Abstract

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

Highlights

  • High-rate data broadband transmission over multiple-input multiple-output (MIMO) channel has become one of the mainstream techniques for the generation communication systems [1]

  • To simultaneously achieve higher convergence speed and lower steady-state mean square error (MSE) performance, we propose sparse variable step-size NLMS (VSSNLMS) algorithms for estimating MIMO channels

  • Two sparse VSS-NLMS algorithms were proposed for estimating MIMO channels

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Summary

Introduction

High-rate data broadband transmission over multiple-input multiple-output (MIMO) channel has become one of the mainstream techniques for the generation communication systems [1]. To reduce the computational cost, sparse channel estimation methods using greedy iterative algorithms were proposed in [9, 11]. Their complexity still depends on the number of nonzero taps of MIMO channel. Adaptive sparse channel estimation (ASCE) methods using sparse invariable step-size (ISS) least mean square algorithms (ISS-LMS) were proposed in [20] for single-input single-output (SISO) channels. Different from conventional sparse ISS-NLMS algorithms [21], zero-attracting variable step-size NLMS (ZA-VSS-NLMS) algorithm was proposed for ASCE to improve estimation performance in sparse multipath single-input single-output (SISO) systems [22].

System Model
Overview of Sparse ISS-NLMS Algorithms
Proposed Sparse VSS-NLMS Algorithms for Estimating MIMO Channels
Computer Simulations
Conclusion
Full Text
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