Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches

Doan, Khai Nguyen; Vaezi, Mojtaba; Shin, Wonjae; Poor, H. Vincent; Shin, Hyundong; Quek, Tony Q. S.

doi:10.1109/tcomm.2019.2947418

Cited by 63 publications

(28 citation statements)

References 22 publications

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“…Therefore, for the same state and action pair, the reward could be different in successive trials. A simple approach employed to get stable reward values is to execute successive trials and take the trials' average reward [35]. We determine the average reward values by executing consecutive trials for each state and action pair.…”

Section: Architecture Of the Dqn And Other Design Considerationsmentioning

confidence: 99%

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

2020

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Section: Architecture Of the Dqn And Other Design Considerationsmentioning

confidence: 99%

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

2020

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“…Thus, we can further reduce the computational complexity of the proposed algorithm by a relatively lower number of iterations, which seems feasible to an extent for practical applications. Compared with optimization techniques, deep learning-based method has gained widespread popularity recently to deal with the resource allocation problem for NOMA systems in a non-iterative fashion, which has been demonstrated to be able to achieve good performance with reasonable training time [39,40,41]. However, it is out of the scope of this work and it will be a topic for our further study.…”

Section: Remarkmentioning

confidence: 99%

“…f,m according to (38), (39), (40), and (41) under the step size constraint of (42), (43), (44), and (45). as well as the power allocation alternatively, the iteration process is terminated when the convergence of the optimal power update is guaranteed or the maximum number of iterations is reached.…”

mentioning

confidence: 99%

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

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A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.<br>

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“…Some researchers have applied DL to NOMA systems [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. In [ 15 ], a DL-aided sparse code multiple access (SCMA) was proposed in which the mapping of data to the resource and the decoding of received signals is conducted with a deep neural network (DNN).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

Lee

2020

Sensors

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In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).

show abstract

Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches

Cited by 63 publications

References 22 publications

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

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