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Found 19 papers, 4 papers with code
Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach
First, we add a dithering signal to artificially decrease the SNR and then infer the likelihood function from the quantized dithered signals by using an SNR estimate derived from a deep neural network-based estimator which is trained offline.
Adaptive Learning-Based Detection for One-Bit Quantized Massive MIMO Systems
At low SNR, observations vary frequently in value but the high noise power makes capturing the effect of the channel difficult.
Low Complexity Hybrid Beamforming for mmWave Full-Duplex Integrated Access and Backhaul
We consider an integrated access and backhaul (IAB) node operating in full-duplex (FD) mode.
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters
We show that strong duality holds between the primal DL formulation and its manageable Lagrangian dual problem which can be interpreted as the virtual uplink (UL) problem with adjustable noise covariance matrices.
Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC
In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs).
Forward Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC
In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs).
Full-Duplex Wideband mmWave Integrated Access and Backhaul with Low Resolution ADCs
We consider a wideband integrated access and backhaul system operating in full-duplex mode between the New Radio gNB donor and single user equipment.
Full-Duplex Massive MIMO Cellular Networks with Low Resolution ADC/DAC
In this paper, we provide an analytical framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low resolution analog-to-digital and digital-to-analog converters (ADCs and DACs).
Joint Beamforming and Interference Cancellation in MmWave Wideband Full-Duplex Systems
Full-duplex (FD) systems have the capability to transmit and receive at the same time in the same frequency band.
Coordinated Beamforming in Quantized Massive MIMO Systems with Per-Antenna Constraints
In this work, we present a solution for coordinated beamforming for large-scale downlink (DL) communication systems with low-resolution data converters when employing a per-antenna power constraint that limits the maximum antenna power to alleviate hardware cost.
Hybrid Beamforming Design for Wideband mmWave Full-Duplex Systems
Recently, full duplex (FD) has been studied in 5G LTE millimeter wave (mmWave) cellular communications for New Radio in 3GPP releases 15-17.
Spectral Efficiency vs Complexity in Downlink Algorithms for Reconfigurable Intelligent Surfaces
Subject to the non-convex constraints due to the RIS phase shifts, we maximize the spectral efficiency or equivalent channel power as a proxy.
Compressed-Sensing based Beam Detection in 5G NR Initial Access
To support millimeter wave (mmWave) frequency bands in cellular communications, both the base station and the mobile platform utilize large antenna arrays to steer narrow beams towards each other to compensate the path loss and improve communication performance.
Deep Learning Predictive Band Switching in Wireless Networks
Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location.
A Framework for Automated Cellular Network Tuning with Reinforcement Learning
In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments.
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement
We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments.
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells.
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks.
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments.
