Abstract
A novel application of quantum behaved particle swarm optimization technique (QPSO) in developing an adaptive channel equalizer based on finite impulse response (FIR) filter is presented. Equalizers form an inherent part of receiver in communication system that help in eliminating distortions in the received data to counterbalance the interference and nonlinearity that appears in practical channels using a simple and proficient optimization algorithm QPSO. Two examples of linear and nonlinear channels are undertaken. The results obtained are compared with those achieved by genetic algorithm (GA), standard PSO, and the conventional least mean square (LMS) methods. Extensive simulation carried out at two values of additive white Gaussian noise (AWGN), 20 and 30 dB reveals that the proposed approach to design the adaptive channel equalizer has an improved performance in terms of both mean square error (MSE) and bit error rate (BER).
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Sinha, R., Choubey, A., Mahto, S.K., Ranjan, P. (2019). Quantum Behaved Particle Swarm Optimization Technique Applied to FIR-Based Linear and Nonlinear Channel Equalizer. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_4
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DOI: https://doi.org/10.1007/978-981-13-0341-8_4
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