Cross-Layer Design for Green Power Control

In this work, we propose a new energy efficiency metric which allows one to optimize the performance of a wireless system through a novel power control mechanism. The proposed metric possesses two important features. First, it considers the whole power of the terminal and not just the radiated power. Second, it can account for the limited buffer memory of transmitters which store arriving packets as a queue and transmit them with a success rate that is determined by the transmit power and channel conditions. Remarkably, this metric is shown to have attractive properties such as quasi-concavity with respect to the transmit power and a unique maximum, allowing to derive an optimal power control scheme. Based on analytical and numerical results, the influence of the packet arrival rate, the size of the queue, and the constraints in terms of quality of service are studied. Simulations show that the proposed cross-layer approach of power control may lead to significant gains in terms of transmit power compared to a physical layer approach of green communications.Comment: Presented in ICC 201

A Flow Level Perspective on Base Station Power Allocation in Green Networks

International audienc

A Flow Level Perspective on Base Station Power Allocation in Green Networks

International audienc

Realistic Energy Saving Potential of Sleep Mode for Existing and Future Mobile Networks

Abstract—This paper presents an extensive overview on an energy saving feature referred to as ‘site sleep mode’, designed for existing and future mobile broadband networks. In addition to providing a detailed understanding of the main concept, the paper also provides various studies and results to highlight potential savings, and emphasize some of the expected limitations. Since site measurements show that the energy consumption of base station sites is largely load-independent, this makes such a feature highly effective for reducing the energy consumption of mobile networks during hours of low traffic. After going through a number of different alternatives of the feature, this is applied to different network topologies, macro-only based networks, and a set of heterogeneous networks that employ the use of small cells in traffic hotspots. Results obtained through detailed case studies show that sleep mode can reduce the average daily energy consumption of a network by around 30%. This can be achieved while maintaining a predefined level of performance, used as a measure of comparing different scenarios. Index Terms—sleep mode, energy efficiency, case study, HSPA, LTE, base station, energy model, power model, heterogeneous network, femtocells, picocells I

Reducing fuel consumption in platooning systems through reinforcement learning

Fuel efficiency in platooning systems is a central topic of interest because of its significant economic and environmental impact on the transportation industry. In platoon systems, Adaptive Cruise Control (ACC) is widely adopted because it can guarantee string stability while requiring only radar or lidar measurements. A key parameter in ACC is the desired time gap between the platoon's neighboring vehicles. A small time gap results in a short inter-vehicular distance, which is fuel efficient when the vehicles are moving at constant speeds due to air drag reductions. On the other hand, when the vehicles accelerate and brake a lot, a bigger time gap is more fuel efficient. This motivates us to find a policy that minimizes fuel consumption by conveniently switching between two desired time gap parameters. Thus, one can interpret this formulation as a dynamic system controlled by a switching ACC, and the learning problem reduces to finding a switching rule that is fuel efficient. We apply a Reinforcement Learning (RL) algorithm to find a time switching policy between two desired time gap parameters of an ACC controller to reach our goal. We adopt the proximal policy optimization (PPO) algorithm to learn the appropriate transient shift times that minimize the platoon's fuel consumption when it faces stochastic traffic conditions. Numerical simulations show that the PPO algorithm outperforms both static time gap ACC and a threshold-based switching control in terms of the average fuel efficiency