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IEEE Conference on Wireless Communications and Networking

作者： Jinyue Yang Rong He Yujun Liang Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-link operation (MLO) is one of the key features of 802.11be, which can achieve higher throughput, and lower latency than single-link operation in a heavily loaded network. For a nonsimultaneous transmit and receive (NSTR) multi-link device (MLD), synchronous transmissions across multiple links are required to avoid the in-device coexistence (IDC) interference. Compared to legacy access mechanisms of single-link, the modeling and performance theoretical analysis of synchronous multi-link access mechanisms face some new challenges. The two-dimensional Markov performance analysis model for single-link cannot be applied to multi-link scenarios. In this paper, we present an analytical model to compute the multi-link synchronous access probability and the saturation throughput in the assumption of ideal channel conditions. The proposed Markov model combines the distributed coordination function (DCF) mechanism with the process of multi-link synchronous access based on the threshold of waiting time. Different from other proposed MLO models, we consider the stochastic state of channel during the synchronous access backoff procedures of each link, and define the concept of waiting time threshold. Comparison with simulation results show that the proposed model is accurate in predicting the multi-link synchronous access probability, as well as system saturation throughput.

关键词： Analytical models Accuracy Computational modeling Simulation Stochastic processes Quality of service Systems modeling Predictive models Throughput Wireless fidelity

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An Integrated Communication and Computing Scheme for Wi-Fi Networks based on Generative AI and Reinforcement Learning

arXiv

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arXiv 2024年

作者： Du, Xinyang Fang, Xuming Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing Artificial Intelligence (AI) computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Integrated Communication and Computing Scheme for Wi-Fi Networks based on Generative AI and Reinforcement Learning

An Integrated Communication and Computing Scheme for Wi-Fi N...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinyang Du Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

关键词： Training Costs Generative AI Computational modeling Simulation Quality of service IEEE 802.11ax Standard Resource management Wireless fidelity Tuning

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Research on Next-Generation Wi-Fi Spatial Reuse Power Control Based on Federated Reinforcement Learning

Research on Next-Generation Wi-Fi Spatial Reuse Power Contro...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jing Wang Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

With the proliferation of densely deployed access points (APs), competition among multiple APs has led to increased occurrences of co-channel interference and access conflicts between APs. The IEEE 802.11 working group has proposed multi-AP coordination schemes in the next-generation Wi-Fi standard to enhance Wi-Fi performance in multi-AP scenarios. We primarily investigate the coordinated spatial reuse (CSR) mechanism for multiple APs. In the CSR group, a set of APs utilize the same channel for data transmission within a single transmission opportunity (TXOP), during which power control for individual devices is necessary to mitigate interference between APs. We propose a Federated Learning Double Deep Q-Network (FL-DDQN) algorithm for power allocation among multiple users in the CSR environment. The simulation results indicate that the proposed algorithm can improve throughput performance by approximately 11.06% to 100.51% and reduce the latency by approximately 61.34% to 86.71 % compared to several baseline schemes.

关键词： Performance evaluation Simulation Power control Reinforcement learning Throughput Approximation algorithms Delays

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Optimization of Resource Allocation in Multi-Link P2P Communication Systems Based on Reinforcement Learning

Optimization of Resource Allocation in Multi-Link P2P Commun...

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International Conference on information, Communication and Networks (ICICN)

作者： Haifeng Zhu Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

The next-generation Wi-Fi 7 protocol supports multi-link operation and Peer-to-Peer (P2P) communication. Combining two of them can further improve spectrum resource utilization. However, P2P communication may cause severe co-channel interference. To address this issue, this paper proposes a resource allocation strategy based on the Double Deep Q Network-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm, utilizing a centralized training-distributed execution framework for multi-agent, multi-task reinforcement learning. This approach aims to ensure Quality of Service (QoS) for Wi-Fi users while facilitating reasonable Resource Unit (RU) reuse selection and transmission power control by P2P users across multiple links. It also seeks to mitigate the interference of P2P communication on direct Wi-Fi communication while maximizing system capacity. Simulation results indicate that the proposed algorithm achieves greater throughput performance gains compared to the Multi-Agent Deep Q Network (MA-DQN), Multi-Agent Greedy (MA-Greedy), and random selection algorithms mentioned in this paper, with the DDQN-TD3 algorithm exhibiting optimal convergence.

关键词： Protocols Simulation Power control Quality of service Throughput Multitasking Peer-to-peer computing Resource management Wireless fidelity Convergence

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Deep Diffusion Deterministic Policy Gradient based Performance Optimization for Wi-Fi Networks

arXiv

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arXiv 2024年

作者： Liu, Tie Fang, Xuming He, Rong Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

