General expressions for downlink signal to interference and noise ratio in homogeneous and heterogeneous LTE-Advanced networks

Ali, Nora A.; Mourad, H.-A.M.; ElSayed, Hany M.; El-Soudani, Magdy S.; Amer, Hassanein H.; Daoud, Ramèz M.

doi:10.1016/j.jare.2016.09.003

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…The main challenges that affect a user of cellular networks on the ground are the Doppler effect [15], line of sight [16], and co-channel interference [17]. These challenges are even more impactful for airborne receivers, as illustrated in Fig.…”

Section: Interference In Airborne Applicationsmentioning

confidence: 99%

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Detection Algorithm for Cellular Synchronization Signals in Airborne Applications

Stevens

Younis

2021

IEEE Access

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Cellular to air communication is critical for the booming aerial package delivery and transportation business. Detecting cellular signals in airborne applications is challenging because it requires receiving and processing waveforms that are subject to significantly more interference than those experienced in terrestrial settings. This paper highlights and tackles the complexity of 4G Long Term Evolution (LTE) signal synchronization in high altitude applications, e.g., cell access onboard an aircraft. Specifically, we design a novel cell detector that operates efficiently under high interference levels found in airborne applications, maintains a constant false alarm rate using an optimized threshold implementation for Zadoff Chu sequences, and monitors multiple towers with different time delays simultaneously. We validate our cell detector through simulation and experimentation. Lastly, the cell detector is used to estimate the interference in live waveforms taken from an aircraft at 2 to 2.5 km altitude and velocities of 200-400 km/h. Our cell detection model can be adapted to support 5G New Radio (NR) synchronization signals as NR deploys aerial support in the future. The threshold implementation to handle correlation spurs can be applied directly to other Zadoff Chu based signals such as random access signals found in both LTE and NR.

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Section: Interference In Airborne Applicationsmentioning

confidence: 99%

“…Cellular signal detection and synchronization for groundbased applications have been thoroughly investigated [1] [2]. The main challenges with synchronization include line of sight limitations, co-channel interference, and Doppler effects [3] [4]. The severity of these challenges increases in higher altitude applications.…”

Section: Introductionmentioning

confidence: 99%

Detection Algorithm for Cellular Synchronization Signals in Airborne Applications

Stevens

Younis

2021

IEEE Access

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“…Based on the cellular to UAV path-loss model (1), we can calculate the received power and SINR at (x U AV , y U AV ) from [36] when the serving cell is a macro BS:…”

Section: Coverage Hole Detection and Optimisation Problemsmentioning

confidence: 99%

Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand

2020

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The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

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“…Figures 6, 7 and 8 present relatively, in percent, minimum and maximum contribution of BTSs situated in rings 2 to 6 to the interference value caused by the BTSs in central cell and in the first ring of cells. The results are calculated according to (11). The points, which are chosen to calculate the contribution of further rings to the interference, form equi-interference line of the relative value 3.8.…”

Section: Contribution Of Further Rings To Interference Valuementioning

confidence: 99%

“…Interference is significantly greater than regular signal: the value of S/I may be in the worst case between −15 dB (when taking into account only the MSs from the same cell) and −17 dB (when taking into account also the MSs from the adjacent cells). The similar calculation process is implemented for LTE (4G) [11] and 5G systems [12]. A brief review of the interference influence when MSs are moving in the cell and over the cell rim leading to handover may be found in [13].…”

Section: Introductionmentioning

confidence: 99%

Design of equi-interference lines in CDMA mobile systems

Lebl¹,

Mitić²,

Markov³

et al. 2019

Journal of Electrical Engineering

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In this paper it is presented the process of designing equi-interference lines in the CDMA mobile telephony systems. It is proved that shape of these lines in one base station cell is determined by emission characteristics of that base station and the base stations, which form the first ring around the considered cell. The influence of base stations from the other rings after the first one is dominantly noticed as the interference increase in each point of equi-interference line. The value of interference increase depends on the value of environmental propagation coefficient. For its small values it is necessary to consider the influence of base stations from more than twenty rings around the analyzed cell, while for great values it is enough to consider only two rings.

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General expressions for downlink signal to interference and noise ratio in homogeneous and heterogeneous LTE-Advanced networks

Abstract: Graphical abstract

Cited by 16 publications

References 11 publications

Detection Algorithm for Cellular Synchronization Signals in Airborne Applications

Detection Algorithm for Cellular Synchronization Signals in Airborne Applications

Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand

Design of equi-interference lines in CDMA mobile systems

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