Joint Channel Selection and Power Control for NOMA: A Multi-Armed Bandit Approach

Adjif, Mohamed Ali; Habachi, Oussama; Cances, Jean-Pierre

doi:10.1109/wcncw.2019.8902878

Cited by 22 publications

(19 citation statements)

References 17 publications

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“…Controller selects the power allocation action a m t = (p m t , q m t ) according to Equations ( 20), (21).…”

Section: Multi-agent Td3-based Power Allocation (Mtpa) Networkmentioning

confidence: 99%

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

Jo¹,

Jong²,

Pak³

et al. 2021

IET Communications

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NOMA and MIMO are considered to be the promising technologies to meet huge access demands and high data rate requirements of 5G wireless networks. In this paper, the power allocation problem in a downlink MIMO-NOMA system to maximize the energy efficiency while ensuring the quality-of-service of all users is investigated. Two deep reinforcement learning-based frameworks are proposed to solve this non-convex and dynamic optimization problem, referred to as the multi-agent DDPG/TD3-based power allocation framework. In particular, with current channel conditions as input, every single agent of two multi-agent frameworks dynamically outputs the optimum power allocation policy for all users in every cluster by DDPG/TD3 algorithm, and the additional actor network is also added to the conventional multi-agent model in order to adjust power volumes allocated to clusters to improve overall performance of the system. Finally, both frameworks adjust the entire power allocation policy by updating the weights of neural networks according to the feedback of the system. Simulation results show that the proposed multi-agent deep reinforcement learning based power allocation frameworks can significantly improve the energy efficiency of the MIMO-NOMA system under various transmit power limitations and minimum data rates compared with other approaches, including the performance comparison over MIMO-OMA.

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“…Controller selects the power allocation action a m t = (p m t , q m t ) according to Equations ( 20), (21).…”

Section: Multi-agent Td3-based Power Allocation (Mtpa) Networkmentioning

confidence: 99%

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

Jo¹,

Jong²,

Pak³

et al. 2021

IET Communications

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show abstract

“…They formulate the problem as a noncooperative game and transform the nonconvex optimization problem into the convex form by using nonlinear fractional programming and solve the transformed problem by Dinkelbach’s method and Lagrangian duality theory [ 12 ]. Adjif et al adopted a multiarmed bandit-based method (MAB) in the uplink scenario with the goal of optimizing system throughput [ 13 ]. Zheng et al considered user selfishness, and modeled the NOMA uplink power allocation problem as a Nash bargaining game, which is solved by KKT Condition [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

Cui

Zhai

et al. 2021

Sensors

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The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

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“…This allows one to essentially explore at the beginning of the learning and mostly to exploit the best arm found so far after a certain amount of time. The new exploration probability is defined as [ 22 , 31 ]:

where

is the exploration parameter. However, the main challenge of this policy is how to properly set the value of L .…”

Section: Power Allocation With Multi-armed Banditsmentioning

confidence: 99%

Section: -Greedymentioning

confidence: 99%

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Autonomous Power Decision for the Grant Free Access MUSA Scheme in the mMTC Scenario

Ameur

Mary

Hélard

et al. 2020

Sensors

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Non-orthogonal multiple access schemes with grant free access have been recently highlighted as a prominent solution to meet the stringent requirements of massive machine-type communications (mMTCs). In particular, the multi-user shared access (MUSA) scheme has shown great potential to grant free access to the available resources. For the sake of simplicity, MUSA is generally conducted with the successive interference cancellation (SIC) receiver, which offers a low decoding complexity. However, this family of receivers requires sufficiently diversified received user powers in order to ensure the best performance and avoid the error propagation phenomenon. The power allocation has been considered as a complicated issue especially for a decentralized decision with a minimum signaling overhead. In this paper, we propose a novel algorithm for an autonomous power decision with a minimal overhead based on a tight approximation of the bit error probability (BEP) while considering the error propagation phenomenon. We investigate the efficiency of multi-armed bandit (MAB) approaches for this problem in two different reward scenarios: (i) in Scenario 1, each user reward only informs about whether its own packet was successfully transmitted or not; (ii) in Scenario 2, each user reward may carry information about the other interfering user packets. The performances of the proposed algorithm and the MAB techniques are compared in terms of the successful transmission rate. The simulation results prove that the MAB algorithms show a better performance in the second scenario compared to the first one. However, in both scenarios, the proposed algorithm outperforms the MAB techniques with a lower complexity at user equipment.

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Joint Channel Selection and Power Control for NOMA: A Multi-Armed Bandit Approach

Cited by 22 publications

References 17 publications

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

Autonomous Power Decision for the Grant Free Access MUSA Scheme in the mMTC Scenario

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