Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

Dziedzic, Adam; Sathya, Vanlin; Rochman, Muhammad Iqbal; Ghosh, Monisha; Krishnan, Sanjay

doi:10.1109/ojvt.2020.2981519

Cited by 19 publications

(9 citation statements)

References 36 publications

(49 reference statements)

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“…Authors in [32] demonstrated the use of neural networks to detect the number of Wi-Fi access points during the LTE-U OFF period and to set the LTE-U duty cycle accordingly. Their experimental results suggest that the proposed MLbased approach results in a higher accuracy compared to existing methods.…”

Section: A Related Workmentioning

confidence: 99%

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

2021

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The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9% precision and 88.1% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

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Section: A Related Workmentioning

confidence: 99%

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

2021

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“…Similarly, an energy detection-based approach is used to distinguish between one and two WiFi APs in [24]. The work in [25] offers an ML-based strategy for determining numerous WiFi APs using energy levels measured. This work also proposes an LTE-U/WiFi coexistence scheme that utilizes the number of APs detected by the system.…”

Section: Coexistence In Uncoordinated Lte and Wifi Networkmentioning

confidence: 99%

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Girmay

Maglogiannis

Naudts

et al. 2021

Sensors

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Nowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively.

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“…The problem persists with coexistence scenarios involving the dense network. The spectrum sharing in dense network with low cost includes some of the FEM applications [12,13].…”

Section: Literature Surveymentioning

confidence: 99%

“…This includes competitive analysis at an initial stage and further involves navigation schemes that affects performance. In most scenarios D2D (device to device) communication is preferred with short-range that includes: lower band, greater throughput and improved energy efficiency [11,12]. A set of application are defined by IEEE 802.11 enabling the PHY and MAC layer to support WLAN communication.…”

Section: Introductionmentioning

confidence: 99%