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Improper Gaussian Signaling for the $K$-User MIMO Interference Channels With Hardware Impairments

arXiv: 2001.10403
This paper investigates the performance of improper Gaussian signaling (IGS) for the $K$-user multiple-input, multiple-output (MIMO) interference channel (IC) with hardware impairments (HWI). HWI may arise due to imperfections in the devices like I/Q imbalance, phase noise, etc. With I/Q imbalance, the received signal is a widely linear transformation of the transmitted signal and noise. Thus, the effective noise at the receivers becomes improper, which means that its real and imaginary parts are correlated and/or have unequal powers. IGS can improve system performance with improper noise and/or improper interference. In this paper, we study the benefits of IGS for this scenario in terms of two performance metrics: achievable rate and energy efficiency (EE). We consider the rate region, the sum-rate, the EE region and the global EE optimization problems to fully evaluate the IGS performance. To solve these non-convex problems, we employ an optimization framework based on majorization-minimization algorithms, which allow us to obtain a stationary point of any optimization problem in which either the objective function and/or constraints are linear functions of rates. Our numerical results show that IGS can significantly improve the performance of the $K$-user MIMO IC with HWI and I/Q imbalance, where its benefits increase with the number of users, $K$, and the imbalance level, and decrease with the number of antennas.
accepted
- University of Cantabria Spain
- University of Cantabria Spain
- University of Paderborn Germany
Signal Processing (eess.SP), FOS: Computer and information sciences, Improper Gaussian signaling, Computer Science - Information Theory, Information Theory (cs.IT), Convex/concave procedure, Interference channel, MIMO systems, Achievable rate region, Energy efficiency, FOS: Electrical engineering, electronic engineering, information engineering, Majorization-minimization, Electrical Engineering and Systems Science - Signal Processing, Generalized Dinkelbach algorithm, Hardware impairments
Signal Processing (eess.SP), FOS: Computer and information sciences, Improper Gaussian signaling, Computer Science - Information Theory, Information Theory (cs.IT), Convex/concave procedure, Interference channel, MIMO systems, Achievable rate region, Energy efficiency, FOS: Electrical engineering, electronic engineering, information engineering, Majorization-minimization, Electrical Engineering and Systems Science - Signal Processing, Generalized Dinkelbach algorithm, Hardware impairments
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