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Found 4 papers, 1 papers with code
Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach
The problem of joint control of the RIS's amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS.
A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation
Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, without blur kernels estimation.
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications
Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL).
Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users
Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks.
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