GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A nonrandom and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size.