Analysis of LTE Relay Interface for Self-Backhauling in LTE Mesh Networks

Favraud, Romain; Nikaein, Navid

doi:10.1109/vtcfall.2017.8288063

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…The ASCOM TEMS investigation tools were employed in this work to collect three types of downlink throughput data. These include the RLC throughput (Chen et al 2003;Paul et al 2016;Shreevastav and Carbajo 2016), PDSCH throughput (Padaganur and Mallapur 2018), and PDSCH throughput data rates (Favraud and Nikaein 2017). These throughput types were obtained via FTP file downloading 1GBytes at the User Equipment (UE) terminal.…”

Section: Mean User Throughput Measurementsmentioning

confidence: 99%

Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

Isabona

Imoize

2021

Journal of Technological Advancements

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Machine learning models and algorithms have been employed in various applications, from prognostic scrutinizing, learning and revealing patterns in data, knowledge extracting, and knowledge deducing. One promising computationally efficient and adaptive machine learning method is the Gaussian Process Regression (GPR). An essential ingredient for tuning the GPR performance is the kernel (covariance) function. The GPR models have been widely employed in diverse regression and functional approximation purposes. However, knowing the right GPR training to examine the impacts of the kernel functions on performance during implementation remains. In order to address this problem, a stepwise approach for optimal kernel selection is presented for adaptive optimal prognostic regression learning of throughput data acquired over 4G LTE networks. The resultant learning accuracy was statistically quantified using four evaluation indexes. Results indicate that the GPR training with the mertern52 kernel function achieved the best user throughput data learning among the ten contending Kernel functions.

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Section: Mean User Throughput Measurementsmentioning

confidence: 99%

Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

Isabona

Imoize

2021

Journal of Technological Advancements

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“…The first one is the most commonly used due to the versatility of the network and the independence between the access and the backhaul network, which leads to minimal interference [27]. On the other hand, the in-band perspective is included in LTE Release 10 as a relay mechanism to support evolved NodeB (eNBs) that have suffered from lost backhaul connectivity and should rely on a Donor eNB (DeNB) to have access to the CN [29]. In-band backhauling is an attractive option due to the fast reconfiguration time and low operational cost but imposes interference management and resource allocation challenges.…”

Section: The Architecture Of Backhaul For Uabs In Emergency Networkmentioning

confidence: 99%

Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks

Castellanos

Deruyck

Martens

et al. 2019

Sensors

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Today’s wireless networks provide us reliable connectivity. However, if a disaster occurs, the whole network could be out of service and people cannot communicate. Using a fast deployable temporally network by mounting small cell base stations on unmanned aerial vehicles (UAVs) could solve the problem. Yet, this raises several challenges. We propose a capacity-deployment tool to design the backhaul network for UAV-aided networks and to evaluate the performance of the backhaul network in a realistic scenario in the city center of Ghent, Belgium. This tool assigns simultaneously resources to the ground users—access network—and to the backhaul network, taking into consideration backhaul capacity and power restrictions. We compare three types of backhaul scenarios using a 3.5 GHz link, 3.5 GHz with carrier aggregation (CA) and the 60 GHz band, considering three different types of drones. The results showed that an optimal UAV flight height (80 m) could satisfy both access and backhaul networks; however, full coverage was difficult to achieve. Finally, we discuss the influence of the flight height and the number of requesting users concerning the network performance and propose an optimal configuration and new mechanisms to improve the network capacity, based on realistic restrictions.

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