Network Coded Multi-Hop Wireless Communication Networks: Channel Estimation and Training Design

TL;DR

This paper develops a network coding transmission strategy and channel estimation techniques for multi-hop wireless networks, enhancing data rates and coverage, with specific focus on 4-hop and 2N-hop scenarios, using LMMSE and ML methods.

Contribution

It introduces a novel 4-hop network coding transmission strategy and extends it to 2N-hop scenarios, along with designing LMMSE and ML channel estimation methods for improved transmission quality.

Findings

Closed-form MSE expressions for LMMSE estimation

Effective training sequence design based on CRLB

Numerical results confirm accuracy and effectiveness

Abstract

User cooperation based multi-hop wireless communication networks (MH-WCNs) as the key communication technological component of mobile social networks (MSNs) should be exploited to enhance the capability of accumulating data rates and extending coverage flexibly. As one of the most promising and efficient user cooperation techniques, network coding can increase the potential cooperation performance gains among selfishly driven users in MSNs. To take full advantages of network coding in MH-WCNs, a network coding transmission strategy and its corresponding channel estimation technique are studied in this paper. Particularly, a $4-$hop network coding transmission strategy is presented first, followed by an extension strategy for the arbitrary $2N-$hop scenario ($N\geq 2$). The linear minimum mean square error (LMMSE) and maximum-likelihood (ML) channel estimation methods are designed to improve the transmission quality in MH-WCNs. Closed form expressions in terms of the mean squared error (MSE) for the LMMSE channel estimation method are derived, which allows the design of the optimal training sequence. Unlike the LMMSE method, it is difficult to obtain closed-form MSE expressions for the nonlinear ML channel estimation method. In order to accomplish optimal training sequence design for the ML method, the Cram\'{e}r-Rao lower bound (CRLB) is employed. Numerical results are provided to corroborate the proposed analysis, and the results demonstrate that the analysis is accurate and the proposed methods are effective.