Power Allocation in Downlink Non-orthogonal Multiple Access IoT-enabled Systems: A Particle Swarm Optimization Approach

Pliatsios, Dimitrios; Sarigiannidis, Panagiotis

doi:10.1109/dcoss.2019.00085

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…This paper extends our earlier work, presented in [35], by solving (a) the user-channel matching problem using a heuristic matching algorithm and (b) the power allocation problem using an evolutionary algorithm, namely particle swarm optimization (PSO) [36]. The proposed heuristic matching algorithm, produces a stable matching between two disjoint sets of users and channels, while the PSO algorithm near-optimally distributes the power budget to the channels.…”

Section: Novelty Of This Work and Structure Of The Papermentioning

confidence: 72%

“…The Gini index ranges from 0, corresponding to the maximum fairness level, to 1, corresponding to the minimum level of fairness among users. We evaluate the proposed user-channel matching PSO (UCM-PSO) method, by comparing it with the User-subchannel matching algorithm (USMA) proposed in [16], our previously proposed extensive tabu search PSO (ETS-PSO) method [35], as well as with the conventional OMA scheme. Figure 3 depicts the achieved system throughput as a function of the number of channels, assuming that the number of deployed users is K = 10.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In particular, we proposed a three-step method that solves the user-channel matching and power allocation problems. In order to evaluate the performance of the proposed method, we performed extensive simulations and compared the results with the USMA proposed in [16], our previous method presented in [35] and the conventional OMA scheme. The results indicate that our proposed method outperforms all the compared ones in terms of system fairness and average number of unconnected users.…”

Section: Discussionmentioning

confidence: 99%

“…A simulation environment in Matlab was developed, where the aforementioned scenario was designed to evaluate the proposed method against the methods proposed in [16,35], as well as the conventional OMA scheme. The final results were derived by taking the average values from running 10 4 simulations.…”

Section: System Model Parametersmentioning

confidence: 99%

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Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Pliatsios

Sarigiannidis

2019

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The ever-increasing requirement of massive connectivity, due to the rapid deployment of internet of things (IoT) devices, in the emerging 5th generation (5G) mobile networks commands for even higher utilization of the available spectrum. Non-orthogonal multiple access (NOMA) is a promising solution that can effectively accommodate a higher number of users, resulting in increased spectrum utilization. In this work, we aim to maximize the total throughput of a NOMA system, while maintaining a good level of fairness among the users. We propose a three-step method where the first step matches the users to the channels using a heuristic matching algorithm, while the second step utilizes the particle swarm optimization algorithm to allocate the power to each channel. In the third step, the power allocated to each channel is further distributed to the multiplexed users based on their respective channel gains. Based on extensive performance simulations, the proposed method offers notable improvement, e.g., 15% in terms of system throughput and 55% in terms of user fairness.

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Section: Novelty Of This Work and Structure Of The Papermentioning

confidence: 72%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: System Model Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Pliatsios

Sarigiannidis

2019

Information

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“…A fitness function is defined for energy efficiency and its performance evaluated through simulations. A PSO motivated power allocation technique for downlink NOMA IoT enabled systems is presented in [19]. The performance of the designed PSO approach is compared to conventional PA methods such as equal power allocation and water-filling.…”

Section: Related Workmentioning

confidence: 99%

Biological Resource Allocation Algorithms for Heterogeneous Uplink PD-SCMA NOMA Networks

Sefako

Walingo

2020

IEEE Access

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Due to their ability to multiplex users on a resource element (RE), Non-orthogonal multiple access (NOMA) techniques have gained popularity in 5G network implementation. The features of 5G heterogeneous networks have necessitated the development of hybrid NOMA schemes combining the merits of the individual NOMA schemes for optimal performance. The hybrid technologies on 5G networks make complex air interfaces resulting in new resource allocation (RA) and user pairing (UP) challenges aimed at limiting the multiplexed users interference. Furthermore, common analytical techniques for evaluating the performance of the schemes lead to unrealistic network performance bounds necessitating alternative schemes. This work explores the feasibility of a hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). The scheme integrates both power and code domain multiple access on an uplink network of small cell user equipments (SUEs) and macro cell user equipments (MUEs). Alternative biological RA/UP schemes; the ant colony optimization (ACO), particle swarm optimization (PSO) and a hybrid adaptive particle swarm optimization (APASO) algorithms, are proposed. The performance results indicate the developed APASO outperforming both the PSO and ACO in sum rate and energy efficiency optimization on application to the PD-SCMA based heterogeneous network.

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Battery and Energy Management in UAV‐Based Networks

Kumar

Vasudeva

Sood

2021

Unmanned Aerial Vehicles for Internet of Things (IoT)

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Power Allocation in Downlink Non-orthogonal Multiple Access IoT-enabled Systems: A Particle Swarm Optimization Approach

Cited by 12 publications

References 23 publications

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Biological Resource Allocation Algorithms for Heterogeneous Uplink PD-SCMA NOMA Networks

Battery and Energy Management in UAV‐Based Networks

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