A new multi-rat scheduling algorithm for heterogeneous wireless networks

Mansouri, Wahida; Mnif, Kais; Zarai, Faouzi; Obaidat, Mohammad S.; Kamoun, Lotfı

doi:10.1016/j.jss.2015.02.073

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…A new dynamic scheduling algorithm for heterogeneous wireless network while maintaining performance was proposed by Mansouri et al [17]. The scheduling algorithm was specifically designed on the basis of link conditions of transmission from two distinct types of techniques namely, Media Independent Handover (MIH) and Handoff Call Prioritization.…”

Section: Related Workmentioning

confidence: 99%

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

Dhandapani¹,

Chandrasekar²

2022

I2M

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Prevailing personal mobile network architectures make use of streamlined mobility control system, where the complete understanding is concentrated on single-end that results in scarce of dynamic mobility support when data volume is found to be large. The present-day networks necessitate seamless connections regardless of node position and connectivity that has to be accomplished between personal are network (PAN). In this work, a novel method called, Markov Renewal Prediction and Radial Kronecker Neural Network (MRP-RKNN) based optimized handover for seamless mobility in PAN is proposed. By employing a Markov Renewal Prediction model for Seamless Mobility along with the two-hop network architecture, in this paper, we propose a transition probabilities (TP) function to mitigate the persistent handover issue in conventional wireless communication systems. The proposed Markov Renewal Prediction model for Seamless Mobility significantly reduces handover execution time and seamless mobility handover accuracy with efficient transition probabilities. In PANs, the unavoidable deployment of low power sink nodes permits the mobile nodes with many issues in terms of Quality of Service (QoS) due to complication of recurrent handovers due to high mobility. Addressing this issue of handover optimization in the deployment of PAN, this work proposes a model called to optimize the handovers in a cost-efficient manner. In this work, Radial Kronecker Delta Neural Network is utilized for handling frequent handovers based on received signal strength and cost metrics. Here, the resultant desired output is obtained using the Radial Kronecker function being a function of two variables with which optimized handover is performed. Simulation results presented in the study exhibits the performance and prediction rate of the proposed method in terms of handover execution time, seamless mobility prediction accuracy, mobility handover cost and packet loss rate.

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Section: Related Workmentioning

confidence: 99%

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

Dhandapani¹,

Chandrasekar²

2022

I2M

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“…The inter cellular interference monitoring is suggested for the coordination of network nodes in resource allocation in different wireless network models. In the provision of quality of service a dynamic scheduling is outlined in [6]. In [7] a spectrum sensing and allocation in cognitive radio is presented.…”

Section: International Journal Of Intelligent Engineering and Systemsmentioning

confidence: 99%

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Arikatla¹,

Gavini²,

Nanjappa³

2018

IJIES

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Provision quality of service (QoS) is the need of coming generation networks. Wherein mechanism for the provision of quality of service has emerged in different directions, the effort on minimizing the broadcasting overhead in a setup process is observed to be high. The past development in resource allocation has focused on the optimization of quality of service in the communication process. However, not much emphasis is made on the signaling overhead for multicast signal propagation under dynamic network resource condition. The limitation is observed to be more predominant in dynamic network such as mobile Adhoc network. The link overhead under a dynamic resource variation impact on the channel allocation performance and hence introduces delay. This paper, present an approach of resource allocation under the dynamic network condition for 1-hop and Multihop broadcasting. The proposed approach optimizes the channel allocation by the self-detection of available channel due to additional secondary users and allocates a communication channel based on a sensing mechanism. Wherein, the conventional method uses a fixed level of available channel, the proposed approach gives the advantage of channel allocation with channel sensing under dynamic user condition. The proposed approach illustrates a higher success rate and minimization of average delay under a dynamic network condition.

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“…For horizontal spectrum sharing, recognizing the technology provides extra insights to counteract the interference: e.g., for a Wi-Fi network, if the concurrent technology is LTE-U [ 11 ], it would be beneficial to maximize the usage of the periodical off-time (LTE-U switches off traffic periodically so that Wi-Fi can share the medium). From a broader perspective, real-time technology recognition is essential for the interoperability among Multiple Radio Access Technologies (Multi-RAT) in the NGWN [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

Liu

Kulin

Kazaz

et al. 2017

Sensors

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Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

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A new multi-rat scheduling algorithm for heterogeneous wireless networks

Cited by 4 publications

References 15 publications

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

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