Generative Diffusion Models (GDMs), have made significant strides in modeling complex data distributions across diverse domains. Meanwhile, Deep Reinforcement Learning (DRL) has demonstrated substantial improvements in optimizing Wi-Fi network performance. Wi-Fi optimization problems are highly challenging to model mathematically, and DRL methods can bypass complex mathematical modeling, while GDMs excel in handling complex data modeling. Therefore, combining DRL with GDMs can mutually enhance their capabilities. The current MAC layer access mechanism in Wi-Fi networks is the Distributed Coordination Function (DCF), which dramatically decreases in performance with a high number of terminals. In this study, we propose the Deep Diffusion Deterministic Policy Gradient (D3PG) algorithm, which integrates diffusion models with the Deep Deterministic Policy Gradient (DDPG) framework to optimize Wi-Fi network performance. To the best of our knowledge, this is the first work to apply such an integration in Wi-Fi performance optimization. We propose an access mechanism that jointly adjusts the contention window and the aggregation frame length based on the D3PG algorithm. Through simulations, we have demonstrated that this mechanism significantly outperforms existing Wi-Fi standards in dense Wi-Fi scenarios, maintaining performance even as the number of users increases sharply. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Leveraging Data-Driven Deep Neural Network for IEEE 802.11bd Receiver Design

Leveraging Data-Driven Deep Neural Network for IEEE 802.11bd...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ziyi Zhang Yin Jiang Hu Zhao Bowen Xiao Yan Long Honghao Ju Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

IEEE 802.11bd is especially designed for the next generation vehicle-to-everything (NGV) communication. However, due to vehicle’s high mobility, the performance of IEEE 802.11bd receiver can be severely deteriorated by the fast channel fading, multi-path, carrier frequency offset (CFO), and symbol timing offset (STO) impairments, and these impose great challenges to the conventional receiver design. Moreover, since the conventional receiver is built on a pre-assumed model of the signal impairments, this limits its performance improvement. To overcome this, in this paper, we explore the special frame structure (subcarrier number, pilot, and midamble) of the IEEE 802.11bd as well as the strong non-linear fitting capability of the deep neural network (DNN), and design a data-driven DNN assisted IEEE 802.11bd receiver. We optimize our symbol recovery DNN structure to provide strong symbol recovery performance given the fast channel fading, multi-path, CFO, and STO impairments. To further improve the generalization performance of our receiver, we propose a training data variety augmentation method and optimize the training data and reference data structure in DNN training and reference stages. We verify the performance of our proposed receiver in rural line-of-sight (LOS) channel, highway LOS channel, and urban LOS channel, and simulation results show that our proposed receiver can significantly reduce the symbol error rate compared with the conventional receiver, and can preserve a competitive generalization performance.

关键词： Fading channels Training Road transportation Vehicular and wireless technologies Simulation Symbols Training data Receivers Artificial neural networks Data structures

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Rate-Splitting Multiple Access Interplaying with Active Reconfigurable Intelligent Surfaces for Railway Communications With Imperfect CSIT

Rate-Splitting Multiple Access Interplaying with Active Reco...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yongbo Li Gang Liu Jiewen Hu Zheng Ma The Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Evolving from informatization to intelligence, railway wireless communication system (RWCS) is required to support more high quality service with strained resources. Nevertheless, due to the high mobility of trains, the perfect channel state information (CSI) of RWCS is typically hard to obtain, which may induce severe performance loss. To address these issues, we proposed an active RIS-assisted RSMA RWCS, where the CSI acquired is assumed to be imperfect. In this system, we formulated a weighted sum-rate (WSR) maximization problem to jointly optimize the beamforming, rate allocation vectors and RIS precoding matrix. To overcome the uncertainty of imperfect CSI and non-convexity of this problem, we transform orignial WSR maximization problem into a weighted average sum-rate ones, and we propose a block coordinate descent (BCD) algorithm based on weighted minimum mean square error (WMMSE) , fractional programming (FP) and sample average approximated (SAA) method to solve it. Numerical results show that active RIS-aied rate-splitting architecture has impressive advantages over conventional passive RIS-assisted space division multiple access (SDMA) under imperfect CSI.

关键词： Wireless communication Vehicular and wireless technologies Railway communication Uncertainty Array signal processing Precoding Transforms Reconfigurable intelligent surfaces Vectors Resource management

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints 100

Secondary Access Class Barring for Machine Type Communicatio...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wen, Yuxuan Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency. © 2024 IEEE.

关键词： Internet of things

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A Scheme of AP Selection and Power Allocation Based on Multi-Ap Coordination in Wi-Fi Networks

SSRN

引用

SSRN 2023年

作者： Zheng, Zihan Fang, Xuming He, Rong Xiang, Yun Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

For the densely deployed scenario of Wi-Fi, stations located on the edge of overlapping Basic Service Sets(OBSS) may suffer from severe co-channel interference or adjacent channel interference from adjacent OBSSs, thus leading to a decrease in aggregate throughput. Multiple access point(Multi-AP) coordination technology is one of the potential technologies of future Wi-Fi standard, which provides an effective way to coordinate the inter-BSS interference on edge Stations(STAs). Multi-AP coordination can reduce inter-cell interference and improve the spectrum utilization of the system. In this paper, an optimization scheme of AP selection and power allocation based on multi-AP coordination is proposed to optimize the system throughput of multi-BSS. The AP selection is to set the path loss threshold to enable STA to select the appropriate cooperative AP from multiple APs and provides a higher rate for STA. While the power allocation problem of cooperative multi-AP is modeled to the problem of maximizing the system capacity, and the improved genetic algorithm (GA) is adopted to decrease the computing complexity. Simulation results show that compared with Random Power Allocation(RPA) and Equal Power Allocation(EPA), the average SINR of each STA in the system is increased by 10% and 24% respectively. © 2023, The Authors. All rights reserved.

关键词： Wi-Fi

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