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Unreliability normalization weighted bit-flipping algorithms of LDPC decoding for ReRAM systems

Science China(information Sciences) 2024年第12期67卷 335-336页

作者： Qike PANG Zheng MA Xiaohu TANG Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University

As the emerging non-volatile storage technology, resistive random-access memory(ReRAM) [1] has garnered considerable interest from diverse industries. It is also one of the most promising candidates for the in-memory ...

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Leveraging Data-Driven Deep Neural Network for IEEE 802.11bd Receiver Design 100

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

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

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

ISBN: (纸本)9798331517786

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. © 2024 IEEE.

关键词： Frequency allocation

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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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2024 IEEE Global Communications Conference, GLOBECOM 2024

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

ISBN: (纸本)9798350351255

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. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimization of Resource Allocation in Multi-Link P2P Communication Systems Based on Reinforcement Learning 12

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

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12th IEEE International Conference on information and Communication Networks, ICICN 2024

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

ISBN: (纸本)9798350355802

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. © 2024 IEEE.

关键词： Wi-Fi

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Rate-Splitting Multiple Access Interplaying with Active Reconfigurable Intelligent Surfaces for Railway Communications With Imperfect CSIT 100

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

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Li, Yongbo Liu, Gang Hu, Jiewen Ma, Zheng Southwest Jiaotong University The Provincial Key Laboratory of Information Coding and Transmission Chengdu611756 China

ISBN: (纸本)9798331517786

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. © 2024 IEEE.

关键词： Mean square error

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A miniaturized wideband high-gain end-fire antenna for 5G-R communication applications

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High-Speed Railway 2024年第4期2卷 259-264页

作者： Yutong Shi Yanliang Wu Changhai Hu Zheng Ma Wen Luo Provincial Key Laboratory of Information Coding and Transmission School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China State Key Laboratory of Rail Transit Engineering Informatization China Railway First Survey andDesign Institute Group Company Ltd.Xi’an 710043China

This paper presents a miniaturized wideband high-gain microstrip end-fire antenna specifically designed for 5G-R communication *** antenna structure comprises a microstrip folded dipole resonator and end-fire directing *** employing Intercalated Coupling Structures(ICS)between the folded dipole resonator and the ground plane,the resonant frequency of the antenna is shifted to lower frequencies,thereby significantly enhancing the operational ***,the inclusion of three end-fire directing units positioned in front of the folded dipole oscillator substantially improves the antenna's end-fire *** designed antenna exhibits a relative impedance bandwidth of 46%(ranging from 1.36 to 2.18 GHz),with a peak gain of 7.33 dBi at the 2100 MHz 5G-R frequency *** overall dimensions of the antenna are 0.31λ_(L)×0.39λ_(L)×0.008λ_(L),whereλ_(L)denotes the wavelength at the lowest *** proposed antenna demonstrates a broad operational bandwidth,rendering it suitable for 5G-R mobile communications.

关键词： End-fire antenna Dipole resonator Wideband High-gain Intercalated Coupling Structures(ICS)

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Adaptive transmission Modes Selection and Resource Allocation in Wi-Fi Networks

Adaptive Transmission Modes Selection and Resource Allocatio...

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IEEE Conference on Wireless Communications and Networking

作者： Yang Lin Rong He Chuang Wang Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The IEEE 802.11ax and 802.11be standards support multiple data transmission modes, including orthogonal frequency division multiple access (OFDMA), single-user multiple input multiple output (SU-MIMO), multi-user MIMO (MU-MIMO), and combination of OFDMA and MU-MIMO transmission mode. One of the significant challenges of improving system performance is how to select proper transmission mode and allocate resources based on the characteristics of transmission modes and quality of service (QoS) requirements of different traffic types. In this paper, we propose an adaptive algorithm for selecting data transmission modes. The optimization goal is to maximize the sum of user satisfactions degree, namely system satisfaction degree, subject to the delay requirements of latency-sensitive traffics. We adopt the genetic algorithm to obtain the decision of transmission mode selection. Moreover, we design the resource unit (RU) and spatial stream allocation algorithms to maximize resource utilization ratio. The simulation results show that the proposed algorithm can adaptively select suitable transmission modes for the scheduled users, and provide frequency and stream resources allocation. Compared to three baseline algorithms, the system satisfaction and throughput can be improved significantly,

关键词： Adaptive systems Simulation OFDM Quality of service Throughput Delays Resource management Data communication Wireless fidelity Genetic algorithms

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Resource Allocation for Uplink CSI Sensing Report in Multi-User WLAN Sensing

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Journal of Beijing Institute of Technology 2022年第5期31卷 524-534页

作者： Yifei Li Jilei Yan Yan Long Xuming Fang Rong He Key Laboratory of Information Coding and Transmission Southwest Jiaotong UniversityChengdu 611756China 54th Research Institute of CETC Shijiazhuang 050002China

Sensing in wireless local area network(WLAN) gains great interests recently. In this paper we focus on the multi-user WLAN sensing problem under the existing 802.11 standards. Multiple stations perform sensing with the access point and transmit channel state information(CSI)report simultaneously on the basis of uplink-orthogonal frequency division multiple access(OFDMA). Considering the transmission resource consumed in CSI report and the padding wastage in OFDMA based CSI report, we optimize the CSI simplification and uplink resource unit(RU)allocation jointly, aiming to balance the sensing accuracy and padding wastage performances in WLAN sensing. We propose the minimize padding maximize efficiency(MPME) algorithm to solve the problem and evaluate the performance of the proposed algorithm through extensive simulations.

关键词： wireless local area network(WLAN)sensing channel state information(CSI)simplification padding wastage resource allocation

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Modeling of Multi-Link Synchronous Access with Threshold of Waiting Time in Wi-Fi 7

Modeling of Multi-Link Synchronous Access with Threshold of ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

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

ISBN: (纸本)9798350368369

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. © 2025 IEEE.

关键词： Markov processes

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Adaptive transmission Modes Selection and Resource Allocation in Wi-Fi Networks

Adaptive Transmission Modes Selection and Resource Allocatio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Lin, Yang He, Rong Wang, Chuang Fang, Xuming Southwest Jiaotong University Key Laboratory of Information Coding and Transmission Chengdu China

ISBN: (纸本)9798350368369

关键词： Wi-Fi

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