English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, a machine learning based deployment framework of unmannedaerial vehicles (UAVs) is studied. In the considered model, UAVs are deployedas flying base stations (BS) to offload heavy traffic from ground BSs. Due totime-varying traffic distribution, a long short-term memory (LSTM) basedprediction algorithm is introduced to predict the future cellular traffic. Topredict the user service distribution, a KEG algorithm, which is a jointK-means and expectation maximization (EM) algorithm based on Gaussian mixturemodel (GMM), is proposed for determining the service area of each UAV. Based onthe predicted traffic, the optimal UAV positions are derived and threemulti-access techniques are compared so as to minimize the total transmitpower. Simulation results show that the proposed method can reduce up to 24\%of the total power consumption compared to the conventional method withouttraffic prediction. Besides, rate splitting multiple access (RSMA) has thelower required transmit power compared to frequency domain multiple access(FDMA) and time domain multiple access (TDMA).

Machine Learning for Predictive Deployment of UAVs with Multiple Access