Joint Channel Estimation and Data Detection for Multihop OFDM Relaying System under Unknown Channel Orders and Doppler Frequencies

TL;DR

This paper introduces a novel iterative variational inference algorithm for joint channel estimation and data detection in multihop OFDM systems operating under unknown and time-varying channel conditions, without relying on prior statistical information.

Contribution

It develops a practical, sparsity-based approach using GCE-BEM and VI to accurately estimate channels and detect data with limited pilot symbols in complex multihop OFDM scenarios.

Findings

Performance close to optimal algorithms without prior statistical info

Effective channel estimation with limited pilot data

Robust data detection under unknown channel conditions

Abstract

In this paper, channel estimation and data detection for multihop relaying orthogonal frequency division multiplexing (OFDM) system is investigated under time-varying channel. Different from previous works, which highly depend on the statistical information of the doubly-selective channel (DSC) and noise to deliver accurate channel estimation and data detection results, we focus on more practical scenarios with unknown channel orders and Doppler frequencies. Firstly, we integrate the multilink, multihop channel matrices into one composite channel matrix. Then, we formulate the unknown channel using generalized complex exponential basis expansion model (GCE-BEM) with a large oversampling factor to introduce channel sparsity on delay-Doppler domain. To enable the identification of nonzero entries, sparsity enhancing Gaussian distributions with Gamma hyperpriors are adopted. An iterative algorithm is developed under variational inference (VI) framework. The proposed algorithm iteratively estimate the channel, recover the unknown data using Viterbi algorithm and learn the channel and noise statistical information, using only limited number of pilot subcarrier in one OFDM symbol. Simulation results show that, without any statistical information, the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information on system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers.