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Found 15 papers, 2 papers with code
Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain.
Positive concave deep equilibrium models
Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and computer vision tasks.
Inverse Feasibility in Over-the-Air Federated Learning
We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms.
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.
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform.
Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems
To overcome the issue of large signalling overhead in the mmWave band, the proposed method exploits the spatiotemporal correlation between sub-6GHz and mmWave bands, and it predicts/tracks the RF precoders in the mmWave band from sub-6GHz channel measurements.
Deep Learning Beam Optimization in Millimeter-Wave Communication Systems
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense.
Fixed points of nonnegative neural networks
We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors.
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.
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.
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.
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.
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.
