Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

Recent Progress in Reconfigurable and Intelligent Metasurfaces: A Comprehensive Review of Tuning Mechanisms, Hardware Designs, and Applications

“…[361] Unsupervised Learning: Unsupervised learning is not datahungry as it does not depend on prior knowledge, unlike supervised learning. Therefore, unsupervised learning FNN PHY key generation [349] Reinforcement learning DDPG Sum-rate maximization [331] PDS-PER Secrecy rate enhancement [337] DDPG Coverage rate maximization [342] DDPG Efficient resource allocation [350] MDP Sum-rate maximization [351] Supervised learning ODE-based CNN Performance maximization [352] CNN Sum-rate maximization [353] CV-DnCNN Performance maximization [354] CNN Achievable rate maximization [355] Unsupervised learning RISBFNN Gain enhancement [344] CNN, FNN Sum-rate maximization [356] DNN Spectral efficiency [160] NN Throughput maximization [357] Federated learning CNN Channel estimation [358] CNN Propagation error reduction [359] DNN Energy-efficient [360] algorithms [362] can be applied in RIS-aided wireless systems to overcome challenges such as channel state detection, [363] beamforming, [364] and transmission power control in device-todevice communications. [365] An unsupervised learning algorithm has been proposed for passive beamforming in RIS-aided wireless communication networks.…”

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

“…The MLP-based NN addressing dynamic positioning of multiple RISs to overcome the storage and computational performance limitations [255] Optimized Energy Expenditure…”

“…The fact that the considered scheme does not have a layered structure causes it to lag behind supervised learning performance. However, [255] has demonstrated that a design outperforming supervised learning in terms of both energy and time consumption can be created without the use of a layered structure. The proposed algorithm with trained NNs by the approach of deep unsupervised learning optimizes both energy expenditure and phase configuration to outperform even the Genetic algorithm, which is considered a groundbreaking development in optimization.…”

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

“…The relay selection optimization to reduce propagation loss over distance by the proposed Deep Reinforcement Learning model that can learn from the environment [241] Deep Reinforcement Learning-DNN A method that addresses the challenge in path loss optimization for RIS-aided terahertz communication having high molecular absorption and attenuation [242] Deep Reinforcement Learning-NN Resource allocation for D2D networks and phase shift configuration optimization in terms of achievable rate and computational time performance [219] Deep Reinforcement Learning-DNN Deep Reinforcement Learning approach presenting a fully energy-efficient method by optimizing the ON/OFF state of RIS elements besides transmit power [243] MLP-Position Trained NN The MLP-based NN addressing dynamic positioning of multiple RISs to overcome the storage and computational performance limitations [244] Unsupervised Learning-NN A NN trained by the approach of deep unsupervised learning optimizing both energy expenditure and phase configuration [245] ML/DL-DNN The ML-based approach leveraged by DL techniques has no need for CSI for direct mapping [218] Unsupervised Learning-DNN Proposed algorithm bypassing channel prediction process, requires fewer pilots compared to prior studies with the channel estimation [246] Unsupervised Learning-DNN Proposed deep-transfer learning-based algorithm requesting less sampled data for training process resulting in reduced hardware complexity and training load [247] Deep Reinforcement Learning Deep Reinforcement Learning-based novel architecture capable of learning channel behaviour performance in every cycle thanks to the evaluation of previous rewards for actions, so that enhance optimization of phase matrix. The channel behaviour can be interpreted by evaluating historical line-of-sight path channels to determine optimal phase configuration for further actions.…”

“…The fact that the considered scheme does not have a layered structure causes it to lag behind supervised learning performance. However, [244] has demonstrated that a design outperforming supervised learning in terms of both energy and time consumption can be created without the use of a layered structure. The proposed algorithm with trained NNs by the approach of deep unsupervised learning optimizes both energy expenditure and phase configuration to outperform even the Genetic algorithm, which is considered a groundbreaking development in optimization.…”