Chengjie Zhao | Searcharxiv

arXiv2025

Joint Positioning, Beamforming, and Power Allocation in Full-Duplex MIMO with Position-Reconfigurable Antenna Arrays

We consider a multi-user (MU) full-duplex (FD) multiple-input multiple-output (MIMO) communication system, in which the base station transceiver is equipped with transmit and receive position reconfigurable antennas (PRAs) to mitigate both MU-interference (MUI) and self-interference (SI) while enhancing the desired signal quality. We first formulate a joint design of beamforming, transmit power allocation, and antenna placement to maximize the weighted sum-rate of the considered PRA-based MU-FD-MIMO system under the constraints on reconfigurable region size, minimum antenna spacing, and transmit power. To address this highly non-convex problem, we then propose an alternating optimization (AO) framework that decomposes the original problem into subproblems and solves them iteratively. In particular, fractional programming techniques are used to separate the optimization variables from the logarithmic and ratio terms, while the block successive upper bound minimization method handles the non-convexity of PRA placement. Simulation results confirm a significant performance gain when integrating PRAs into the MU-FD-MIMO system and demonstrate the advantage of the proposed optimization framework.

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arXiv2023

Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework

In many communication scenarios, the communication signals simultaneously suffer from white Gaussian noise (WGN) and non-Gaussian impulsive noise (IN), i.e., mixed Gaussian-impulsive noise (MGIN). Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN. Moreover, as the widely adopted IN model has no analytical and general closed-form expression of probability density function (PDF), it is extremely hard to obtain optimal communication signal and corresponding detection schemes based on classical stochastic signal processing theory. To circumvent these difficulties, we propose a data-driven end-to-end framework to address the communication signal design and detection under MGIN channel in this paper. In this proposed framework, a channel noise simulator (CNS) is elaborately designed based on an improved generative adversarial net (GAN) to simulate the MGIN without requirement of any analytical PDF. Meanwhile, a multi-level wavelet convolutional neural network (MWCNN) based preprocessing network is used to mitigate the negative effect of outliers due to the IN. Compared with conventional approaches and existing end-to-end systems, extensive simulation results verify that our proposed novel end-to-end communication system can achieve better performance in terms of bit-error rate (BER) under MGIN environments.

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