On a fair coexistence of LTE and Wi-Fi in the unlicensed spectrum: A Survey

Zinno, Stefania; Stasi, Giovanni Di; Avallone, Stefano; Ventre, Giorgio

doi:10.1016/j.comcom.2017.10.019

Cited by 59 publications

(41 citation statements)

References 19 publications

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“…Medium access mechanisms are key blocks to allow coexistence between different technologies in the unlicensed spectrum. Thus, the mismatch between parameters can lead one technology to overlap others concerning access to the communication channel [16]. Even with this concern, LTE-LAA, with the carrier-sense multiple access with collision avoidance (CSMA-CA) mechanism, can achieve a performance equal to or better than Wi-Fi coexisting with Wi-Fi [12].…”

Section: Related Workmentioning

confidence: 99%

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Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Neto

Ana

et al. 2020

Sensors

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Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

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Section: Related Workmentioning

confidence: 99%

“…Select an action a ∈ A according to max Take the immediate reward r, which is T tx wi f i + T tx wi f i ;16 Observe the next state s ∈ S; 17 Update Q-table according to the expression:…”

mentioning

confidence: 99%

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Neto

Ana

et al. 2020

Sensors

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“…At present, although several articles evaluate the coexistence of both mechanisms [8], there is still a need to investigate in this area with end-to-end performance assessment purposes especially in dense environments [9], avoiding, as far as possible, the degradation of pre-existing technologies. Most of the collected works mentioned in [8] choose a certain configuration of the access channel parameters to show the benefits of their proposal or to demonstrate the harmful or beneficial effect on WiFi. Many of these chosen parameters are not sufficiently justified or do not fully comply with the standard.…”

Section: Introductionmentioning

confidence: 99%

KQI Performance Evaluation of 3GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios

2020

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The rapid proliferation of user devices with access to mobile broadband has been a challenge from both the operation and deployment points of view. With the incorporation of new services with high demand for bandwidth such as video in 4K, it has been deemed necessary to expand the existing capacity by including new bands, among which the unlicensed 5-GHz band is a very promising candidate. The operation of future 3GPP (Third Generation Partnership Project) mobile network standards deployments in this band implies the coexistence with other technologies such as WiFi, which is widespread. In this context, the provision of Quality of Service (QoS) or Quality of Experience (QoE) becomes an essential asset and is a challenge that has yet to be overcome. In this sense, 3GPP has proposed a traffic prioritization method based on the Listen Before Talk access parameters, defining a series of priorities. However, it does not specify how to make use of them, and even less so in potentially conflicting situations. This paper assesses the end-to-end performance of downlink unlicensed channel priorities in dense scenarios via implementing a novel simulation setup in terms of both multi-service performance and coexistence.

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“…In LTE‐A, unlicensed spectrum receives very good treatment for the better QoE/Quality of Service (QoS). Zinno et al proposed basic overview and schema for Wi‐Fi with LTE integrated networks. The objective of LTE/LTE‐A is to improve the performance of licensed bands with a high level of security.…”

Section: Introductionmentioning

confidence: 99%

Fog computing–based node‐to‐node communication and mobility management technique for 5G networks

Babu

Biswash

2019

Trans Emerging Tel Tech

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In this paper, we present a Fog computing–based node‐to‐node communication architecture for 5G networks. It supports the minimum network infrastructure dependent communication. In order to establish the connection, associated next generation evolve node base station (gNB) imports the required information from nearby Fog servers. The retrieved pieces of information are processed in data analytic units (DAUs). The DAUs are in‐build processing units and closely tie‐up with Fog servers/gNBs. The Fog servers and data analytic units are frequently deployed in the coverage area. They always offer and receive the services from gNBs to establish node‐to‐node communication. A robust mobility management scheme is also proposed for dynamic mobile users to achieve node‐to‐node communication. The signaling cost, energy consumption, delay, and latency are the performance parameters for the proposed work. The numerical and system level simulation results are produced to validate the proposed contributions. We compare the obtained results with 4G/LTE cellular networks in order to check the effectiveness of work. We achieved 26% to 63% of performance improvement as compared to legacy networks w.r.t. listed parameters.

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On a fair coexistence of LTE and Wi-Fi in the unlicensed spectrum: A Survey

Cited by 59 publications

References 19 publications

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

KQI Performance Evaluation of 3GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios

Fog computing–based node‐to‐node communication and mobility management technique for 5G networks

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