Performance Evaluation of LAA-LBT Based LTE and WLAN's Co-Existence in Unlicensed Spectrum

Dama, Sreekanth; Kumar, Abhinav; Kuchi, Kiran

doi:10.1109/glocomw.2015.7414071

Cited by 31 publications

(17 citation statements)

References 5 publications

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“…We can find several works dealing with the coexistence of the new unlicensed LTE technologies and the consolidated Wi-Fi [11][12][13][14]. One common finding of these works is that the LTE solutions are, for the evaluated scenarios, a better neighbor for Wi-Fi than Wi-Fi itself in terms of interference and capacity.…”

Section: Related Workmentioning

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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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%

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

Neto

Ana

et al. 2020

Sensors

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“…The application of LBT potentially enhances the coexistence, such as with Wi-Fi, through a clear channel assessment. Some analysis and performance tests have been reported toward the fairness under such a scenario [26,27]. However, a duty-cycle based approach still utilizes resources more tightly and with no modifications of the LTE standard [19].…”

Section: Related Studiesmentioning

confidence: 99%

Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band

Bajracharya

Shrestha

Kim

2019

Sensors

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The increased demand for spectrum resources for multimedia communications and a limited licensed spectrum have led to widespread concern regarding the operation of long term evolution (LTE) in the unlicensed (LTE-U) band for internet of things (IoT) systems. Because Wi-Fi and LTE are diverse with dissimilar physical and link layer configurations, several solutions to achieve an efficient and fair coexistence have been proposed. Most of the proposed solutions facilitate a fair coexistence through a discontinuous transmission using a duty cycling or contention mechanism and an efficient coexistence through a clean channel selection. However, they are constrained only by fairness or efficient coexistence but not both. Herein, we propose joint adaptive duty cycling (ADC) and dynamic channel switch (DCS) mechanisms. The ADC mechanism supports a fair channel access opportunity by muting certain numbers of subframes for Wi-Fi users whereas the DCS mechanism offers more access opportunities for LTE-U and Wi-Fi users by preventing LTE-U users from occupying a crowded channel for a longer time. To support these mechanisms in a dynamic environment, LTE-U for IoT applications is enhanced using Q-learning techniques for an automatic selection of the appropriate combination of muting period and channel. Simulation results show the fair and efficient coexistence achieved from using the proposed mechanism.

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“…In this regard two main protocols have been proposed, these are, listen before talk (LBT) method and duty cycle method. LBT is a mechanism by which users should assess the medium before transmitting, i.e, users adopt carrier sensing multiple access with collision avoidance to prevent the probability of collision with WiFi users [8], [9]. In the duty cycle, however, LTE-U users leave a space for WiFi users by refraining from transmission for some periods of time [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Joint channel and Power Allocation for Device-to-Device Communication on Licensed and Unlicensed Band

2019

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Device-to-Device communications (D2D) are being used to improve the spectral efficiency and to reduce the load of the base station (BS) by reutilizing the licensed spectrum. However, the primary cellular users may face high interference from D2D users. There is also a scarcity of licensed spectrum due to the dense deployment of smart devices. To alleviate this problem, in this paper, we are extending the D2D users to the unlicensed band. In this scenario, the D2D users are allowed to reuse the licensed spectrum or share the unlicensed spectrum with the incumbent WiFi users. However, cellular and WiFi users experience high interference if an efficient interference management scheme is not used. In this paper, we propose a joint mode selection, channel allocation, and power control algorithm using particle swarm optimization to manage the interference and improve the overall throughput of cellular and D2D users such that the minimum data rate requirement of WiFi users is guaranteed. Through numerical simulations, we show that the proposed algorithm can significantly mitigate the interference and improve the throughput of the system.

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Performance Evaluation of LAA-LBT Based LTE and WLAN's Co-Existence in Unlicensed Spectrum

Cited by 31 publications

References 5 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

Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band

Joint channel and Power Allocation for Device-to-Device Communication on Licensed and Unlicensed Band

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