Binomial distribution based grey wolf optimization algorithm for channel estimation in wireless communication system

Several input high-data-rate transmissions over broadband wireless channels are possible using multiple input multiple output (MIMO) systems paired with orthogonal frequency division multiplexing (OFDM) technology. Channel estimation is an essential technique and a necessary component of MIMO-OFDM systems. However, the noise will be there in MIMO-OFDM due to the environment. As a result, the wireless system performs degrades in terms of bit error rate (BER). The suggested method offers a better pilot pattern strategy for MIMO-OFDM and an efficient power allocation to address this issue. The binomial distribution-based grey wolf optimization (BDGWO) algorithm is proposed to identify the optimal pilot patterns. The power is then adaptively distributed to each transmit antenna to increase the spectral efficiency and maximum channel capacity through an adaptive neuro-fuzzy inference system with a sigmoid membership function (SMFANFIS). The best pilot patterns in PDGSIP (pilot design with generalized shift invariant property) were determined using the BDGWO algorithm based on the binomial distribution. According to the simulation results, the proposed BDGWO established pilot design with generalized shift invariant property (BDGWO-DGSIP) achieves higher performance compared other existing approaches such as PDGSIP, TPDGSIP, and LS in terms of NMSE, BER, and SER. Compared to the PDGSIP technique, the proposed PDGSIP-BDGWO system minimizes NMSE at 10%, BER at about 12%, and SER at 15%.