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Found 9 papers, 3 papers with code
Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing
In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions.
Power Optimization in Multi-IRS Aided Delay-Constrained IoVT Systems
However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption.
Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing
To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchain-enabled UC-MEC system where blockchain operations and resource optimization are jointly performed.
Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC
To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS.
Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective
In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC).
Energy Efficiency Optimization in IRS-Enhanced mmWave Systems with Lens Antenna Array
To address this problem, we propose an intelligent reflect surface (IRS) enhanced multi-user mmWave communication system with lens antenna array.
Joint Passive Beamforming and User Association Optimization for IRS-assisted mmWave Systems
Considering the impact of IRS on user association, we formulate a sum rate maximization problem by jointly optimizing the passive beamforming at IRS and user association, which is an intractable non-convex problem.
MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration
Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization.
Learning Deterministic Policy with Target for Power Control in Wireless Networks
DRL-DPT overcomes the main obstacles in applying reinforcement learning and deep learning in wireless networks, i. e. continuous state space, continuous action space and convergence.
