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Found 16 papers, 0 papers with code
RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean
The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user's position is determined based on the estimated multiple AoD pairs in the second stage.
RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI
Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy.
Rate Splitting Multiple Access for Simultaneous Lightwave Information and Power Transfer
This paper initiate the application of rate splitting multiple access (RSMA) for simultaneous lightwave information and power transfer (SLIPT), where users require to decode information and harvest energy.
Joint Active and Passive Beamforming Optimization for Beyond Diagonal RIS-aided Multi-User Communications
Benefiting from its capability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide additional design flexibility via interactions between RIS elements, beyond-diagonal RIS (BD-RIS) has attracted considerable research interests recently.
Cramér-Rao Bound Based Waveform Optimization for MIMO Radar: An Efficient Linear-Proximal Method
This paper focuses on radar waveform optimization for minimizing the Cram\'er-Rao bound (CRB) in a multiple-input multiple-output (MIMO) radar system.
RS-BNN: A Deep Learning Framework for the Optimal Beamforming Design of Rate-Splitting Multiple Access
Rate splitting multiple access (RSMA) relies on beamforming design for attaining spectral efficiency and energy efficiency gains over traditional multiple access schemes.
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC
Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time.
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN.
Reconfigurable Intelligent Surface Empowered Rate-Splitting Multiple Access for Simultaneous Wireless Information and Power Transfer
Numerical results show that by marrying the benefits of RSMA and RIS, the proposed RIS empowered RSMA achieves a better tradeoff between the WSR of IRs and energy harvested at ERs.
Rate-Splitting Multiple Access for 6G -- Part III: Interplay with Reconfigurable Intelligent Surfaces
This letter is the third part of a three-part tutorial that focuses on rate-splitting multiple access (RSMA) for 6G.
Rate-Splitting assisted Massive Machine-Type Communications in Cell-Free Massive MIMO
This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications.
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design
Specifically, we theoretically establish the convergence analysis of the FL process, and then apply the proposed device scheduling policy to maximize the number of weighted devices under the FL system latency and sum power constraints.
Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications
In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference.
Cooperative Rate-Splitting for Secrecy Sum-Rate Enhancement in Multi-antenna Broadcast Channels
In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes.
Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying.
