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Found 26 papers, 0 papers with code
SignSGD with Federated Voting
SignSGD with majority voting (signSGD-MV) is an effective distributed learning algorithm that can significantly reduce communication costs by one-bit quantization.
Nonlinear Self-Interference Cancellation With Learnable Orthonormal Polynomials for Full-Duplex Wireless Systems
In this paper, we propose a novel algorithm for nonlinear digital SIC that adaptively constructs orthonormal polynomial basis functions according to the non-stationary moments of the transmit signal.
Modeling and Coverage Analysis of K-Tier Integrated Satellite-Terrestrial Downlink Networks
In this paper, we introduce a tractable approach to analyze the downlink coverage performance of multi-tier ISTNs, where each network tier operates with orthogonal frequency bands.
Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems
In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems.
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
Distributed learning is an effective approach to accelerate model training using multiple workers.
Ergodic Secrecy Rate Analysis for LEO Satellite Downlink Networks
Satellite networks are recognized as an effective solution to ensure seamless connectivity worldwide, catering to a diverse range of applications.
Coverage Analysis of Dynamic Coordinated Beamforming for LEO Satellite Downlink Networks
Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in direct proportion to the number of antennas on each satellite.
On the Learning of Digital Self-Interference Cancellation in Full-Duplex Radios
The experimental results demonstrate the robustness of the model-based SIC methods, providing practical evidence of their effectiveness.
Finding Globally Optimal Configuration of Active RIS in Linear Time
This paper presents an algorithm for finding the optimal configuration of active reconfigurable intelligent surface (RIS) when both transmitter and receiver are equipped with a single antenna each.
Deep Polar Codes
This decoding algorithm leverages the parity check equations in the reverse process of the multi-layered pre-transformed encoding for SCL decoding.
BanditLinQ: A Scalable Link Scheduling for Dense D2D Networks with One-Bit Feedback
The algorithm clusters the links and applies the UCB algorithm per cluster using the collected one-bit feedback information.
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning
The training efficiency of complex deep learning models can be significantly improved through the use of distributed optimization.
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM
The proposed split structure in the computation of the Kalman gain allows to compensate for state and measurement model mismatch effects independently.
Communication-Efficient Federated Learning via Quantized Compressed Sensing
We also present a low-complexity approach for the gradient reconstruction.
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning
In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding.
Rate-Splitting Multiple Access for Downlink MIMO: A Generalized Power Iteration Approach
Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access.
Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems
For maximizing EE in quantized downlink massive MIMO systems, this paper formulates a precoding optimization problem with antenna selection; yet acquiring the optimal joint precoding and antenna selection solution is challenging due to the intricate EE characterization.
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity
A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource.
Bayesian Federated Learning over Wireless Networks
This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).
Distributed Precoding Using Local CSIT for MU-MIMO Heterogeneous Cellular Networks
The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT).
Terahertz Line-Of-Sight MIMO Communication: Theory and Practical Challenges
A relentless trend in wireless communications is the hunger for bandwidth, and fresh bandwidth is only to be found at ever-higher frequencies.
Noncooperative Precoding for Massive MIMO HetNets: SILNR Maximization Precoding
In this paper, we present a novel noncooperative massive MIMO precoding technique called signal-to-interference-plus-leakage-plus-noise-ratio (SILNR) maximization precoding.
Sparse Joint Transmission for Cell-Free Massive MIMO: A Sparse PCA Approach
Cell-free massive multiple-input multiple-output (MIMO) is a promising cellular network.
Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers
The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data.
Information Theory Information Theory
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function.
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing
A reliable support detection is essential for a greedy algorithm to reconstruct a sparse signal accurately from compressed and noisy measurements.
