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

TLDR

LTE‑LAA offers a promising way to alleviate spectrum scarcity by operating LTE in unlicensed bands, but it requires a fair coexistence mechanism with incumbent WiFi deployments. This work proposes a novel deep‑learning framework to model resource allocation for LTE‑LAA small base stations. The framework uses a noncooperative game where SBSs predict future actions to perform proactive channel selection, carrier aggregation, and fractional spectrum access, converging to a mixed‑strategy Nash equilibrium that guarantees long‑term fairness with WiFi and other LTE‑LAA operators. Simulations with real traces show the scheme yields up to 28 % and 11 % performance gains over reactive and proportional‑fair approaches, serves delay‑tolerant traffic ahead of time, and preserves WiFi performance in dense deployments.

Abstract

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 fractional 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.