A Centralized and Scalable Uplink Power Control Algorithm in Low SINR Scenarios

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). Investigations in [1] proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g. tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small-and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.Comment: Accepted by IEEE Transactions on Vehicular Technolog

A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks

This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of the most common energy-efficient power control problems with a complexity that, although still exponential in the number of variables, is much lower than other available global optimization frameworks. Moreover, the reduced complexity of the proposed framework allows its practical implementation through the use of deep neural 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. This is in contrast with other power control methods based on deep learning, which train the neural network based on suboptimal power allocations due to the large complexity that generating large training sets of optimal power allocations would have with available global optimization methods. As a benchmark, we also develop a novel first-order optimal power allocation algorithm. Numerical results show that a neural network can be trained to predict the optimal power allocation policy.Comment: submitte

Systematic review of energy theft practices and autonomous detection through artificial intelligence methods

Energy theft poses a significant challenge for all parties involved in energy distribution, and its detection is crucial for maintaining stable and financially sustainable energy grids. One potential solution for detecting energy theft is through the use of artificial intelligence (AI) methods. This systematic review article provides an overview of the various methods used by malicious users to steal energy, along with a discussion of the challenges associated with implementing a generalized AI solution for energy theft detection. In this work, we analyze the benefits and limitations of AI methods, including machine learning, deep learning, and neural networks, and relate them to the specific thefts also analyzing problems arising with data collection. The article proposes key aspects of generalized AI solutions for energy theft detection, such as the use of smart meters and the integration of AI algorithms with existing utility systems. Overall, we highlight the potential of AI methods to detect various types of energy theft and emphasize the need for further research to develop more effective and generalized detection systems, providing key aspects of possible generalized solutions