Phase Noise Suppression Algorithm Based on Modified LLR Metric in SC-FDMA System

Zijie Xu,Guangliang Ren

doi:10.1155/2017/9410483

Abstract

In single-carrier frequency-division multiple-access (SC-FDMA) receivers in the long-term evolution uplink, phase noise is one of the major radio-frequency front-end impairments that degrade system performance. To address this issue, we propose a new log-likelihood ratio (LLR) computation algorithm. A signal model with residual phase noise is considered in the algorithm. Based on this model, we derive a closed-form expression of the likelihood function of the received symbol and calculate more accurate LLR information. Thus the accuracy of the decoder is increased and the performance of the SC-FDMA system is improved. Simulation results show that our proposed algorithm achieves superior bit error rate (BER) performance compared with the existing LLR calculation algorithms in high-order quadrature amplitude modulations (QAM).

Highlights

To achieve higher spectral efficiencies, the current trend in the long-term evolution (LTE) uplink is to employ high-order modulation schemes, such as 256-ary quadrature amplitude modulation (256-quadrature amplitude modulations (QAM)) or 1024-QAM [1]
The single-carrier frequency-division multiple-access (SCFDMA) system with high-order modulations is very vulnerable to phase noise (PN), which will break the orthogonality among subcarriers and distort the received signals [2]
Numerical results for the proposed likelihood ratio (LLR) (P-LLR) algorithm are presented

Summary

Introduction

To achieve higher spectral efficiencies, the current trend in the long-term evolution (LTE) uplink is to employ high-order modulation schemes, such as 256-ary quadrature amplitude modulation (256-QAM) or 1024-QAM [1]. A pilot-tone assisted LLR algorithm based on a modified Bessel function was derived in [8, 9], where only common phase error (CPE) is considered in the signal model. Another approach [10] was to employ linear transform (LT) on the phase noise term and derive an approximate expression of the likelihood function. After carrier-phase recovery, the received signal is disturbed by the residual PN and AWGN Based on this received signal model, we derive an accurate closed-form formula of the likelihood function and propose a modified LLR calculation algorithm.

System Model

LLR Calculation Algorithm

Simulation Results

Conclusion