A novel RSRP-based E-CID positioning for LTE networks

Celik, Gokhan; Celebi, Hasari; Tuna, Gokhan

doi:10.1109/iwcmc.2017.7986538

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…1) State space: Mobility-based BS selection strategies have been intensively studied for vehicular networks, utilizing a UE's location and speed [14]. Measuring the exact location of a UE is impractical, while RSRP information can be utilized to estimate a UE's location [48], [49]. This provides a strong mapping between a geographical location in a defined area to a set of RSRP values from the BSs within the area, while more BSs available in the area may further improve the location estimation's precision.…”

Section: B Proposed Ddqn-based Ho Algorithmmentioning

confidence: 99%

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

Tan

Bremner

Kernec

et al. 2022

IEEE Trans. Veh. Technol.

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For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (UK) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

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Section: B Proposed Ddqn-based Ho Algorithmmentioning

confidence: 99%

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

Tan

Bremner

Kernec

et al. 2022

IEEE Trans. Veh. Technol.

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show abstract

“…In addition, the error is even higher in urban areas where exposure to the celestial sphere is limited. In mobile communication systems, several techniques for positioning and tracking UEs have been developed, including the observed time difference of arrival (OTDOA) [20], enhanced cell ID (E-CID) [21], and fingerprints [22]. However, all of these techniques have error margins of tens of meters [18].…”

Section: B Object Trackingmentioning

confidence: 99%

“…Need particular device for mmWave radar [21] E-CID Limited accuracy (10 m >) [22] Fingerprints Limited accuracy (10 m >) [34] Localization with vision and RSSI Difficult to handle moving UEs [23], [24] mmWave radar Limited angular resolution…”

Section: B Object Trackingmentioning

confidence: 99%

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

Mihara

Murakami

Yamaguchi

et al. 2022

IEEE Open J. Commun. Soc.

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In a mobile millimeter wave (mmWave) communication system, blockages cause disconnections or serious degradation of communications. Several techniques have been proposed to control radio links between multiple base stations based on blockage prediction using camera images to avoid these problems. However, blockage prediction requires continuously determining the position of user equipment (UE) with decimeter precision, which is difficult when sensors and resources on the UE side are not available, and there are many moving objects around the UE. To resolve this problem, we propose a UE tracking method that uses a received signal strength indicator (RSSI) and RGB-D camera images from the base station. The proposed method consists of a combination of visual tracking and reidentification of the UE using radio information and camera images. We use the synchronization of the RSSI variation with the occlusion on the image by movements of the UE and objects for reidentification. We evaluated the proposed method experimentally in an outdoor environment by simulating a communication area formed by mmWave band base stations. The proposed method achieved an 11.5% improvement in tracking accuracy compared with conventional visual tracking.INDEX TERMS Image analysis, millimeter wave communication, object tracking,

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“…The point representing the position of the mobile terminal can be obtained by geometric calculations, for which the centroid method is the most common [44]. The weight of each Antenna Node (AN) is proportional to its Received Signal Strength (RSS) value; as a result, the AN with the highest RSS pulls the centroid most strongly to its own position [45]. The same weighting analysis can be performed using time measurements.…”

Section: Algorithms and Technologiesmentioning

confidence: 99%

Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

2021

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There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

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A novel RSRP-based E-CID positioning for LTE networks

Cited by 11 publications

References 7 publications

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

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