Beamspace-MIMO-NOMA Enhanced mm-Wave Wireless Communications: Performance Optimization

Majeed, Ammar A.; Hburi, Ismail

doi:10.1109/csase51777.2022.9759687

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…The Power of noise is represented as no. The achievable rate for the proximal user (R 1n ) and the distant user (R 2n ) are determined by the subsequent formulas of Shannon Capacity [3,22]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…NOMA has emerged as a seminal technology to address these burgeoning challenges. Unlike conventional Orthogonal Multiple Access (OMA) schemes, NOMA enables the concurrent utilization of identical time-fre-quency resources by multiple users, thereby substantially augmenting both SE and system connectivity [3,4]. MIMO-NOMA systems, which integrate MIMO technology into NOMA, further enhance system capacity and efficiencies, providing an advanced solution for next-gen wireless networks [5].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Problem Formulationmentioning

confidence: 99%

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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“…Ref. [12] developed a simple iterative technique that, after some iterations, achieves a performance that is nearly on par with that of the ideal. This technique, which is based on the Mean Square-Error Dynamic Power Allocation Algorithm, offers an improvement in energy efficiency of roughly 85% when compared to the standard OFDM systems' EE.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the study in [12], which optimizes the rate of NOMA systems subject to the minimum rate (min_QoS) required by individual users, this study maximizes the rate of NOMA systems with power-allocation constraint employing a simple approach for this task. This work focuses on the rate balance of beam space NOMA systems.…”

Section: Introductionmentioning

confidence: 99%

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

Salih Al Ammar,

Hburi

2024

ejuow

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The multi-input-multi-output-antenna Scheme is regarded as the most promising method for next-generation wireless communication networks that employ millimeter-wave (mm-wave) frequencies and enable them to have a large information throughput. As a result of the large number of radio frequency chains, MIMO mm-wave technology has difficulty with energy consumption. The beam space MIMO (or BS-MIMO for short) reduces the total number of radio frequency components or chains (RF-Chains) without degrading performance significantly. Yeti, VS-MIMO restricts the number of clients that can be supported at the same time-frequency domain (cannot exceed the overall RF-chains number ). In order to cope with such problems, it has been intended to include the NOMA technique (which stands for Non-Orthogonal-Multiple-Access) with the concept of beam space to create the extra model of Non-Orthogonal beam space-MIMO (NOMA-BS-MIMO). With such a scheme the base station can support the service for more than one client (supposed to have correlated channels) through the same beam employing the same radio frequency chain, causing the total clients number greater than the number of employed radio chains in the system. In this research, the Spectral Efficiency of a proposed Non-Orthogonal beam space-MIMO has been considered. More specifically, the research proposes and develops a simple iterative algorithm with near-perfect performance in terms of system Spectral Efficiency. To achieve rate balancing across users, the proposed method, SLNR-IV, employs the signal-to-leakage-plus-noise ratio (SLNR) and the intermediate value method. Considering algorithm validity and a particular parameter setting scenario, the proposed strategy improves bandwidth efficiency by around 15% in comparison to the conventional approach based on OFDM and Zero-forcing digital precoder.

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“…The outer layer converts the original fractional objective function by using the Dinkelbach method, while the inner layer utilizes alternating optimization to solve the transformed problem. In [ 8 ], the authors introduce a low-complexity iterative algorithm called mean square error-based dynamic power allocation algorithm (MSE-DPA), which achieves near-perfect performance. Ref.…”

Section: Introductionmentioning

confidence: 99%

Efficient Precoding and Power Allocation Techniques for Maximizing Spectral Efficiency in Beamspace MIMO-NOMA Systems

Liu,

Si,

Wang

et al. 2023

Sensors

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Beamspace MIMO-NOMA is an effective way to improve spectral efficiency. This paper focuses on a downlink non-orthogonal multiple access (NOMA) transmission scheme for a beamspace multiple-input multiple-output (MIMO) system. To increase the sum rate, we jointly optimize precoding and power allocation, which presents a non-convex problem. To solve this difficulty, we employ an alternating algorithm to optimize the precoding and power allocation. Regarding the precoding subproblem, we demonstrate that the original optimization problem can be transformed into an unconstrained optimization problem. Drawing inspiration from fraction programming (FP), we reconstruct the problem and derive a closed-form expression of the optimization variable. In addition, we effectively reduce the complexity of precoding by utilizing Neumann series expansion (NSE). For the power allocation subproblem, we adopt a dynamic power allocation scheme that considers both the intra-beam power optimization and the inter-beam power optimization. Simulation results show that the energy efficiency of the proposed beamspace MIMO-NOMA is significantly better than other conventional schemes.

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Beamspace-MIMO-NOMA Enhanced mm-Wave Wireless Communications: Performance Optimization

Cited by 5 publications

References 17 publications

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

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

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

Efficient Precoding and Power Allocation Techniques for Maximizing Spectral Efficiency in Beamspace MIMO-NOMA Systems

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