Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation

2019-10-29 · Ankita Tondwalkar, Dr Andres Kwasinski

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. The presented algorithm is the first deep reinforcement learning technique for which convergence to equilibrium policies can be shown in the non-stationary multi-agent environment that results from the uncoordinated dynamic interaction between radios through the shared wireless environment. Moreover, simulation results show that in a finite learning time the presented technique is able to find policies that yield performance within 3 % of an exhaustive search solution, finding the optimal policy in nearly 70 % of cases. Moreover, it is shown that standard single-agent deep reinforcement learning may not achieve convergence when used in a non-coordinated, coupled multi-radio scenario.

📄 PDF Abstract BibTeX arXiv:1911.03366

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation

2022-05-27 · Ankita Tondwalkar, Andres Kwasinski

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents during learning may …

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio

2022-04-09 · Jithin Jagannath, Kian Hamedani, Collin Farquhar, Keyvan Ramezanpour 외

Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in this domain. In most cases…

Deep Reinforcement Learning GPU OpenAI Gym

Smoothing Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios

2020-12-14 · CUHK Course IERG5350 2020 12 · Wanli Wang

The spectrum sharing in a cognitive radio system is related with a secondary user sharing common spectrum with a primary user for power transmit without induc- ing harmful inference. The deep reinforcement learning has b…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Destination-Feedback Free Distributed Transmit Beamforming using Guided Directionality

2021-08-04 · Samer Hanna, Enes Krijestorac, Danijela Cabric

Distributed transmit beamforming enables cooperative radios to act as one virtual antenna array, extending their communications' range beyond the capabilities of a single radio. Most existing distributed beamforming appr…

Distributed Transmit Beamforming: Design and Demonstration from the Lab to UAVs

2021-10-26 · Samer Hanna, Danijela Cabric

Cooperating radios can extend their communication range by adjusting their signals to ensure coherent combining at a destination radio. This technique is called distributed transmit beamforming. Beamforming (BF) relies o…