Journal ArticleDOI
A fuzzy reinforcement learning approach to power control in wireless transmitters
D. Vengerov,Nicholas Bambos,H.R. Berenji +2 more
- Vol. 35, Iss: 4, pp 768-778
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TLDR
This work presents a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs, and shows that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms.Abstract:
We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs. Our experimental results show that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms. The main tradeoff facing a transmitter is to balance its current power level with future backlog in the presence of stochastically changing interference. Simulation experiments demonstrate that the ACFRL-2 algorithm achieves significant performance gains over the standard power control approach used in CDMA2000. Such a large improvement is explained by the fact that ACFRL-2 allows transmitters to learn implicit coordination policies, which back off under stressful channel conditions as opposed to engaging in escalating "power wars".read more
Citations
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Journal ArticleDOI
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
TL;DR: The workings of the natural gradient is described, which has made its way into many actor-critic algorithms over the past few years, and a review of several standard and natural actor-Critic algorithms is given.
Book
Power Control in Wireless Cellular Networks
TL;DR: This survey provides a comprehensive discussion of the models, algorithms, analysis, and methodologies in this vast and growing literature of power control in cellular networks, including optimization theory, control theory, game theory, and linear algebra.
Journal ArticleDOI
Quantum Reinforcement Learning
TL;DR: The results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems and shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism.
Proceedings Article
Cellular Network Traffic Scheduling With Deep Reinforcement Learning
Sandeep Chinchali,Pan Hu,Tianshu Chu,Manu Sharma,Manu Bansal,Rakesh Misra,Marco Pavone,Sachin Katti +7 more
TL;DR: This work presents a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic and can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic.
Journal ArticleDOI
Review: Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues
TL;DR: An overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks and how several wireless network schemes have been approached using RL to provide network performance enhancement are discussed.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Neuro-Dynamic Programming.
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Book
Neuro-dynamic programming
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Journal ArticleDOI
A simple distributed autonomous power control algorithm and its convergence
Gerard J. Foschini,Z. Miljanic +1 more
TL;DR: For wireless cellular communication systems, one seeks a simple effective means of power control of signals associated with randomly dispersed users that are reusing a single channel in different cells, and the authors demonstrate exponentially fast convergence to these settings whenever power settings exist for which all users meet the rho requirement.
Book
Actor-critic algorithms
TL;DR: This thesis proposes and studies actor-critic algorithms which combine the above two approaches with simulation to find the best policy among a parameterized class of policies, and proves convergence of the algorithms for problems with general state and decision spaces.