Machine Learning Assisted Caching and Adaptive LDPC Coded Modulation for Next Generation Wireless Communications

Hassan Nooh, Zhikun Zhu, Soon Ng

Abstract

Unmanned Aerial Vehicles (UAVs) constitute a key technology for next generation wireless communications. Compared to terrestrial communications, wireless systems with low-altitude UAVs are in general faster to deploy, more flexible and are likely to have better communication channels due to the presence of short-range Line of Sight (LoS) links. In this contribution, a Latent Dirichlet Allocation (LDA) based machine learning algorithm was utilized to optimize the content caching of UAVs, while the K-means clustering algorithm was invoked for optimizing the assignment of mobile users to the UAVs. We further investigated a practical adaptive Low Density Parity Check (LDPC) coded modulation (ALDPC-CM) scheme for the communication links between the UAVs and the users. We found that the caching efficiency of each UAV can be boosted from 50% with random caching to above 90% with the employment of LDA. We also found that the proposed ALDPC-CM scheme is capable of performing closely to the ideal perfect coding based scheme, where the mean delay of the former is only about 0.05 ms higher than that of the latter, when the UAV system aims to minimize both the transmission and request delays.

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