相关论文: Demo: A Reinforcement Learning-based Flexible Dupl…

This paper studies the joint beamwidth and transmit power optimization problem in millimeter wave communication systems. A deep reinforcement learning based approach is proposed. Specifically, a customized deep Q network is trained offline,…

The sixth-generation (6G) of wireless communication networks aims to leverage artificial intelligence tools for efficient and robust network optimization. This is especially the case since traditional optimization methods often face high…

Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by high compute and memory requirements, hindering their…

In this letter, we propose a joint transmission mode and transmit/receive (Tx/Rx) antenna configuration scheme referred to as X-duplex in the relay network with one source, one amplify-and-forward (AF) relay and one destination. The relay…

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way…

Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing Multi-Reconfigurable…

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive…

In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex…

In this paper, a novel hybrid-duplex scheme based on received power is proposed for heterogeneous networks (HetNets). In the proposed scheme, the duplex mode (half- or full-duplex) of each user is switchable according to the received power…

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral…

With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a…

We propose a multiple-input multiple-output (MIMO)-based full-duplex (FD) scheme that enables wireless devices to simultaneously transmit information and harvest energy using the same time-frequency resources. In this scheme, for a MIMO…

We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable…

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while…

Emerging 6G industrial networks envision autonomous in-X subnetworks to support efficient and cost-effective short range, localized connectivity for autonomous control operations. Supporting timely transmission of event-driven, critical…

Deep Learning (DL) based neural receiver models are used to jointly optimize PHY of baseline receiver for cellular vehicle to everything (C-V2X) system in next generation (6G) communication, however, there has been no exploration of how…

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing…

This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs). The performance of widely-used overcurrent…

Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple receivers due to its benefits in terms of privacy and…

Full-duplex small-cell relays with multiple antennas constitute a core element of the envisioned 5G network architecture. In this paper, we use stochastic geometry to analyze the performance of wireless networks with full-duplex…