Joint Power Allocation and Hybrid Beamforming for Downlink mmWave-NOMA Systems

Pang, Lihua; Wu, Wenjie; Zhang, Yang; Yuan, Yin; Chen, Yijian; Wang, Anyi; Li, Jiandong

doi:10.1109/tvt.2021.3103762

Cited by 19 publications

(19 citation statements)

References 30 publications

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“…Here we will discuss the proposed approach to solve the joint power optimization and beamformer design problem in Eq. (7). We have utilized the Deep Reinforcement Learning (DRL) approach to introduce the optimized power and beamformer weights.…”

Section: Joint Power Allocation and Beamformer Designmentioning

confidence: 99%

“…Moreover, in [6], the joint problem was considered but the optimization was only performed on the most powerful path in the channel. Further, in [7] joint power allocation and beamforming were optimized by considering Signalto-Leakage-plus-Noise (SLNR) in order to decouple the power allocation and beamforming. On the contrary to all the mentioned prior works, we have used newly introduced machine learning (ML) tools to optimize the joint power allocation and beamformer design in mmW-NOMA.…”

mentioning

confidence: 99%

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Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

Akbarpour-Kasgari¹,

Ardebilipour²

2022

Preprint

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The high demand for data rate in the next generation of wireless communication could be ensured by Non-Orthogonal Multiple Access (NOMA) approach in the millimetre-wave (mmW) frequency band.Joint power allocation and beamforming of mmW-NOMA systems is mandatory which could be met by optimization approaches. To this end, we have exploited Deep Reinforcement Learning (DRL) approach due to policy generation leading to an optimized sum-rate of users. Actor-critic phenomena are utilized to measure the immediate reward and provide the new action to maximize the overall Q-value of the network. The immediate reward has been defined based on the summation of the rate of two users regarding the minimum guaranteed rate for each user and the sum of consumed power as the constraints.The simulation results represent the superiority of the proposed approach rather than the Time-Division Multiple Access (TDMA) and another NOMA optimized strategy in terms of sum-rate of users.

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Section: Joint Power Allocation and Beamformer Designmentioning

confidence: 99%

mentioning

confidence: 99%

Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

Akbarpour-Kasgari¹,

Ardebilipour²

2022

Preprint

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show abstract

“…Moreover, in [16], the joint problem was considered but the optimization was only performed on the most powerful path in channel. Further, in [17] joint power allocation and beamforming was optimized with considering Signal-to-Leakage-plus-Noise (SLNR) in order to decouple the power allocation and beamforming. In the contrary to all the mentioned prior works, we have used newly introduced machine learning (ML) tools to optimize the joint power allocation and beamformer design in mmW-NOMA.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming

Akbarpour-Kasgari¹,

Ardebilipour²

2022

Preprint

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High demand of data rate in the next generation of wireless communication could be ensured by Non-Orthogonal Multiple Access (NOMA) approach in the millimetre-wave (mmW) frequency band. Decreasing the interference on the other users while maintaining the bit rate via joint power allocation and beamforming is mandatory to guarantee the high demand of bit-rate. Furthermore, mmW frequency bands dictates the hybrid structure for beamforming because of the trade-off in implementation and performance, simultaneously. In this paper, joint power allocation and hybrid beamforming of mmW-NOMA systems is brought up via recent advances in machine learning and control theory approaches called Deep Reinforcement Learning (DRL). Actor-critic phenomena is exploited to measure the immediate reward and providing the new action to maximize the overall Q-value of the network.Additionally, to improve the stability of the approach, we have utilized Soft Actor-Critic (SAC) approach where overall reward and action entropy is maximized, simultaneously. The immediate reward has been defined based on the soft weighted summation of the rate of all the users. The soft weighting is based on the achieved rate and allocated power of each user. Furthermore, the channel responses between the users and base station (BS) is defined as the state of environment, while action space is involved of the digital and analog beamforming weights and allocated power to each user. The simulation results represent the superiority of the proposed approach rather than the Time-Division Multiple Access (TDMA) and Non-Line of Sight (NLOS)-NOMA in terms of sum-rate of the users. It's outperformance is caused by the joint optimization and independency of the proposed approach to the channel responses.

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“…However, due to the random generation of the initial analog combination precoder during the alternating optimization, it requires many iterations to achieve better performance, which leads to high computational complexity. Using the signal-to-leakage-and-noise ratio (SLNR) as the performance index, the authors of reference [19] jointly optimized the analog-digital hybrid precoding and power allocation to maximize the spectral efficiency of the system. The authors of [20] proposed effective alternating minimization algorithms based on the zero-gradient method to establish fully connected structures.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [20] proposed effective alternating minimization algorithms based on the zero-gradient method to establish fully connected structures. Although the algorithms in references [19,20] have improved the system performance to some extent, the implementation complexity is high and the fully connected structure also results in higher hardware costs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Precoding Based on Partial Connection for Millimeter-Wave Massive MIMO System

et al. 2022

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Aiming at poor system performance in existing partially connected hybrid precoding schemes, in this paper, we propose a partially connected hybrid precoding scheme based on alternating optimization with the auxiliary precoder. In particular, the spectral efficiency optimization problem is transformed into a sub-optimization problem under certain constraints. For each sub-optimization problem, we use the singular value decomposition method to optimize the analog precoder and digital precoder alternately, and optimal solutions are provided for both digital and analog precoders in each alternate iteration. Moreover, we design a heuristic algorithm to calculate the initial analog precoder as the starting point for alternating optimization, which speeds up the convergence speed. The simulation results show that the proposed algorithm is at least 16.2% higher than the existing two traditional partially connected algorithms at 20 dB received SNR with almost no increase in complexity.

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Joint Power Allocation and Hybrid Beamforming for Downlink mmWave-NOMA Systems

Cited by 19 publications

References 30 publications

Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming

Hybrid Precoding Based on Partial Connection for Millimeter-Wave Massive MIMO System

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