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

Lee, Jaehee; So, Jaewoo

doi:10.3390/s20247094

Cited by 16 publications

(19 citation statements)

References 27 publications

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“…Simulation results demonstrated that the proposed DL-based solution was able to achieve a significant rate advantage with respect to other trivial approaches, such as fixed or random cluster assignments. In [104], the authors proposed an RL-based joint UP and power allocation scheme. By applying Q-learning, it is possible to perform UP and power allocation simultaneously, which was the key novelty of the presented work.…”

Section: Massive Mimo With Nomamentioning

confidence: 99%

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Gkonis

2023

IEEE Access

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The deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the support of highly demanding applications, paving the way towards global broadband connectivity. In the same context, related advances in network infrastructure, such as network function virtualization via the decoupling of related functionalities from hardware equipment has leveraged end-to-end service support in highly heterogeneous environments. To this end, uninterrupted provision of service necessitates a holistic network redesign where access points (APs) should be placed and configured dynamically. One of the key supporting technologies of 5G communications are massive multiple input multiple output (m-MIMO) configurations, deployed over various APs, either in a centralized or in a distributed manner. However, such a configuration would pose an additional degree of complexity during network planning, since on one hand accurate channel estimation for all potential links is limited due to the pilot contamination effect, and on the other hand the number of actually deployed radio frequency chains is again limited when compared to the fully digital approach, in an effort to relax hardware and computational burden. Moreover, additional issues should be taken into consideration as well, such as the coexistence of m-MIMO configurations with other novel technologies in the 5G air interface as well as the need for provision of acceptable quality of service to a large number of mobile users. In an effort to deal with the aforementioned issues, machine learning (ML) algorithms have emerged over the last decade as a promising solution that can deal with complex optimization problems. In this context, the main goal of this survey paper is to present the state-of-the-art approaches in the deployment of ML methods in m-MIMO configurations. Unlike other related survey papers, this is the first scientific attempt to cover all aspects of m-MIMO practical implementations (i.e., channel estimation, MIMO precoding and signal detection, hybrid beamforming, distributed configurations, user pairing and power allocation in non-orthogonal multiple access), in the context of ML-aided calculations. To this end, all presented works are categorized according to the studied aspect, while in the same context potential limitations and future challenges are highlighted as well.INDEX TERMS Massive MIMO, machine learning, channel estimation, hybrid beamforming, cell-free orientations, NOMA.

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Section: Massive Mimo With Nomamentioning

confidence: 99%

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Gkonis

2023

IEEE Access

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“…RL is a prominent strategy for dealing with the problem of uncertain and variable environments [ 24 ]. RL-based strategies for power allocation in an outage compensation situation were put forth in [ 25 , 26 ]. However, one disadvantage of RL-based techniques is that they may take a very long time to converge as they attempt to identify the best answer through the interaction of their environment via trial and error.…”

Section: Related Workmentioning

confidence: 99%

“…Different Game theory algorithms for multiple user pairing and machine learning algorithms for user pairing have been proposed in recent research. In [ 26 ], the authors proposed an RL-enabled joint power allocation and user pairing scheme. Through Q-learning, they were able to successfully implement both power allocation and user pairing with reduced computational complexity.…”

Section: Key Aspects For Practical Implementation Of Dl-based Nomamentioning

confidence: 99%

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Mohsan

Shvetsov

et al. 2023

Sensors

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Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.

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“…Lee J and So J. 24 proposed PA and user pairing based Reinforcement Learning (RL) using MIMO-NOMA. NOMA technique has the higher SE and widely used in 5G WC (Wireless Communication) systems.…”

Section: Related Workmentioning

confidence: 99%

Hybrid cascaded attention-guided refinement network-based optimal power allocation in MIMO-NOMA system

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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The multiple input multiple output (MIMO) plays an integral role in improving the energy efficiency for enhancing the QoS of all users. For the 5G wireless communication (WC), NOMA strategy is used with MIMO system to improve the sum rate as well as the computational complexity of the system. This developed work mainly focuses on emphasizing deep learning based optimal power allocation and user pairing techniques in MIMO-NOMA technology to enhance the sum rate and to reduce computational complexity. Normally, NOMA system is used to improve the spectrum in 5G mobile communication. When NOMA is hybridizing with MIMO system, the spectral efficiency (SE) is highly improved. But user pairing problems arise in NOMA system. To overcome this, deep reinforcement learning approach (DRL) is established to receive data from the user equipment (UE) about CSI and position. To allocate the power in UE, cascaded attention guided refinement network (CAGR-Net) is proposed. In addition to this, tiki taka algorithm (TTA) is manipulated to perform clustering as well as to vary the power levels for optimizing the system overall performance of the proposed method. The performance measures such as energy efficiency (EE), spectral efficiency (SE), channel gain, ergodic sum capacity, MSE and error rate are evaluated. In experimental scenario, the error between user 1 and user 2 attained about 0.1044 and 0.5642 respectively with the SNR of 16dB. The performance of the developed model shows better outcome compared to other existing techniques.

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

Cited by 16 publications

References 27 publications

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Hybrid cascaded attention-guided refinement network-based optimal power allocation in MIMO-NOMA system

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