Transmit power policies for stochastic stabilisation of multi-link wireless networked control systems
Transmit power control is one of the most important issues in wireless networks, where nodes typically operate on limited battery power. Reducing communicating power consumption is essential for both economic and ecologic reasons. In fact, transmitting at unnecessarily high power not only reduces node lifetime, but also introduces excessive interference and electromagnetic pollution. Existing work in the wireless community mostly focus on designing transmit power policies by taking into account communication aspects like quality of service or network capacity. Wireless networked control systems (WNCSs), on the other hand, have different and specific needs such as stability, which require transmit power policies adapted to the control context. Transmit power design in the control community has recently attracted much attention, and available works mostly consider linear systems or specific classes of non-linear systems with a single-link view of the system. In this paper, we propose a framework for the design of stabilising transmit power levels that applies to much larger classes of non-linear plants, controllers, and multi-link setting. By exploiting the fact that channel success probabilities are related to transmit power in a non-linear fashion, we first derive closed-loop stability conditions that relate channel probabilities with transmission rate. Next, we combine these results together with well-known and realistic interference models to provide a design methodology for stabilising transmit power in non-linear and multi-link WNCSs.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GIFT: Global stabilisation via Intrinsic Fine Tuning
Deep reinforcement learning policies achieve strong performance in complex continuous control environments with nonlinear contact forces. However, these policies often produce chaotic state dynamics, with trivially small…
Reinforcement LearningContinuous ControlOn Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback
We consider a network, tasked with solving binary distributed detection, consisting of N sensors, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is eq…
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. In the proposed approach, for a given EH ne…
Deep LearningReal-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond
Efficient sampling and remote estimation are critical for a plethora of wireless-empowered applications in the Internet of Things and cyber-physical systems. Motivated by such applications, this work proposes decentraliz…
Decision MakingSchedulingPower Control and Frequency Band Selection Policies for Underlay MIMO Cognitive Radio
We study power control and frequency band selection policies for multi-band underlay MIMO cognitive radio with the objective of maximizing the rate of a secondary user (SU) link while limiting the interference leakage to…