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Found 26 papers, 3 papers with code
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles.
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services.
Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks
Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network.
Integrated Distributed Semantic Communication and Over-the-air Computation for Cooperative Spectrum Sensing
Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.
Channel Estimation and Training Design for Active RIS Aided Wireless Communications
To address this issue, we further investigate this ARIS-specific channel estimation problem and propose a least-square (LS) based channel estimator, whose performance can be further improved with the design on ARIS reflection patterns at the channel training phase.
Deep Learning-Empowered Semantic Communication Systems with a Shared Knowledge Base
With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation.
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach
This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source.
Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems
To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives.
Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing
Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems.
Semi-Blind Cascaded Channel Estimation for Reconfigurable Intelligent Surface Aided Massive MIMO
In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the passive RIS elements.
Bayesian Inference
Information Theory
Information Theory
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.
Toward Smart Security Enhancement of Federated Learning Networks
In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads.
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
To exploit the full potential of SR, in this paper, we address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx's RF signals using reconfigurable intelligent surfaces.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Reconfigurable Intelligent Surface Assisted MIMO Symbiotic Radio Networks
In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment.
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks
Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated.
Signal Processing
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems
Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users.
Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks
In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels.
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.
Joint Service Pricing and Cooperative Relay Communication for Federated Learning
However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e. g., central cloud or edge server, and the mobile devices for transferring the model update.
Cryptography and Security Computer Science and Game Theory
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their states when the state and action spaces are small.
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
