Abstract
A soft output low complexity maximum likelihood sequence estimation (MLSE) equalizer is proposed to equalize M-QAM signals in systems with extremely long memory. The computational complexity of the proposed equalizer is quadratic in the data block length and approximately independent of the channel memory length, due to high parallelism of its underlying Hopfield neural network structure. The superior complexity of the proposed equalizer allows it to equalize signals with hundreds of memory elements at a fraction of the computational cost of conventional optimal equalizer, which has complexity linear in the data block length but exponential in die channel memory length. The proposed equalizer is evaluated in extremely long sparse and dense Rayleigh fading channels for uncoded BPSK and 16-QAM-modulated systems and remarkable performance gains are achieved.
Highlights
Multipath propagation in wireless communication systems is a challenge that has enjoyed much attention over the last few decades
It will be established that the Hopfield neural network (HNN) maximum likelihood sequence estimation (MLSE) equalizer outperforms the minimum mean squared error (MMSE) equalizer in long fading channels and it will be shown that the performance of the HNN MLSE equalizer in sparse channels is better than its performance in equivalent dense channels, that is, longer channels with the same amount of nonzero channel impulse response (CIR) taps
Least Squares (LS) channel estimation is used to determine an estimate for the CIR, in order to include the effect of imperfect channel state information (CSI) in the simulation results
Summary
Multipath propagation in wireless communication systems is a challenge that has enjoyed much attention over the last few decades. The Viterbi MLSE algorithm and the MAP algorithm estimate the transmitted information with maximum confidence, their computational complexities are prohibitive, increasing exponentially with an increase in channel memory [4]. A complete complexity analysis, as well as the performance of the proposed equalizer in sparse channels, are presented) is developed for equalization in M-QAMmodulated systems with extremely long memory. (A complete computational complexity analysis is presented in Section 5) Its superior computational complexity, compared to that of the Viterbi MLSE and MAP algorithms, is due to the high parallelism and high level of interconnection between the neurons of its underlying HNN structure This equalizer, referred to as the HNN MLSE equalizer, iteratively mitigates the effect of ISI, producing near-optimal estimates of the transmitted symbols.
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