Downlink Communication

Downlink communication, the transmission of data from a base station to user devices, is a critical area of research in wireless networks, aiming to improve spectral efficiency, reliability, and user fairness. Current research focuses on optimizing resource allocation and precoding techniques, often employing deep learning models like deep deterministic policy gradient (DDPG) and autoencoders, as well as advanced signal processing methods such as canonical correlation analysis, to address challenges like interference, channel estimation, and limited feedback. These advancements are crucial for enabling high-quality services in emerging 5G and beyond networks, particularly in scenarios with dense user deployments and resource-constrained devices.

Papers

October 30, 2024

Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation
Samuele Peri, Alessio Russo, Gabor Fodor, Pablo Soldati
Reinforcement Learning Offline Reinforcement Learning Adaptation Concern Sequence Modeling Radio Access Network Conservative Q Learning Downlink Communication

September 17, 2024

A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems
Giovanni Di Gennaro, Amedeo Buonanno, Gianmarco Romano, Stefano Buzzi, Francesco A.N Palmieri
Deep Learning Approach Channel State Information Massive MIMO Short Term Memory User Clustering Downlink Communication

November 30, 2023

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
Jiwei Zhao, Jiacheng Chen, Zeyu Sun, Yuhang Shi, Haibo Zhou, Xuemin, Shen
Radio Access Network Transmission Data Complex Valued Neural Network Radio Map Downlink Communication Spectrum Utilization

October 25, 2023

How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels
Linping Qu, Shenghui Song, Chi-Ying Tsui, Yuyi Mao
Native Robustness Wireless Network Systematic Comparison Uplink Control Downlink Communication Communication Error

June 23, 2023

A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design
Vukan Ninkovic, Dejan Vukobratovic, Adriano Pastore, Carles Anton-Haro
Autoencoder Based Non Orthogonal Multiple Access Decoder Network Downlink Communication AE Based

April 11, 2023

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach
Zhaoyuan Shi, Huabing Lu, Xianzhong Xie, Helin Yang, Chongwen Huang, Jun Cai, Zhiguo Ding
Generating User Activity Deep Reinforcement Learning Approach Non Orthogonal Multiple Access Reconfigurable Intelligent Surface Downlink Communication

February 4, 2023

Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems
Omar M. Sleem, Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri, Philip Pietraski
Unsupervised Learning Channel Estimation Spectral Efficiency Downlink Communication

January 24, 2023

A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems
Farimehr Zohari, S. M. Mahdi Shahabi, Mehrdad Ardebilipour
Deep Reinforcement Learning Based Approach Non Convex Optimization Problem Phase Shift Spectral Efficiency Multi Input Multi Output Intelligent Reflecting Surface Downlink Communication

November 29, 2022

Scalable Hierarchical Over-the-Air Federated Learning
Seyed Mohammad Azimi-Abarghouyi, Viktoria Fodor
Potential Scalability Air Aggregation Downlink Communication Gradient Aggregation

January 13, 2022

Jamming Attacks on Federated Learning in Wireless Networks
Yi Shi, Yalin E. Sagduyu
Wireless Network Uplink Control Jamming Attack Downlink Communication

November 12, 2021

A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints
Wangyang Xu, Lu Gan, Chongwen Huang
Channel State Information Deep Learning Based Robust Deep Downlink Communication Operational Constraint

Downlink Communication

Papers

Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation

A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach

How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels

A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems

A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems

Scalable Hierarchical Over-the-Air Federated Learning

Jamming Attacks on Federated Learning in Wireless Networks

A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints