LTE/Wi-Fi Coexistence in 5 GHz ISM Spectrum: Issues, Solutions and Perspectives

Abinader, Fuad M.; Sousa, Vicente A. de; Choudhury, Sayantan; Chaves, F. de S.; Cavalcante, André; Almeida, Erika P. L.; Vieira, Robson D.; Tuomaala, E.; Doppler, Klaus

doi:10.1007/s11277-017-5114-2

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…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]. With LTE-U, channel access alternatives based on channel sensing and dynamic adaptation of the duty cycle fractional work cycle have been proposed [17,18]. The authors in [18] proposed an approach to disable standard LTE transmissions on specific subframes using the almost blank subframe (ABS) LTE functionality.…”

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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“…Recent studies propose to dynamically set the ABS ratio tracking the network environment [7,8], especially in varying scenarios in terms of interference. The mute periods of ABS are also used in the context of multiple radio technologies coexistence [9][10][11], with several examples of solutions to automatically adapt ABS ratio to deal with the interference profile between the coexisting systems [12][13][14][15]. Following this trend, we propose to associate trend indicators [16] and trading know-how [17] to dynamically monitor and modify ABS parameters to improve HetNets performance.…”

Section: Introductionmentioning

confidence: 99%

MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how

2020

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Mobile communications are preparing for the incredible changes from 4th Generation (4G) to 5th Generation (5G) in the coming years. In this new generation, co-channel interference is one of the critical challenges to be tackled due to network densification by providing high data rates through several macro and small cells working together, configuring the so-called Heterogeneous Network (HetNet). The umbrella of 3GPP system (including LTE and its evolution towards 5G) provides the Almost Blank Subframe (ABS) as a scheme of the Enhanced Inter-Cell Interference Coordination (e-ICIC) framework to mitigate interference among macro and small cells. The ABS mutes some of the macro cell transmissions in selected subframes to decrease interference to small cells, thus orthogonalizing macro and small cell transmissions in time-domain. In this paper, we use a moving average convergence/divergence technique based on trading know-how to propose a real time ABS e-ICIC algorithm. Our proof-of-concept simulation results show relative capacity gains of 112% compared to the case without ABS.

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“…There are solutions that take such dynamic traffic conditions under consideration, such as Carrier-Sensing Adaptive Transmission (CSAT) [3], a Qualcomm solution for LTE-U that senses the medium for a long period for coexisting network activity, and adapts LTE transmission duration proportionally. In our previous works [13]- [15], we present and discuss the problems and solutions related to the operation of LTE networks in the unlicensed spectrum, especially the coexistence with Wi-Fi. However, there is scarce literature on the coexistence between multiple LTE-U networks, an expected scenario given the spectrum scarcity faced by some operators, and one which we believe needs to be properly addressed.…”

Section: Introductionmentioning

confidence: 99%

GTDM-CSAT: an LTE-U self Coexistence Solution based on Game Theory and Reinforcement Learning

Ana

Neto

Abinader

et al. 2019

JCIS

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There is substantial literature covering both problems and solutions related to the operation of Long Term Evolution (LTE) networks in unlicensed spectrum (LTE-U) while in coexistence with other technologies, such as Wi-Fi. However, a seldom explored scenario is the coexistence between multiple LTE-U networks. Within this scenario, a big issue is establishing optimal configurations that take into account fairness among different operators coexisting in the same unlicensed spectrum coverage area. Solutions to this problem could, for instance, react to changes in the environment by "tuning" different system configurations. We propose a game theoretical reinforcement learning algorithm, called GTDM-CSAT, aiming to maximize the LTE-U aggregated throughput while keeping channel access fairness among different access points. GTDM-CSAT uses the relative data rate offered by the system to adapt the LTE-U ON-OFF time. For this, we formulate the problem as a Markovian game, where the LTE-U operators coexist on a two-zero-sum game. The solution for the best ON-OFF time ratio is defined by applying a modified Minimax Q-learning algorithm for finding the game equilibrium. We perform simulations following 3GPP specifications using the ns-3 simulator for evaluating GTDM-CSAT under different traffic load scenarios. Results indicate gains in the system aggregated throughput by improving the individual data rate of each operator.

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LTE/Wi-Fi Coexistence in 5 GHz ISM Spectrum: Issues, Solutions and Perspectives

Cited by 7 publications

References 15 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

MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how

GTDM-CSAT: an LTE-U self Coexistence Solution based on Game Theory and Reinforcement Learning

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