Self-improved grey wolf optimization for estimating carrier frequency offset in SCM-OFDM systems

Abstract

PurposeThe purpose of this paper is to demonstrate a proficiency for accomplishing optimal CFO and keep down the error among the received and transmitted signal. Orthogonal frequency-division multiplexing (OFDM) is considered as an attractive modulation scheme that could be adopted in wireless communication systems owing to its reliability in opposition to multipath interruptions under different subchannels. Carrier frequency offset (CFO) establishes inter-carrier interference that devastates the orthogonality between the subcarriers and fluctuates the preferred signal and minimizes the effectual signal-to-noise ratio (SNR). This results in corrupted system performance. For sustaining the subcarriers’ orthogonality, timing errors and CFOs have to be approximated and sufficiently compensated for. Single carrier modulation (SCM) is a major feature for efficient OFDM system.Design/methodology/approachThis paper introduces a novel superposition coded modulation-orthogonal frequency-division multiplexing (SCM-OFDM) system with optimal CFO estimation using advanced optimization algorithm. The effectiveness of SCM-OFDM is validated by correlating the transmitted and received signal. Hence, the primary objective of the current research work is to reduce the error among the transmitted and received signal. The received signal involves CFO, which has to be tuned properly to get the signal as closest as possible with transmitted signal. The optimization or tuning of CFO is done by improved grey wolf optimization (GWO) called GWO with self-adaptiveness (GWO-SA). Further, it carries the performance comparison of proposed model with state-of-the-art models with the analysis on bit error rate (BER) and mean square error (MSE), thus validating the system’s performance.FindingsFrom the analysis, BER of the proposed and conventional schemes for CFO at 0.25 was determined, where the adopted scheme at 10th SNR was 99.6 per cent better than maximum likelihood, 99.6 per cent better than least mean square (LMS), 99.3 per cent better than particle swarm optimization (PSO), 75 per cent better than genetic algorithm (GA) and 25 per cent better than GWO algorithms. Moreover, MSE at 1st SNR, the proposed GWO-SA scheme, is 4.62 per cent better than LMS, 60.1 per cent better than PSO, 37.82 better than GA and 67.85 per cent better than GWO algorithms. Hence, it is confirmed that the performance of SCM-OFDM system with GWO-SA-based CFO estimation outperformed the state-of-the-art techniques.Originality/valueThis paper presents a technique for attaining optimal CFO and to minimize the error among the received and transmitted signal. This is the first work that uses GWO-SA for attaining optimal CFO.