Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions

The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing feedback strategies, making it impractical to deploy in recent years. To address this issue, this paper proposes a channel modeling aided data augmentation method based on a limited number of field channel data. Specifically, the user equipment (UE) extracts the primary stochastic parameters of the field channel data and transmits them to the base station (BS). The BS then updates the typical TR 38.901 model parameters with the extracted parameters. In this way, the updated channel model is used to generate the dataset. This strategy comprehensively considers the dataset collection, model generalization, model monitoring, and so on. Simulations verify that our proposed strategy can significantly improve performance compared to the benchmarks.

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations.

Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks

Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model. Therefore, with the objective of minimizing the AoI of V2I links and prioritizing transmission of V2V links payload, we construct this optimization problem as an Markov decision process (MDP) problem in which the BS serves as an agent to allocate resources and control phase-shift for the vehicles using the soft actor-critic (SAC) algorithm, which gradually converges and maintains a high stability. A AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme based on SAC algorithm is proposed and simulation results show that its convergence speed, cumulative reward, AoI performance, and payload transmission probability outperforms those of proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3) and stochastic algorithms.

Machine learning-based Near-field Emitter Localization via Grouped Hybrid Analog and Digital Massive MIMO Receive Array

A fully-digital massive MIMO receive array is promising to meet the high-resolution requirement of near-field (NF) emitter localization, but it also results in the significantly increasing of hardware costs and algorithm complexity. In order to meet the future demand for green communication while maintaining high performance, the grouped hybrid analog and digital (HAD) structure is proposed for NF DOA estimation, which divides the large-scale receive array into small-scale groups and each group contains several subarrays. Thus the NF direction-of-arrival (DOA) estimation problem is viewed as far-field (FF) within each group, and some existing methods such as MUSIC, Root-MUSIC, ESPRIT, etc., can be adopted. Then by angle calibration, a candidate position set is generated. To eliminate the phase ambiguity arising from the HAD structure and obtain the emitter position, two low-complexity clustering-based methods, minimum sample distance clustering (MSDC) and range scatter diagram (RSD) - angle scatter diagram (ASD)-based DBSCAN (RSD-ASD-DBSCAN), are proposed based on the distribution features of samples in the candidate position set. Then to further improve the localization accuracy, a model-driven regression network (RegNet) is designed, which consists of a multi-layer neural network (MLNN) for false solution elimination and a perceptron for angle fusion. Finally, the Cramer-Rao lower bound (CRLB) of NF emitter localization for the proposed grouped HAD structure is also derived. The simulation results show that the proposed methods can achieve CRLB at different SNR regions, the RegNet has great performance advantages at low SNR regions and the clustering-based methods have much lower complexity.

Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets

This letter proposes a semantic-aware resource allocation (SARA) framework with flexible duty cycle (DC) coexistence mechanism (SARADC) for 5G-V2X Heterogeneous Network (HetNets) based on deep reinforcement learning (DRL) proximal policy optimization (PPO). Specifically, we investigate V2X networks within a two-tiered HetNets structure. In response to the needs of high-speed vehicular networking in urban environments, we design a semantic communication system and introduce two resource allocation metrics: high-speed semantic transmission rate (HSR) and semantic spectrum efficiency (HSSE). Our main goal is to maximize HSSE. Additionally, we address the coexistence of vehicular users and WiFi users in 5G New Radio Unlicensed (NR-U) networks. To tackle this complex challenge, we propose a novel approach that jointly optimizes flexible DC coexistence mechanism and the allocation of resources and base stations (BSs). Unlike traditional bit transmission methods, our approach integrates the semantic communication paradigm into the communication system. Experimental results demonstrate that our proposed solution outperforms traditional bit transmission methods with traditional DC coexistence mechanism in terms of HSSE and semantic throughput (ST) for both vehicular and WiFi users.

Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning

This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement learning (DRL) soft actor-critic (SAC) approach. Firstly, we delve into the extraction of semantic information. Secondly, we redefine metrics for semantic information in V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). Finally, we employ the SAC algorithm for decision optimization in V2V and V2I spectrum sharing based on semantic information. This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results demonstrate that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum sharing algorithms and spectrum sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

Hybrid Beamforming Design for RSMA-assisted mmWave Integrated Sensing and Communications

Integrated sensing and communications (ISAC) has been considered one of the new paradigms for sixth-generation (6G) wireless networks. In the millimeter-wave (mmWave) ISAC system, hybrid beamforming (HBF) is considered an emerging technology to exploit the limited number of radio frequency (RF) chains in order to reduce the system hardware cost and power consumption. However, the HBF structure reduces the spatial degrees of freedom for the ISAC system, which further leads to increased interference between multiple users and between users and radar sensing. To solve the above problem, rate split multiple access (RSMA), which is a flexible and robust interference management strategy, is considered. We investigate the joint common rate allocation and HBF design problem for the HBF-based RSMA-assisted mmWave ISAC scheme. We propose the penalty dual decomposition (PDD) method coupled with the weighted mean squared error (WMMSE) minimization method to solve this high-dimensional non-convex problem, which converges to the Karush-Kuhn-Tucker (KKT) point of the original problem. Then, we extend the proposed algorithm to the HBF design based on finite-resolution phase shifters (PSs) to further improve the energy efficiency of the system. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the RSMA-ISAC scheme outperforms other benchmark schemes.

Huygens-Fresnel Model Based Position-Aided Phase Configuration for 1-Bit RIS Assisted Wireless Communication

Reconfigurable intelligent surface (RIS), composed of nearly passive elements, is regarded as one of the potential paradigms to support multi-gigabit data in real-time. However, in traditional CSI (channel state information) driven frame, the training overhead of channel estimation greatly increases as the number of RIS elements increases to intelligently manipulate the reflected signals. To conveniently use the reflected signal without complex CSI feedback, in this paper we propose a position-aided phase configuration scheme based on the property of Fresnel zone. In particular, we design the impedance based discrete RIS elements with joint absorption mode and reflection mode considering the fabrication complexities, which integrated the property of the Fresnel zone to resist the impact of position error. Then, with joint absorption and 1-bit reflection mode elements, we develop the two-step position-aided ON/OFF states judgement (TPOSJ) scheme and the frame structure to control the ON/OFF state of RIS, followed by analyzing the impacts of mobility and position error on our proposed scheme. Also, we derive the Helmholtz-Kirchhoff integral theorem based power flow. Simulations show that the proposed scheme can manipulate the ON/OFF state intelligently without complex CSI, thus verifying the practical application of our proposed scheme.

Synchronization Scheme based on Pilot Sharing in Cell-Free Massive MIMO Systems

This paper analyzes the impact of pilot-sharing scheme on synchronization performance in a scenario where several slave access points (APs) with uncertain carrier frequency offsets (CFOs) and timing offsets (TOs) share a common pilot sequence. First, the Cramer-Rao bound (CRB) with pilot contamination is derived for pilot-pairing estimation. Furthermore, a maximum likelihood algorithm is presented to estimate the CFO and TO among the pairing APs. Then, to minimize the sum of CRBs, we devise a synchronization strategy based on a pilot-sharing scheme by jointly optimizing the cluster classification, synchronization overhead, and pilot-sharing scheme, while simultaneously considering the overhead and each AP's synchronization requirements. To solve this NP-hard problem, we simplify it into two sub-problems, namely cluster classification problem and the pilot sharing problem. To strike a balance between synchronization performance and overhead, we first classify the clusters by using the K-means algorithm, and propose a criteria to find a good set of master APs. Then, the pilot-sharing scheme is obtained by using the swap-matching operations. Simulation results validate the accuracy of our derivations and demonstrate the effectiveness of the proposed scheme over the benchmark schemes.