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Sparse channel estimation for OFDM based on IOMP algorithm under unknown sparsity

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Abstract

Compressive sensing (CS) based channel estimation (CE) improves the utilization of spectrum resources effectively in orthogonal frequency division multiplexing (OFDM) system. Generally, the channel sparsity is assumed to be known in sparse reconstruction algorithms such as Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP). However, since the uncertainty of the environment, the channel sparsity is usually tricky to obtain. This paper proposes an improved OMP algorithm based on intersect operation (IOMP), which does not need a priori knowledge of channel sparsity. Initially, we use the OMP algorithm twice to estimate two different partial sparse delay path supports at partially different pilot positions, respectively. Then introduce the intersect operation to process these two partial supports. Besides, we discussed an optimized pilot allocation scheme based on discrete stochastic approximation and Iterative Exhaustive Search, both of which are employed to build the framework of CS-based channel estimation. Simulation results demonstrate that the proposed IOMP algorithm provides a better bit error ratio (BER) performance than the traditional OMP algorithm under unknown channel sparsity.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China

    Zhian Zheng, Jianjian Liu, Junjie Zhu & Haiyan Huang

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  1. Zhian Zheng
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  2. Jianjian Liu
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  4. Haiyan Huang
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Correspondence to Jianjian Liu.

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Zheng, Z., Liu, J., Zhu, J. et al. Sparse channel estimation for OFDM based on IOMP algorithm under unknown sparsity. Wireless Netw 27, 5029–5038 (2021). https://doi.org/10.1007/s11276-021-02788-8

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