Improved Artificial Bee Colony Optimization Approach in UWBOFDM for Frequency Offset Estimation

Abstract

Abstract Orthogonal frequency division multiplexing (OFDM) is considered a crucial and fundamental potential approach because of its significance and role with regards to broadband wireless communication systems. The swarm intelligence optimization approach has been deployed to facilitate frequency offset optimization. An improved artificial bee colony (IABC) based optimization approach is proposed in this work that will enable ultra wideband orthogonal frequency division multiplexing (UWBOFDM) system, which will then be deployed in the carrier frequency offset (CFO) joint estimation and also for sampling frequency offset (SFO). By proper selection of cyclic delay times, correlation existing between adjacent sub carriers, a joint estimation of both CFO and SFO has been derived by employing IABC. The performance of the proposed algorithm is compared with the existing estimators, and this comparison is carried out by MATLAB7.2 simulation, which clearly depicts that the performance of the proposed algorithm is superior in terms of estimation accuracy. The performances are measured in terms of the mean square error (MSE), signal-to-noise ratio (SNR) and normalized mean square error (NMSE). The proposed approach considered for transmitter (t = 4) and receiver (r = 4) with SNR is 30 dB, which achieves MSE of about 0.35 %. Similarly, when (t = 2) and (r = 2), the proposed IABC approach achieves MSE of about 0.4 %, whereas particle swam optimization (PSO) and maximum likelihood estimator (MLE) methods achieve 0.42 and 0.44 % MSE values, respectively. On the other hand, considering (t = 4) and (r = 4) with SNR of 8 dB, the proposed approach achieves NMSE of about 0.0008 %. However, considering (t = 2) and (r = 2), the NMSE values of IABC approach, PSO, and MLE are observed to be 0.002, 0.008, and 0.08 %, respectively, for Channel Model 4 (CM4).