Related papers: Toward Energy-Efficient Massive MIMO: Graph Neural…
This paper investigates the resource allocation design for a pinching antenna (PA)-assisted multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system featuring multiple dielectric waveguides. To enhance…
In 6G systems, extremely large-scale antenna arrays operating at terahertz frequencies extend the near-field region to typical user distances from the base station, enabling near-field communication (NFC) with fine spatial resolution…
Efficient mitigation of power amplifier (PA) nonlinear distortion in multi-user hybrid precoding based broadband mmWave systems is an open research problem. In this article, we first carry out detailed signal and distortion modeling in…
This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, which utilizes a combined signal…
This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink…
The high energy consumption of massive multi-input multi-out (MIMO) system has become a prominent problem in the millimeter wave(mm-Wave) communication scenario. The hybrid precoding technology greatly reduces the number of radio…
Hybrid analog-digital architectures are considered as promising candidates for implementing millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems since they enable a considerable reduction of the required number of…
Physics-inspired and quantum compute based methods for processing in the physical layer of next-generation cellular radio access networks have demonstrated theoretical advances in spectral efficiency in recent years, but have stopped short…
The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the…
Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimizing the GNN computational graph suffers from: (1) Redundant…
Narrowing the performance gap between optimal and feasible detection in inter-symbol interference (ISI) channels, this paper proposes to use graph neural networks (GNNs) for detection that can also be used to perform joint detection and…
In this paper, we consider precoder designs for multiuser multiple-input-single-output (MISO) broadcasting channels. Instead of using a traditional linear zero-forcing (ZF) precoder, we propose a generalized ZF (GZF) precoder in conjunction…
Motivated by the demand for energy-efficient communication solutions in the next generation cellular network, a mixed-ADC architecture for massive multiple input multiple output (MIMO) systems is proposed, which differs from previous works…
Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains and relieve the huge energy consumption in mmWave massive MIMO systems, thus attracting much interests from academic and industry. However, most…
The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy consumption. In this paper, we investigate the compute energy…
The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide coverage, low latency, and power efficiency. Yet, many of…
Massive multi-input multiple-out (MIMO) is a key ingredient in improving the spectral efficiencies for next-generation cellular systems. Thanks to the channel reciprocity, in time-division-duplexing mode, each base station (BS) can acquire…
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be…
Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks…
To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division…