm in terms of solution quality and search performance. Compared with other representative algorithms, MCWFS obtains more competitive results on composite problems and real-world problems.