Time frame optimization using PSO to guarantee QoS in IEEE 802.16 networks

Dosciatti, Eden Ricardo; Foronda, Augusto

doi:10.1109/sai.2015.7237190

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…To solve the optimization problem of (4), we present a new adaptive genetic algorithm and fitness computing function which is presented (6). Let there be a total number of 𝒩 base station and number of 𝒦 subscriber station in each 𝒩.…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

“…As a result, the reliability of WiMAX network performance and QoS are affected. In order to correct this evolutionary computation, [6], [7] was adopted to improve WiMAX QoS. In [6], [7], an evolutionary model was presented to maximize time and adequate allocation of bandwidth for network users.…”

Section: Introductionmentioning

confidence: 99%

“…In order to correct this evolutionary computation, [6], [7] was adopted to improve WiMAX QoS. In [6], [7], an evolutionary model was presented to maximize time and adequate allocation of bandwidth for network users. It does, however, incur overhead computation in user synchronization, as a consequence of which real-time application might not be applicable.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary model to guarantee quality of service for tactical worldwide interoperability for microwave access networks

Huchappa

Patil

2022

IJ-AI

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The smart phone industries evolution had made the growth in wireless communication. The increase in social media usage led to huge increase of network traffic as sharing of data for multimedia like video conferencing, and voice over internet protocol (VoIP). Majority of services like this requires network resources and real-time strict quality of service (QoS). However high cost of network deployment is included. All the major service providers are currently being adopted to the WiMAX 802.16 network. Therefore, the worldwide interoperability for microwave access (WiMAX) network must have different supply policies and QoS for various applications. The implementation of these QoS policies was not provided by WiMAX for various needs of application. Recently development of various scheduling mechanism for QoS provisioning has been made. However, users improper synchronization made these models inefficient. To address this, uplink scheduling with feedback is considered to provision QoS. However, it induces delay in accessing slots, as result the bandwidth is wasted. To utilize bandwidth efficiently evolutionary computing is adopted by various existing model. However, these models induce computation overhead and may not be suitable for provisioning real-time services. The evolutionary computing model is used to compute ideal threshold.

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Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolutionary model to guarantee quality of service for tactical worldwide interoperability for microwave access networks

Huchappa

Patil

2022

IJ-AI

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“…The genetic algorithm is used [25] for setting an ideal value in bringing good tradeoffs in reducing buffer overflow and scheduling newly arrived packet. As discussed in [22] genetic algorithm is very efficient solving multi-objective decision making.…”

Section: Genetic Algorithm With Improved Crossover Mechanismmentioning

confidence: 99%

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

Shankar

Patil

2021

IJ-AI

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For a very long time protecting physical border integrity is considered to be a challenging thing. Government organizations must provide trade operations for economic growth and at the same time must prevent malicious activity. A different resource such as drones, sensors, and radars are used for monitoring border areas which must be communicated to the remote border security force. Efficient wireless communication is required for communicating information. However, these devices cannot connect to a centralized network directly; thus, are connected in an ad-hoc fashion to connect centralized server. Different tactical network applications require different quality of service (QoS); hus efficient resource scheduling plays a very important role. Existing resource scheduling adopting deep learning and reinforcement techniques fails to meet the quality of experience (QoE) of the user and doesn’t assure access fairness among contending users. Further, require network information in prior and induce high training time. For overcoming research issues, this paper presents a delay-aware downlink resource scheduling (DADRA) technique for future generation networks. The optimization problem of reducing buffer overflow and improving scheduling QoS performance is solved using a genetic algorithm with an improved crossover function. Experiment outcome shows DADRA achieves much better throughput, slot utilization, and packet failure performance when compared with standard resource allocation technique.

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“…Thus, the throughput efficiency and QoS of WiMAX network is affecting. To address in [6], [7] presented an evolutionary computing model to enhance QoS of WiMAX network by optimizing time and bandwidth requirement of subscriber. However, their approach is not efficient in synchronizing subscriber.…”

Section: Introductionmentioning

confidence: 99%

Adaptive uplink scheduling model for WiMAX network using evolutionary computing model

Mahesh¹,

Chitnis

2019

IJEECS

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the increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision.

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Time frame optimization using PSO to guarantee QoS in IEEE 802.16 networks

Cited by 6 publications

References 9 publications

Evolutionary model to guarantee quality of service for tactical worldwide interoperability for microwave access networks

Evolutionary model to guarantee quality of service for tactical worldwide interoperability for microwave access networks

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

Adaptive uplink scheduling model for WiMAX network using evolutionary computing model

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