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Found 24 papers, 1 papers with code
Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck
An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server.
Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization
We then develop a model and solution based on the multi-objective multi-armed bandit theory to maximize the network coverage while minimizing the FL delay.
D-Band RIS as a Reflect Array: Characterization and Hardware Impairments Study
Reflecting intelligent surface (RIS) has emerged as a promising technology for enhancing wireless communication performance and enabling new applications in 6G networks with potentially low energy consumption and hardware complexity thanks to their passive nature.
Near-Optimal LOS and Orientation Aware Intelligent Reflecting Surface Placement
More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage.
BiSPARCs for Unsourced Random Access in Massive MIMO
The detection step is followed by a per-user soft-input-soft-output (SISO) decoding of the outer channel code in combination with a successive interference cancellation (SIC) step.
Characterization of the weak Pareto boundary of resource allocation problems in wireless networks -- Implications to cell-less systems
We establish necessary and sufficient conditions for a network configuration to provide utilities that are both fair and efficient in a well-defined sense.
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches
This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement.
Explainable Artificial Intelligence (XAI)
Feature Engineering
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
Due to the non-convexity of this problem, we propose machine learning-based methods for RA antenna mode selection in both dynamic and static scenarios.
Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity
This paper presents a kernel-based adaptive filter that is applied for the digital domain self-interference cancellation (SIC) in a transceiver operating in full-duplex (FD) mode.
A Learning-Based Approach to Approximate Coded Computation
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials.
Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy
Commercial radar sensing is gaining relevance and machine learning algorithms constitute one of the key components that are enabling the spread of this radio technology into areas like surveillance or healthcare.
Superiorized Adaptive Projected Subgradient Method with Application to MIMO Detection
In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient.
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72. 4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario.
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication
This paper addresses the problem of decentralized spectrum sharing in vehicle-to-everything (V2X) communication networks.
Robust Cell-Load Learning with a Small Sample Set
Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period.
Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems
Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit.
Multi-Group Multicast Beamforming by Superiorized Projections onto Convex Sets
We formulate a convex relaxation of the problem as a semidefinite program in a real Hilbert space, which allows us to approximate a point in the feasible set by iteratively applying a bounded perturbation resilient fixed-point mapping.
Channel covariance estimation in multiuser massive MIMO systems with an approach based on infinite dimensional Hilbert spaces
We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems.
Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver.
Spectral radii of asymptotic mappings and the convergence speed of the standard fixed point algorithm
To address this limitation of existing approaches, we show in this study that the spectral radii of asymptotic mappings can be used to identify an important subclass of contractive mappings and also to estimate their moduli of contraction.
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
We propose a novel online learning based detection for the NOMA uplink.
Optimal deep neural networks for sparse recovery via Laplace techniques
Owing to the specific structure, it is shown that the centroid can be computed analytically by extending a recent result that facilitates the volume computation of polytopes via Laplace transformations.
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks.
