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Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement learning

Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement learning
Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement learning
In order to improve the energy efficiency (EE) in the underlay cognitive radio (CR)networks, a power allocation strategy based on an actor-critic reinforcement learning is proposed, where a cluster of cognitive users (CUs) can simultaneously access to the same primary spectrum band under the interference constraints of the primary user (PU), by employing the non-orthogonal multiple access (NOMA) technique. In the proposed scheme, the optimization of the power allocation is formulated as a non-convex optimization problem. Additionally, the power allocation for different CUs is based on the actor-critic reinforcement learning model, in which the weighted data rate is set as the reward function, and the generated action strategy (i.e. the power allocation) is iteratively criticized and updated. Both the CU's spectral efficiency and the PU's interference constraints are considered in the training of the actor-critic reinforcement learning. Furthermore, the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions. According to the simulation results, we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method, while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.
1673-5447
66 - 79
Liang, Wei
9576aa89-5e9a-489f-ae64-1f30628d3514
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Shi, Jia
592123d1-83e1-46ea-a158-619bb74a72d8
Li, Lixin
31d41958-daec-4a24-a9f5-0f30122b1dd2
Wang, Dawei
f46e04f1-fca0-4387-b41d-d045a96f76db
Liang, Wei
9576aa89-5e9a-489f-ae64-1f30628d3514
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Shi, Jia
592123d1-83e1-46ea-a158-619bb74a72d8
Li, Lixin
31d41958-daec-4a24-a9f5-0f30122b1dd2
Wang, Dawei
f46e04f1-fca0-4387-b41d-d045a96f76db

Liang, Wei, Ng, Soon Xin, Shi, Jia, Li, Lixin and Wang, Dawei (2020) Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement learning. China Communications, 17 (12), 66 - 79. (doi:10.23919/JCC.2020.12.005).

Record type: Article

Abstract

In order to improve the energy efficiency (EE) in the underlay cognitive radio (CR)networks, a power allocation strategy based on an actor-critic reinforcement learning is proposed, where a cluster of cognitive users (CUs) can simultaneously access to the same primary spectrum band under the interference constraints of the primary user (PU), by employing the non-orthogonal multiple access (NOMA) technique. In the proposed scheme, the optimization of the power allocation is formulated as a non-convex optimization problem. Additionally, the power allocation for different CUs is based on the actor-critic reinforcement learning model, in which the weighted data rate is set as the reward function, and the generated action strategy (i.e. the power allocation) is iteratively criticized and updated. Both the CU's spectral efficiency and the PU's interference constraints are considered in the training of the actor-critic reinforcement learning. Furthermore, the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions. According to the simulation results, we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method, while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

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Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement - Accepted Manuscript
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Accepted/In Press date: 6 November 2020
e-pub ahead of print date: 1 December 2020

Identifiers

Local EPrints ID: 447452
URI: http://eprints.soton.ac.uk/id/eprint/447452
ISSN: 1673-5447
PURE UUID: 9b9d1187-0685-4a9a-87ac-6336f05a1daa
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194

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Date deposited: 11 Mar 2021 17:37
Last modified: 12 Mar 2021 02:35

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