Channel Estimation, Interference Cancellation, and Symbol Detection for Communications on Overlapping Channels

TL;DR

This paper introduces a joint framework for interference cancellation, channel estimation, and symbol detection in overlapping channels with unsynchronized sources, improving performance across various SNRs.

Contribution

It proposes a novel two-phase joint estimation and detection framework with an iterative algorithm for interference mitigation in overlapping channels.

Findings

Effective interference mitigation across a wide SNR range

Enhanced channel estimation and symbol detection performance

Iterative algorithm outperforms non-iterative methods

Abstract

In this paper, we propose the joint interference cancellation, fast fading channel estimation, and data symbol detection for a general interference setting where the interfering source and the interfered receiver are unsynchronized and occupy overlapping channels of different bandwidths. The interference must be canceled before the channel estimation and data symbol detection of the desired communication are performed. To this end, we have to estimate the Effective Interference Coefficients (EICs) and then the desired fast fading channel coefficients. We construct a two-phase framework where the EICs and desired channel coefficients are estimated using the joint maximum likelihood-maximum a posteriori probability (JML-MAP) criteria in the first phase; and the MAP based data symbol detection is performed in the second phase. Based on this two-phase framework, we also propose an iterative algorithm for interference cancellation, channel estimation and data detection. We analyze the channel estimation error, residual interference, symbol error rate (SER) achieved by the proposed framework. We then discuss how to optimize the pilot density to achieve the maximum throughput. Via numerical studies, we show that our design can effectively mitigate the interference for a wide range of SNR values, our proposed channel estimation and symbol detection design can achieve better performances compared to the existing method. Moreover, we demonstrate the improved performance of the iterative algorithm with respect to the non-iterative counterpart.