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Abstract
class=Abstract>This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random 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. DOI: http://dx.doi.org/10.5755/j01.eee.22.1.14114
Highlights
One of the major problems that limits the high speed data transmission through a wireless channel is the Inter Symbol Interference (ISI) [1]
It is clearly evident that the proposed Genetic Algorithms (GA) based technique performs significantly better than the techniques in [3] and [14] for the case of sparse multipath channel
It can be witnessed that the performance of GA estimator significantly improves with an increase in population size up to 2L
Summary
One of the major problems that limits the high speed data transmission through a wireless channel is the Inter Symbol Interference (ISI) [1]. Based on the behaviour of channel’s selectivity in frequency and time domains, the known training sequence is either transmitted frequently over the dedicated time slots (across over the entire frequency slots), or transmitted over the dedicated frequency slots (across over the entire time slots) This dedicated allocation of time/frequency slots for the training sequence is an overhead on the spectral efficiency, for the case of highly selective wireless fading channels. The channel is estimated by exploiting the statistical properties of information sequence known at the receiver. Instead of using explicit time/frequency slots for training sequence, a low-power periodic training sequence is arithmetically added over the information sequence This improves the spectral efficiently and effectively tracks the time and frequency selectivity in the communication channel.
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