Abstract

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

Highlights

  • Rising demands of high data rates and reliable communications given the limited power and bandwidth resources impose enormous challenges on the generation of wireless communication systems [1], [2]

  • Numerical evaluations of the adopted Hardware distortions (HWDs) system are carried out to study the drastic effects of hardware imperfections and the effectiveness of the mitigation strategies

  • The performance of the proposed asymmetric transmission schemes probabilistic shaping (PS) and hybrid shaping (HS) is quantified as opposed to the benchmark no-shaping (NS) and conventional geometric shaping (GS), with varying energy per bit per noise ratio (EbNo) and HWD levels

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Summary

Introduction

Rising demands of high data rates and reliable communications given the limited power and bandwidth resources impose enormous challenges on the generation of wireless communication systems [1], [2]. Various research contributions propose new configurations and novel techniques to address these challenges [3], [4]. The performance of OFC IGS PGS HWD NS GS PS HS AS KKT. SNR PDF EbNo AWGN QAM PSK DoF ML MAP SCP. Signal-to-noise ratio Probability density function Energy per bit per noise ratio Additive white Gaussian noise Quadrature amplitude modulation. Phase shift keying Degrees of freedom Maximum likelihood Maximum a posterior Successive convex programming FSO. Free-space optics r.v. Random variable MC Monte Carlo TB

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