Machine Learning for Predictive Deployment of UAVs With Multiple Access

Abstract

This paper presents a machine learning-based framework for the predictive deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from ground BSs. To account for time-varying traffic distribution, a long short-term memory (LSTM)-based prediction algorithm is introduced to predict future cellular traffic. A joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture models (GMM) is proposed to determine the service area of each UAV based on the predicted user service distribution. Based on the predicted traffic, the optimal positions of UAVs are derived, and four multiple access techniques, namely, rate splitting multiple access (RSMA), frequency domain multiple access (FDMA), time domain multiple access (TDMA), and nonorthogonal multiple access (NOMA), are compared to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Furthermore, RSMA is found to require the lowest transmit power among the four multiple access techniques. Therefore, this paper focuses on the comparison of multiple access techniques for UAV deployment, which is essential for the efficient and effective use of UAVs as flying BSs.

LEO-Assisted Aerial Deployment in Post-Disaster Scenarios Using a Combinatorial Bandit and Genetic Algorithmic Approach

“…η Sj i (t) given in (6) is the assigned capacity of the LEO-aerial link to cover cluster i by aerial j at time t. Also, Ψ j i (t) given in ( 4) is the total data rate between aerial j and cluster i. In (7), the minimum of the aerial-UD link and the aerial-LEO link data rates is taken because UD data will be relayed to LEO-Sat via its coverage aerial. Thus, the total link speed between UD and LEO-Sat will be the minimum between both links as both will exist simultaneously.…”

“…A(t) is evaluated by enumerating the |A(t)| clusters having the highest (q i (t) + Ω γi (t)). Afterward, the aerials should be distributed among them and attain x * j i (t) in a manner that minimizes aerials battery consumption subject to the aerial remaining battery energy at time t, i.e., Γ jR (t), as given in constraint C4 in (7). To do that, Γ j i (t) matrix is evaluated for ∀i ∈ A(t) and ∀j ∈ N using ( 9) by assuming the worst case scenario when P tj i S (t) = P Vmax for ∀j ∈ N .…”

“…After obtaining the values of x * j i (t) and distributing the aerials among the selected A(t), the values of Ψ j i (t) become known. Then, in the next step, the values of P tj i S (t) and B Sj i (t) should be optimized to accommodate the values of Ψ j i (t) related to every aerial under constraints C6 and C7 in (7). This optimization problem can be formulated as follows: max…”

“…This is due to their ability to quickly reach remote and hard-to-access areas [1][2][3]. In postdisaster situations, aerials can offer wireless connection for user devices (UDs) owned by survivors and rescue teams where the terrestrial wireless communication networks are completely damaged or malfunctioned [4][5][6][7]. Deploying multiple aerials in such scenarios is challenging, requiring multi-objective optimization, including coverage, limited aerial battery life times, heights, and communication performance.…”