Design and Implementation of a Low-Complexity Symbol Detector for Sparse Channels

Abstract

In this paper, we present a low-complexity symbol detector for communication channels which have long spanning durations but a sparse multipath structure. Traditional maximum-likelihood sequence estimation using the Viterbi algorithm can provide optimal error performance for eliminating the multipath effect, but the hardware complexity grows exponentially with channel length and it is not practical for long sparse channels. We implement a near-optimal algorithm and its architecture by cascading an adaptive partial response equalizer (PRE) with an iterative belief propagation (BP) detector. A sparse channel is first equalized by a PRE to a target impulse response (TIR) with only a few nonzero coefficients remaining. The residual intersymbol interference is then canceled by a BP detector whose complexity is solely dependent on the number of nonzero coefficients in the TIR. Moreover, we present a pipeline high-throughput implementation of the detector for channel length 30 with quadrature phase-shift keying modulation. The detector can achieve a maximum throughput of 206 Mb/s with an estimated core area of 3.162 mm2 using 90-nm technology node. At a target frequency of 515 MHz, the dynamic power is about 1.096 W.