Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

By Ursula Challita, Li Dong, Walid Saad. 2017

167 citations recorded by the source corpus.

Performing cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and f ractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as homo egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with wireless local area network and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “The effective amount of bandwidth can also be increased using low-complexity dynamic spectrum access [44] and proactive resource management [45].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[305] Proactive resource management for LTE Reinforcement learning Deep policy gradient”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “on each of the selected channels based on the network trafﬁc conditions while also guaranteeing a longterm equal weighted fairness with WLAN and other BSs. The proactive resource allocation scheme in [158] is formulated as a noncooperative game in which the players are the BSs. Each BS must choose w”
Artificial-Intelligence-Enabled Intelligent 6G Networks (2019, background): “5 (b), where the framework first modules input massive spectrum datasets in different radio contexts [15].”
Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks (2019, methodology): “Recently, DRL-based approach has been applied to several research fields, such as resource management and allocation [25]–[31], mobile offloading [32], dynamic channel access [33], fog radio access networks [30], mobile edge computing and caching [34].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “The average throughput gain for LTE-U upon applying a proactive approach (with varying T) as compared to a reactive approach [156].”