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| 1 | A Joint Power and Bandwidth Allocation Method Based on Deep Reinforcement Learning for V2V Communications in 5G显示文摘Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system.When maintaining the high reliability and low latency in the vehicle-to-vehicle(V2V)links as well as large capacity in the vehicle-to-infrastructure(V2I)links,it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication.This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method.In this radio resources allocation framework,a vehicle or V2V link that is designed as an agent.And through interacting with the environment,it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level.Because the proposed method can output continuous actions and multi-dimensional actions,it greatly reduces the implementation complexity of large-scale communication scenarios.The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications. | Xin Hu Sujie Xu Libing Wang Yin Wang Zhijun Liu Lexi Xu You Li Weidong Wang | 2021 | China Communications2021，18，7： | 1 |
| 2 | QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence显示文摘Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization. | Chaowei Wang Xiaofei Yu Lexi Xu Fan Jiang Weidong Wang Xinzhou Cheng | 2023 | China Communications2023，20，3： | 0 |
| 3 | Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO显示文摘The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network.With the popularity of content-centric networks,mobile users are paying more and more attention to the delay and reliability of fetching cached content.For reducing communication costs,increasing network capacity and improving the content delivery,we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multipleoutput(MIMO)system,in which the macro base station is considered as the central processor unit,and the roadside units are treated as roadside access points(RSAPs).The proposed scheme can effectively cache contents in edge nodes,i.e.,RSAPs and vehicles with caching capability.We jointly consider the mobility of vehicles and the content request preferences of users,then we use deep Qnetworks algorithm to optimize the caching decisions.Simulation results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio. | WANG Chaowei WANG Ziye XU Lexi YU Xiaofei ZHANG Zhi WANG Weidong | 2023 | Chinese Journal of Electronics2023，32，6： | 0 |
| 4 | Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey显示文摘Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications. | Kai Chen Qinglei Kong Yijue Dai Yue Xu Feng Yin Lexi Xu Shuguang Cui | 2022 | China Communications2022，19，1： | 0 |
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