Joint Beamforming and Power Control for MIMO-NOMA with Deep Reinforcement Learning

Lu, Tongwei; Zhang, Haijun; Long, Keping

doi:10.1109/icc42927.2021.9500713

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…In MIMO-NOMA, THz communication can provide a maximum transmission rate of 10 GB/s which is very useful in present wideband technology. 12 However, THz communication suffers highly due to high attenuation loss, absorption loss and lack of high-speed data rate for broadband communication systems. 13 To overcome this, pre-coders can boost signal strength by eradicating attenuation in the wireless channel.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, these bands act as a barrier to interference and maintain strong security between the users. In MIMO‐NOMA, THz communication can provide a maximum transmission rate of 10 GB/s which is very useful in present wideband technology 12 . However, THz communication suffers highly due to high attenuation loss, absorption loss and lack of high‐speed data rate for broadband communication systems 13 .…”

Section: Introductionmentioning

confidence: 99%

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Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Rameshchandra

Borugadda

2023

Trans Emerging Tel Tech

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Multiple input multiple output (MIMO) plays an integral role in improving energy efficiency for enhancing all users' quality of service (QoS). For 5G wireless communication (WC), the NOMA strategy is used with the MIMO system to improve the system's sum rate and computational complexity. In this, terahertz (THz) band communication is introduced for future demand to carry more users and obtains high multiplexing gain. Multiple antenna arrays are used in the MIMO‐NOMA system to achieve high signal transmission. However, the use of antenna arrays increases as the number of users increases. A beam‐forming‐based signal transmission is introduced in this work to overcome this issue. For performing digital and Analog beam‐forming, a hybrid minimum mean square error (MMSE) with zero‐forcing (ZF) pre‐coder is proposed to eliminate the inter‐cluster interference. Initially, user clustering is undertaken using a fuzzy k‐means clustering (FKM) algorithm, and beam‐forming is performed among the user clustering. The joint power allocation (PA) and resource allocation (RA) is done using a hybrid artificial gorilla troop with leader optimization (HGTLO) technique to achieve a maximum sum rate of the RF channel. The performance of a proposed method is analyzed using the MATLAB tool. In the experimental scenario, the performance of energy efficiency (EE), spectral efficiency (SE), achievable sum rate, throughput, bit error rate (BER), and mean square error (MSE) attains the value of 35.82 bits/Hz/Joules, 198.11 bps/Hz, 116.78 Mbps, 93.06%, .003 and .011 respectively. The performance measures prove the efficacy of a proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Rameshchandra

Borugadda

2023

Trans Emerging Tel Tech

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“…Multiple input multiple output (MIMO) system with digital beamforming is investigated, 14 where the clustering algorithm for users with correlated channel gain and uncorrelated channel gain is discussed by assuming a fixed threshold for the correlation coefficient. The authors of the article 17 shared the same channel for each cluster, which is formed by two users from different groups. Zero forcing (ZF) based digital beamforming is designed to remove inter‐cluster interference.…”

Section: Introductionmentioning

confidence: 99%

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

Sumathi

Ramesh

Ding

2023

Trans Emerging Tel Tech

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Non-orthogonal multiple access (NOMA) is a most promising candidate in future wireless networks to improve user fairness and spectral / energy efficiency in multiuser scenarios. In this work, we investigate a clustered multi-user millimeter wave (mmWave) downlink NOMA system with a hybrid beamforming architecture. In most existing research with dynamic user clustering, the number of users in each cluster varies from one to any number. A single user in a cluster corresponds to orthogonal multiple access (OMA) case, which is not beneficial. At the same time, clusters with more than one user employ the NOMA technique. However, the main limitation of NOMA with a large number of users is the successive interference cancellation (SIC) decoding complexity which increases with the number of users. Moreover, this also causes a delay in decoding the data at the users. This problem is addressed in our proposed algorithm, where each cluster has a minimum of two users to a maximum of a fixed number of users. Together with the dynamic clustering, a hybrid beamforming vector is also designed. Then, a single-step power allocation algorithm using teaching-learning based optimization (TLBO) to maximize sum spectral efficiency is proposed, which is less complex than the existing suboptimal methods.

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“…Reference [17] the use of RL in managing resources within wireless communication networks, specifically in a single-cell MIMO-NOMA network. The researchers address the challenge of optimizing the total sum rate, a problem complicated by its non-convex nature.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems

A. Majeed,

Ali Saed,

Hburi

2023

Int. j. electr. comput. eng. syst. (Online)

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In this paper, we investigate the performance enhancement of Multiple Input, Multiple Output, and Non-Orthogonal Multiple Access (MIMO-NOMA) wireless communication systems using an Artificial Intelligence (AI) based Q-Learning reinforcement learning approach. The primary challenge addressed is the optimization of power allocation in a MIMO-NOMA system, a complex task given the non-convex nature of the problem. Our proposed Q-Learning approach adaptively adjusts power allocation strategy for proximal and distant users, optimizing the trade-off between various conflicting metrics and significantly improving the system’s performance. Compared to traditional power allocation strategies, our approach showed superior performance across three principal parameters: spectral efficiency, achievable sum rate, and energy efficiency. Specifically, our methodology achieved approximately a 140% increase in the achievable sum rate and about 93% improvement in energy efficiency at a transmitted power of 20 dB while also enhancing spectral efficiency by approximately 88.6% at 30 dB transmitted Power. These results underscore the potential of reinforcement learning techniques, particularly Q-Learning, as practical solutions for complex optimization problems in wireless communication systems. Future research may investigate the inclusion of enhanced channel simulations and network limitations into the machine learning framework to assess the feasibility and resilience of such intelligent approaches.

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Joint Beamforming and Power Control for MIMO-NOMA with Deep Reinforcement Learning

Cited by 10 publications

References 13 publications

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems

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