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Found 10 papers, 1 papers with code
Energy Efficiency Maximization in RIS-Aided Networks with Global Reflection Constraints
This work addresses the issue of energy efficiency maximization in a multi-user network aided by reconfigurable intelligent surface (RIS) with global reflection capabilities.
Synergistic Benefits in IRS- and RS-enabled C-RAN with Energy-Efficient Clustering
Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases.
Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT
As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI).
Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends
Future wireless networks are expected to evolve towards an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices.
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach
In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and analytically show that the MFG has a unique stationary solution.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Deep Learning Based Online Power Control for Large Energy Harvesting Networks
In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems.
Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase.
A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks
Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation.
User Association and Load Balancing for Massive MIMO through Deep Learning
This work investigates the use of deep learning to perform user cell association for sum-rate maximization in Massive MIMO networks.
Deep Learning Power Allocation in Massive MIMO
The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods.
