Resource allocation for NOMA downlink systems: Genetic algorithm approach

Gemici, Omer Faruk; Kara, Fatih; Hökelek, İbrahim; Kurt, Güneş Karabulut; Çırpan, Hakan Ali

doi:10.1109/tsp.2017.8075948

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…However, the above algorithms did not consider the complexity while pursuing fairness. So, the fairness and complexity were weighed [22]- [24]. Genetic algorithm is a powerful heuristic algorithm that can quickly converge to the solution, which can balance the system throughput and user fairness of multi-user NOMA downlink system [22].…”

Section: A Existing Research On Nomamentioning

confidence: 99%

“…So, the fairness and complexity were weighed [22]- [24]. Genetic algorithm is a powerful heuristic algorithm that can quickly converge to the solution, which can balance the system throughput and user fairness of multi-user NOMA downlink system [22]. In order to avoid unnecessary comparisons of candidate users, in [23], Liu et al addressed preconditions for user pairing.…”

Section: A Existing Research On Nomamentioning

confidence: 99%

“…(1) Adopt heuristic algorithm, such as the genetic algorithm mentioned in the literature [14], [22], particle swarm algorithm, etc. This type of algorithm does not have a strict theoretical basis, it is inspired by people's actual life experience and rules of things.…”

Section: B Optimization Problem Formulationmentioning

confidence: 99%

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Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems

Long

Wang

et al. 2019

IEEE Access

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In this paper, we investigate the spectrum resource and power allocation problem for the tradeoff between maximizing the sum rate and minimum rate requirements of users in non-orthogonal multiple access (NOMA) system. First, we formulate the NOMA techniques, basic principles, and double-objective optimization (DOO) problem. Then, the non-convexity of the DOO problem is converted into a single-objective optimization (SOO) problem by power discretization method. Global optimal search (GOS) algorithm is applied to solve the user-subchannel matching and power allocation problem. Due to its high complexity and unfairness among users, it is only suitable for determining the upper bound of users throughput performance. Finally, yet importantly, a spectrum resource and power allocation algorithm with adaptive proportional fair (APF) user pairing is proposed to convert the original optimization problem into user pairing, sub-channel, and power allocation. The users paired on the sub-channel are determined by the scheduling priority which is based on the equivalent channel gain. The BS dynamically adjusts the forgetting factor in the APF algorithm based on the variance of all the users' scheduling priorities so as to influence the update of users' scheduling weights. The power allocation stage proposes three power allocation schemes to ensure the users' minimum data rate requirements under the condition that effectively guarantees the correct execution of successive interference cancellation (SIC). The simulation results demonstrate that it can not only approach the throughput performance compared with the global optimal search and the classical water-filling (WF) power allocation using matching theory but also can improve the fairness of the users.INDEX TERMS Non-orthogonal multiple access (NOMA), spectrum resource and power allocation, global optimal search (GOS), adaptive proportional fair (APF) user pairing, fairness.

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Section: A Existing Research On Nomamentioning

confidence: 99%

Section: A Existing Research On Nomamentioning

confidence: 99%

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Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems

Long

Wang

et al. 2019

IEEE Access

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“…In contrast, when the number of users increased, the average rate loss for the closer user to BS and average rate gain for the farther user BS is decreased for the WF-PF algorithm in [9] . 6 depicts the increment of the geometric mean of the user throughput when three users are multiplexed per subcarrier using a genetic algorithm (GA) approach with using an optimized power level [16]. In addition, the proposed scheme executes a random user grouping in NOMA system.…”

Section: Performance Analysismentioning

confidence: 99%

Throughput Analysis for Non-Orthogonal Multiple Access (NOMA)-Based 5G Networks

2019

IJRTE

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Non-Orthogonal Multiple Access (NOMA) scheme is one of the emerging radio access techniques to enhance the system performance for 5G networks. The powerdomain NOMA is one of the basic NOMA schemes that perform superposition coding (SC) at the transmitter and successive interference cancellation (SIC) at the receiver. Power Allocation (PA) plays a significant role in attaining successful SIC and high system throughput. This work is focusing on power allocation to maximize the throughput for NOMA-based 5G network. The objective functions, algorithms, constraints and limitations of the system design in power allocation techniques for NOMA-based 5G networks in terms of throughput analysis are extensively investigated and reported.

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“…In the LTE OFDMA system, GA utilized to learn antennas coverage pattern which leads to enhance the capacity of the system and decrease the network interference [21]. In the downlink NOMA system, a resource allocation algorithm using GA is proposed for pairing users that share the same frequency resource with an optimal power allocation strategy [22], results show that through the proposed algorithm a fast coverage to the target solution is achieved. On the other hand, GA utilized for power allocation in [23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA)

Alghasmari¹,

Nassef²

2021

IJACSA

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Fifth generation of wireless cellular networks promise to enable better services anytime and anywhere. Nonorthogonal multiple access (NOMA) stands as a suitable multiple accessing scheme due to the ability to allow multiple users to share the same radio resource simultaneously via different domains (power, code, etc.). Through the introduced power domain, users multiplexed at the radio resource within different power levels. This paper studies power allocation in downlink NOMA, an optimization problem formulated that aims to maximize the system's sum rate. To solve the problem, a genetic algorithm based power allocation (GAPA) was proposed that uses genetic algorithm (GA) that employs heuristics to search for suitable solutions. The performance of the proposed power allocation algorithm compared with full search power allocation (FSPA) that gives an optimal performance. Results show that GAPA reaches a performance near to FSPA with lower complexity. In addition, GAPA simulated with various user paring algorithms. Channel state sorting based user pairing with GAPA achieves the best performance comparing to random user pairing algorithm and exhaustive user pairing.

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Resource allocation for NOMA downlink systems: Genetic algorithm approach

Cited by 14 publications

References 13 publications

Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems

Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems

Throughput Analysis for Non-Orthogonal Multiple Access (NOMA)-Based 5G Networks

Optimal Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA)

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