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Autonomous Driving via Brain-Inspired Causality-Aware Contrastive Learning with Time-Frequency Prediction

IEEE Internet of Things Journal 2025年

作者： Wang, Chengyu Wang, Luhan Lu, Zhaoming Wu, Celimuge Shou, Guochu Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China Meta-Networking Research Center The University of Electro-Communications Tokyo182-8585 Japan

Developing trustworthy reinforcement learning (RL) agents for safety-critical control tasks, such as end-to-end autonomous driving, has been a longstanding challenge due to low sample efficiency. Prior works have attempted to address this challenge by performing self-supervised auxiliary tasks like self-reconstruction or predicting long-term future states. However, there still remain unexplored sequential features and causality relationships inherent in sequential state, action, and reward signals in the frequency domain. To fully exploit the temporal and frequential features, we propose a contrastive RL framework called BRain-Inspired causalitY-Aware coNTrastive learning (BRYANT) to achieve efficient representation learning and human-like autonomous driving. Different from existing temporal predictive methods, we transform the sequential latent representations, reward, and action signals into the frequency domain, followed by the symmetric temporal prediction pattern for real and imaginary parts of the frequential signals. To capture the temporal causality for the latent representations, we introduce a brain-inspired network structure called Closed-form Continuous-time (CfC) network to parameterize the derivative of the latent representations and establish the neural dynamic model. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of BRYANT in efficient representation learning, enabling agents to concentrate on potential risks and decrease the collision rate compared to several state-of-the-art RL methods. Furthermore, through the visualization of the latent representation prediction process, we reveal the causal relationships between the critic Q values and the latent representation vectors in the frequency domain, and demonstrate the effectiveness of the frequency domain prediction. © 2014 IEEE.

关键词： Reinforcement learning

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Demonstration of Time Redundancy Mechanism for Deterministic Communications over Hybrid Networks

Demonstration of Time Redundancy Mechanism for Deterministic...

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OptoElectronics and Communications Conference, OECC

作者： Hongxing Li Guochu Shou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

We propose a time redundancy mechanism to enhance the determinism of 5G TSN hybrid networks, and experimentally demonstrate the most cost-effective enhancement occurs when frame is replicated four times with 42% maxim... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： 5G mobile communication Redundancy Jitter

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Autonomous Driving via Knowledge-Enhanced Safe Reinforcement Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Wang, Chengyu Wang, Luhan Lu, Zhaoming Zhou, Shuang Wu, Celimuge Wen, Xiangming Shou, Guochu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Meta-Networking Research Center The University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo Japan

Recently, the autonomous driving technology is at a critical phase evolving from typical, closed scenarios to largescale, open driving scenarios, which is challenged by the diversity and complexity of the driving environment. Reinforcement learning has the potential to address this challenge, but its application has been hindered by safety issues. To address the safety concern, we propose a Lagrangian framework called Knowledge-Enhanced Safe Reinforcement Learning (KESRL), which integrates prior knowledge of model-based methods into the model-free safe RL framework. Firstly, a model-based constraint based on high-order control barrier function is derived to deduce the safety criteria for the control system. Secondly, we propose a model-free constraint which estimates the distribution of accumulated safety cost and restricts the worst-case safety cost value. Furthermore, the above constraints are unified into the proposed Lagrangian framework to equip the end-to-end autonomous driving system with safety constraints. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of the proposed framework KESRL in decreasing the collision rate and enhancing the control safety compared with several state-of-the-art RL methods. IEEE

关键词： Autonomous vehicles

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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SINR-Adaptive and CBR-Controllable Semantic Cellular Communication Considering Imperfect CSI and Inter-Cell Co-channel Interference

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IEEE Transactions on Cognitive Communications and networking 2024年

作者： Niu, Haiwen Wang, Luhan Lu, Zhaoming Wu, Celimuge Wen, Xiangming Beijing University Of Posts And Telecommunications Beijing Key Laboratory Of Network System Architecture And Convergence Beijing Laboratory Of Advanced Information Networks Beijing100876 China The University Of Electro-Communications Meta-Networking Research Center 1-5-1 Chofugaoka Chofu-shi Tokyo182-8585 Japan

Deep learning (DL)-enabled semantic communications offer promising prospects for higher data transmission efficiency. However, most existing studies focus on point-to-point transmissions with perfect channel state information (CSI) and fixed channel bandwidth ratio (CBR). This paper investigates multi-point transmission in cellular networks with spectrum resource sharing and proposes a signal-to-interference plus noise ratio (SINR) adaptive and CBR-controllable semantic cellular communication system (SACC). In this system, we design a semantic encoder by a Transformer-convolutional neural network (CNN) mixture block to capture non-local and local latent image features simultaneously and a SINR-adaptive module based on the channel-spatial soft attention mechanism to scale image features according to SINR conditions. A model adaption strategy is proposed to handle varying inter-cell co-channel interference proportions under the same SINR. Additionally, a semantic-channel encoder-decoder forwarding paradigm, termed semantic translation, enables dynamic networking among intra-cell users. Extensive simulation results show that the proposed SACC achieves graceful degradation and better image reconstruction performance versus the current engineered "JPEG+LDPC+QAM"and DeepJSCC benchmarks across different wireless environments and CBRs. Furthermore, experimental results demonstrate substantial improvements in FLOPs, model sizes, and computing latency over the state-of-the-art ADJSCC and WITT with comparable performance. © 2015 IEEE.

关键词： Bandwidth

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Precise Timing over Beyond 5G Networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Yu, Rong Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Nanning530001 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China School of Automation Guangdong University of Technology Guangzhou510006 China

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground base station (GBS) or unmanned aerial vehicle (UAV), which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Extensive analysis and numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services 16

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Zhang, Tao Xu, Changqiao Zhang, Bingchi Kuang, Xiaohui Wang, Yue Yang, Shujie Muntean, Gabriel-Miro State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728131290

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, Network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution. © 2020 IEEE.

关键词： Reinforcement learning

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Tao Zhang Changqiao Xu Bingchi Zhang Xiaohui Kuang Yue Wang Shujie Yang Gabriel-Miro Muntean State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

关键词： Routing Streaming media Machine learning Frequency modulation Computer crime Convergence Quality of service

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