CSVAG: Optimizing Vertical Handoff Using Hybrid Cuckoo Search and Genetic Algorithm-Based Approaches

Jha, Keshav; Gupta, Akhil; Alabdulatif, Abdulatif; Tanwar, Sudeep; Safirescu, Călin Ovidiu; Mihălţan, Traian Candin

doi:10.3390/su14148547

Cited by 12 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section aims to familiarize the reader with the algorithms and demonstrate how they may be applied in CRN issues by metaheuristic methods, such as GA, PSO, cuckoo search, max feeding optimization, ant colony optimization, and artificial bee colony, may be found in the contemporary literature. The SVM and RDA were chosen for this inquiry because they have demonstrated the most outstanding performance in cases comparable to identifying a spectral opportunity in CRNs [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.

show abstract

Section: Methodsmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…It combines two optimization techniques, Cuckoo Search and Genetic Algorithm, to find the most efficient handoff decision. This helps enhance wireless networks' overall performance and efficiency [30]. Power optimization with low complexity using a scaled Beamforming approach for a massive MIMO and small cell scenario is a technique that uses advanced signal processing algorithms to reduce power consumption in communication systems with many antennas and small cells.…”

Section: Functions Of Antenna Systemsmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

View full text Add to dashboard Cite

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

show abstract

“…A new hybrid cuckoo search (CS) and GA that maximises the capacity of heterogeneous wireless networks in terms of lowering latency, increasing throughput and reducing HO failure probability was proposed in Jha et al [71]. The performance of the suggested system is encouraging for situations where it is necessary to optimise the handoff mechanisms to limit regular HO as well as cut down on the power consumption of user equipment.…”

Section: Intelligent Techniques In Wireless Networkmentioning

confidence: 99%

An intelligent load balancing algorithm for 5G‐satellite networks

Bello,

Pillai,

Sadiq

2024

Satell Commun Network

View full text Add to dashboard Cite

SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

show abstract

CSVAG: Optimizing Vertical Handoff Using Hybrid Cuckoo Search and Genetic Algorithm-Based Approaches

Cited by 12 publications

References 56 publications

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

An intelligent load balancing algorithm for 5G‐satellite networks

Contact Info

Product

Resources

About