Dynamic User Pairing and Power Allocation for NOMA with Deep Reinforcement Learning

Jiang, Fan; Gu, Zesheng; Sun, Changyin; Ma, Rongxin

doi:10.1109/wcnc49053.2021.9417564

Cited by 14 publications

(10 citation statements)

References 6 publications

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“…Previous research showed RL in NOMA has been investigated by focusing on maximizing the sum rate but not EE [135], [136]. In another research but not NOMA related, RL has been implemented in IoT applications where Ashiquzzaman et al [137] create ultra-durable and energy efficient IoT for sensor calibration to decrease consumption and increase device efficiency.…”

Section: Resultsmentioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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<p>Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.</p>

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Section: Resultsmentioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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“…To overcome the user pairing issues, optimization techniques, game theory, machine learning and deep learning algorithms are proposed in the literature. The authors proposed an optimization method while pairing two users [ 45 , 46 , 47 ]. To optimize the user pairing, the channel gain should not be less than the predefined threshold.…”

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

confidence: 99%

“…Through Q-learning, they were able to successfully implement both power allocation and user pairing with reduced computational complexity. In [ 47 ], the authors introduced an optimal power allocation technique with a given sub-channel assignment through a closed-form approach. Considering this, a traditional deep reinforcement learning (DRL) algorithm named Deep Q-Network (DQN) algorithm is used to investigate the optimal user pairing scheme.…”

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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“…The received signal at the BS may be represented as, y = β a Ω t z a s a + β b Ω t z b s b + N in a two-user NOMA situation, where Ω t is the available transmission power, β a and β b are the two users’ power split factors, s a and s b are the transmitted signals, and N is the BS’s AWGN. 56–58 A two-user U/L situation is depicted in Figure 3.…”

Section: System Modelmentioning

confidence: 99%

“…This section establishes the link between the user pairing in NOMA and the DRL scenario. 56 As illustrated in Figure 5, 56 the required compounds of the DRL scenario—including the agent, environment, condition, action, recompense, policy and objective—are described as follows:…”

Section: Drl For Users Pairing Nomamentioning

confidence: 99%

Analysis of user pairing non-orthogonal multiple access network using deep Q-network algorithm for defense applications

Ravi¹,

Kulkarni

Ray

et al. 2022

Journal of Defense Modeling & Simulation

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Non-orthogonal multiple access (NOMA) networks play an important role in defense communication scenarios. Deep learning (DL)-based solutions are being considered as viable ways to solve the issues in fifth-generation (5G) and beyond 5G (B5G) wireless networks, since they can provide a more realistic solution in the real-world wireless environment. In this work, we consider the deep Q-Network (DQN) algorithm-based user pairing downlink (D/L) NOMA network. We have applied the convex optimization (CO) technique and optimized the sum rate of all the wireless users. First, the near-far (N-F) pairing and near-near and far-far (N-N and F-F) pairing strategies are investigated for the multiple numbers of users, and a closed-form (CF) expression of the achievable rate is derived. After that, the optimal power allocation (OPA) factors are derived using the CO technique. Through simulations, it has been demonstrated that the DQN algorithms perform much better than the deep reinforcement learning (DRL) and conventional orthogonal frequency-division multiple access (OFDMA) schemes. The sum-rate performance significantly increases with OPA factors. The simulation results are in close agreement with the analytical results.

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Dynamic User Pairing and Power Allocation for NOMA with Deep Reinforcement Learning

Cited by 14 publications

References 6 publications

Energy-efficient non-orthogonal multiple access for wireless communication system

Energy-efficient non-orthogonal multiple access for wireless communication system

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

Analysis of user pairing non-orthogonal multiple access network using deep Q-network algorithm for defense applications

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