Pilot allocation optimization using enhanced salp swarm algorithm for sparse channel estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing. However, because of the influence of the number of subcarriers and pilots, the complexity of the enumeration method is computationally impractical. The meta-heuristic algorithm of the salp swarm algorithm (SSA) is employed to address this issue. Like most meta-heuristic algorithms, the SSA algorithm is prone to problems such as local optimal values and slow convergence. In this paper, we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy, adaptive weight factor, and increasing local search. Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms. Besides, the CWSSA algorithm is applied to pilot pattern optimization, and its results are better than other methods in terms of BER and MSE.