Online mobile user speed estimation: Performance and tradeoff considerations

Haddad, Majed; Herculea, Dalia Georgiana; Chen, Chung Shue; Altman, Eitan; Capdevielle, Véronique

doi:10.1109/ccnc.2017.7983265

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…In [12], the author presented that the general mobility problem has been addressed by the Mobile Internet Protocol (MIP) with the switching domain to be MPLS. In [13], the author proposed an online scheme for UE mobility estimation in 4G-LTE systems focused on TSSM as it had low execution complexity. The proposed algorithm can permit the speed estimation to be done within 10 seconds and with an average speed assessment error of 15 km/h.…”

Section: Related Workmentioning

confidence: 99%

Tracking and Controlling High-Speed Vehicles Via CQI in LTE-A Systems

Janaby¹,

Al-Omary²,

Ameen³

et al. 2020

IJCDS

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One of the most serious problems facing the community around the world is car accidents. These accidents occur mainly due to the high-speed of vehicles. Thus, the paper aims to capture, track, and control high-speed vehicles using LTE-A mobile networks to avoid high-speed situations as well as decrease the number of accidents. The paper assumes that all vehicle drivers are now days carrying their mobiles that can be considered as mobile network user equipment (UE). This paper presents an innovative tracking and controlling high-speed vehicles in the LTE-A system that taking the advantages of Channel Quality Indicator (CQI) value mapped to the UE speed. The method can be accomplished by uploading the CQI index to the base station (BS), at the uplink, then the evolved node base station (eNB) sends an extra warning message at the downlink to initiate the radio frequency identifier (RFID) component fixed on the vehicle. The proposed scheme design assumes that the LTE networks have the all traffic speed for the covered area and to be activated when the speed is beyond the maximum speed. In that case, the RFID is activated and an alarm is switched on. Under now response, the RFID will activate the vehicle's traction control (TC), Engine Control Unit (ECU) and automatic brake system (ABS) to decrease the speed gradually. The proposed scheme was simulated using the system level-simulator (SLS) and the performance is depicted. The evaluations show that the CQI values are decreased meaningfully to 2 when the UE movement in the high-way increases to 150 km/h. Consequently, with the obtainability of CQI values at the LTE-A system, an immediate activity is completed to control the vehicle speed and warn the driver.

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Section: Related Workmentioning

confidence: 99%

Tracking and Controlling High-Speed Vehicles Via CQI in LTE-A Systems

Janaby¹,

Al-Omary²,

Ameen³

et al. 2020

IJCDS

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“…We obtain the following interesting properties of the optimal power division (from ( 12)): 1) allocated power increases with the speed range of users in a class (users with lower Υ i get higher power), which in turn implies an increased virtual cell; 2) the disparity in the allocated powers between different classes increases with cell size, the disparity in speeds and the path-loss factor. Thus from (12) in a medium with large (path) losses, one needs to allocate larger power to high speed users in order to improve the overall system performance. Intuitively otherwise, large speed users hold on to the system resources for longer time periods which can deteriorate the performance of the low speed users.…”

Section: Finite Number Of User Classesmentioning

confidence: 99%

“…Remarks: From (18), we notice that the optimal power law is linear in the speed. In fact even from discrete case (12), it is proportional to 1/Υ i which approximately equals a speed v ∈ I i if interval I i is sufficiently thin. This is surprising for us, as we anticipated that the power law will be scaled by factors proportional to path-loss factor β.…”

Section: Average Of the Overall Service Time Bmentioning

confidence: 99%

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Speed based optimal power control in small cell networks

Kavitha

Gupta

Capdevielle

et al. 2019

Computer Communications

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Small cell networks promise good quality of service (QoS) even for cell edge users, however pose challenges to cater to the high-speed users. The major difficulty being that of frequent handovers and the corresponding handover losses, which significantly depend upon the speed of the user. It was shown previously that the optimal cell size increases with speed. Thus, in scenarios with diverse users (speeds spanning over large ranges), it would be inefficient to serve all users using common cell radius and it is practically infeasible to design different cell sizes for different speeds. Alternatively, we propose to allocate power to a user based on its speed, e.g., higher power virtually increases the cell size. We solve well known Hamiltonian Jacobi equations under certain assumptions to obtain a power law, optimal for load factor and busy probability, for any given average power constraint and cell size. The optimal power control turns out to be linear in speed. We build a system level simulator for small cell network, using elaborate Monte-Carlo simulations, and show that the performance of the system improves significantly with linear power law. The power law is tested even for the cases, for which the system does not satisfy the assumptions required by the theory. For example, the linear power law has significant improvement in comparison with the 'equal power' system, even in presence of time varying and random interference. We observe good improvement in almost all cases with improvements up to 89% for certain configurations.

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