Radio resource allocation using genetic algorithm in heterogeneous networks

Kao, Shang‐Juh; Hsiao, Kuo-Chan; Chang, Fu‐Min

doi:10.1002/dac.4089

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The loop cycle of this method is as following: initialization of population, fitness function, selection, reproduction, and convergences 184 . The performance of the heterogeneous network improved in Reference 185 for 5G mobile networks using the GA method. Some other specifications that showed improvements using the GA technique include the crossover ratio, mutation ratio, and chromosome length, which are explained in References 186‐189.…”

Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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“…The merits of neural networks applications attracted many researchers. For example, in [30] a radio resource allocation using a genetic algorithm (GA) in heterogeneous networks is proposed to optimize user association and resource block allocation and realize multi-QoS objectives. Some resource allocation solutions are especially designed for vehicular scenarios, for example [32], where interference level of neighboring vehicles is counted in the scheduling function.…”

Section: Related Workmentioning

confidence: 99%

Delay-Oriented Resource Allocation for OFDMA Real-Time Mobile Broadband Services

Madi¹,

Najim²

2023

Int. J. Interact. Mob. Technol.

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Radio Resource Management (RRM) is the key component that influences the Quality of Service (QoS) in emerging mobile systems. The harsh requirements of recent multimedia applications press on the behavior of the resource allocation model at the Medium Access Control (MAC). This, under certain conditions, deteriorates awareness of the scheduler in satisfying QoS characteristics for the diverse attending traffic flows. The trade-off between QoS metrics may be a viable remedy to this problem. However, if the tradeoff is not meticulously addressed, it definitely increases the delay beyond the allowed threshold and plummets the data rates of the involved flows. In this article, a Delay-oriented Resource Allocation (DoRA) scheduler is proposed for the downlink channel to transmit multimedia applications. The main aim of DoRA is to schedule flows of different volumes with guaranteed low delay values and acceptable throughput under harsh network conditions (i.e., high traffic load and mobility). An efficient priority weight function is formulated based on a delay-oriented rule considering the buffer delay of the diverse flows. Eventually, a greedy algorithm assigns channel resources to the prioritized flows with high weights. DoRA is compared to reference schedulers using system-level simulation over two scenarios. The performance results indicate that DoRA maintains a low end-to-end delay with robust throughput and efficient spectrum efficiency for different flow volumes over congested network states.

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“…Resource management enhances heterogeneous 5G networks' performance. A genetic algorithm is presented in Kao et al 28 that provides user association and effective allocation of the resource block. Here, throughput and fairness are considered as QoS measures based on channel quality indicators.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource allocation in 5G network using Orthogonal Frequency Division Multiplexing‐Hybrid Automatic Repeat Request

Jadhav¹,

Dongre²,

Purandare

2022

Int J Communication

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Fading, interference, and collisions cause unpredictable data loss in a wireless channel. In the recent past, the use of polar codes for error detection and correction scaled downed the data error rate considerably. With the introduction of 5G mobile network, minimizing the data loss and enhancing the performance becomes imperative. Here, the use of polar codes based OFDM-HARQ mechanism with CRC is simulated and implemented. The proposed technique was tested for various fading channels. A bundling mechanism is used to attain the optimal number of retransmissions to accomplish better frame dropping ratio and throughput. It has been observed that the bit error rate of the proposed system has improved by optimizing Eb/No and bandwidth. At this optimum setting, the bit error rate has been enhanced by 25%. At the same time, the system optimizes retransmission count to a value of three, which improves frame error rate at the desirable bit error rate of 10 À6 . Further, the use of the proposed optimized polar code reduces complexity computations from one third to one fifth as compared to conventional system modeling using turbo codes. An enhancement in frame dropping ratio by 8% to 12% as well as throughput by 10.23% to 14.33% is achieved. The work carried on the hardware system shows that data loss in the OFDM-HARQ communication system can be minimized by improving the bit error rate and throughput required for device-to-device communication in the 5G network.

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Radio resource allocation using genetic algorithm in heterogeneous networks

Cited by 6 publications

References 16 publications

Overview of evolutionary algorithms and neural networks for modern mobile communication

Overview of evolutionary algorithms and neural networks for modern mobile communication

Delay-Oriented Resource Allocation for OFDMA Real-Time Mobile Broadband Services

Resource allocation in 5G network using Orthogonal Frequency Division Multiplexing‐Hybrid Automatic Repeat Request

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