Optimal aggregated ConvergeCast scheduling with an SINR interference model

Bakshi, Mahesh; Jaumard, Brigitte; Narayanan, Lata

doi:10.1109/wimob.2017.8115744

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…The achievable DL data rate of vehicles during movement in UDNs is calculated according to Shannon’s equation, as expressed in Equation ( 19 ). The signal-to-interference-plus-noise ratio (SINR), which is denoted by

, is the ratio of the received signal to the interference from other wireless BSs plus noise [ 56 ].

The maximum transmission power of small BSs is denoted as

and the path loss function is represented by

, which is defined in Section 3.5 .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Alablani

Arafah

2021

Sensors

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The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

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, is the ratio of the received signal to the interference from other wireless BSs plus noise [ 56 ].

The maximum transmission power of small BSs is denoted as

and the path loss function is represented by

, which is defined in Section 3.5 .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The achievable DL data rate of vehicles during movement in UDNs is calculated according to Shannon's equation, as expressed in Equation (19). The signal-to-interference-plusnoise ratio (SINR), which is denoted by ζ kj , is the ratio of the received signal to the interference from other wireless BSs plus noise [56].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Alablani

Arafah

2021

Sensors

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“…The signal-to-interference-plus-noise ratio is calculated by dividing the strength of the signal received from the serving BS by the sum of interference power from other BSs and the power of the noise [71]. SINR at vehicle V j associated with the base station B i , which is expressed as γ ij , can be calculated as given in Equation ( 27).…”

Section: B Evaluation Of the Proposed A2t-boost 1) Evaluation Metricsmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

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“…Further study of the SINR network model has shown that an SINR model of interference is a more realistic model of interference than the protocol model of interference: a receiver node receives a packet so long as the signal to interference plus noise ratio is above a certain threshold. See, Bakshi et al [2].…”

Section: Introductionmentioning

confidence: 99%

Local large deviation principle, large deviation principle and information theory for the signal-to-interference-plus-noise ratio graph models

Sakyi-Yeboah

Asiedu

Doku-Amponsah

2020

Journal of Information and Optimization Sciences

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The article obtains large deviation asymptotic for sub-critical communication networks modelled as signal-interference-noise-ratio(SINR) random networks. To achieve this, we define the empirical power measure and the empirical connectivity measure, as well as prove joint large deviation principles(LDPs) for the two empirical measures on two different scales. Using the joint LDPs, we prove an Asymptotic equipartition property(AEP) for wireless telecommunication Networks modelled as the subcritical SINR random networks. Further, we prove a Local Large deviation principle(LLDP) for the sub-critical SINR random network. From the LLDPs, we prove the large deviation principle, and a classical McMillan Theorem for the stochastic SINR model processes. Note that, the LDPs for the empirical measures of this stochastic SINR random network model were derived on spaces of measures equipped with the τ − topology, and the LLDPs were deduced in the space of SINR model process without any topological limitations. We motivate the study by describing a possible anomaly detection test for SINR random networks.

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Optimal aggregated ConvergeCast scheduling with an SINR interference model

Cited by 7 publications

References 18 publications

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Local large deviation principle, large deviation principle and information theory for the signal-to-interference-plus-noise ratio graph models

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