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

被引:127
作者
Challita, Ursula [1 ]
Dong, Li [1 ]
Saad, Walid [2 ]
机构
[1] Univ Edinburgh, Sch Informat, Edinburgh EH8 9AB, Midlothian, Scotland
[2] Virginia Tech, Bradley Dept Elect & Comp Engn, Wireless VT, Blacksburg, VA 24061 USA
基金
美国国家科学基金会;
关键词
Licensed assisted access LTE (LTE-LAA); LTE-U; small cell; unlicensed band; long short term memory (LSTM); deep reinforcement learning; game theory; proactive resource allocation; FAIRNESS; NETWORKS;
D O I
10.1109/TWC.2018.2829773
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 IXE-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.
引用
收藏
页码:4674 / 4689
页数:16
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