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Recently, drone small cells (DSCs) has been brought into significant focus, which is the one key enabler for potentially facilitating terrestrial wireless communication systems. Meanwhile, in ultra-dense unmanned networks, artificial intelligence (AI) has been a useful and efficient tool for control and management of the multi-agents. This paper investigates a downlink interference control problem in ultra-dense unmanned networks with AI-aided approach, that each DSC can adjust its altitude to increase the data-rate. This problem is formulated as a mean field game (MFG) framework, an AI-aided method to make decisions. In this framework, each DSC controls its velocity to minimize the cost over a period, where the cost function is composed by the data-rate and height adjusting consumption. Meanwhile, in this model, we adopt the mean-field approximation (MFA) approach to derive the interference introduced from a large number of DSCs. Besides, the control strategy is described and explained by using the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, respectively. Thus, a finite difference algorithm is proposed to solve the coupled partial differential equations, which can obtain the optimal altitude control strategy. The algorithm outputs show the optimal behaviors of DSCs in different environment scenarios. In additon, the simulation results verify that the proposed control strategy has better average signal to interference plus noise ratio (SINR) compared with the baseline method.

This paper investigates the resource allocation and distributed power control in the dense unmanned aerial vehicle (UAV) networks. Under the considered non-orthogonal multiple access (NOMA)-based UAV networks, we propose a two-stage scheme to alleviate the congestion of date traffic and the interference effects. In the proposed scheme, a high altitude platform (HAP) performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the UAVs autonomously perform distributed power control. We formulate centralized resource allocation problem as a roommate matching problem and develop a novel time slot allocation algorithm to solve it. And the distributed power control of massive UAVs is formulated as a mean field game (MFG), which is solved based on the finite difference method. Simulation results show that the proposed scheme can greatly improve the reliability of communication in the dense UAV networks.

The use of drone small cells (DSCs) has recently drawn significant attentions as one key enabler for providing air-to-ground communication services in various situations. This paper investigates the co-channel deployment of dense DSCs, which are mounted on captive unmanned aerial vehicles (UAVs). As the altitude of a DSC has a huge impact on the performance of downlink, the downlink interference control problem is mapped to an altitude control problem in this paper. All DSCs adjust their altitude to improve the available signal-to-interference-plus-noise ratio (SINR). The control problem is modeled as a mean-field game (MFG), where the cost function is designed to combine the available SINR with the cost of altitude controling. The interference introduced from a big amount of DSCs is derived through a mean-field approximation approach. Within the proposed MFG framework, the related Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations are deduced to describe and explain the control policy. The optimal altitude control policy is obtained by solving the partial differential equations with a proposed finite difference algorithm based on the upwind scheme. The simulations illustrate the optimal power controls and corresponding mean field distribution of DSCs. The numerical results also validate that the proposed control policy achieves better SINR performence of DSCs compared to the uniform control scheme.

Unmanned aerial vehicles (UAVs) are foreseen as a promising solution to deal with sophisticated communication scenarios, which can be used as aerial mobile base stations to enhance terrestrial wireless communication systems. Due to the wide range of serviced ground users, a large number of UAVs need to be deployed. In this paper, we study a multi-UAV downlink communication system with the UAVs acting as the aerial mobile base stations, and investigate the probability of successful transmission between the UAV and its served users, with consideration of both energy availability and mobility effects. We propose the optimal control law based on a discrete-time mean field game (MFG) with power control and velocity control. Furthermore, the optimal controls generate an almost surely asymptotic Nash equilibrium according to the estimate of population state average, meaning that the long time average cost reaches its minimum. Simulation results show that the proposed control policy can effectively improve the probability of successful transmission.

In emergency situations such as earthquakes, the cellular infrastructure cannot support communication services because of equipment damage. The use of the large number of unmanned aerial vehicles (UAVs) has been drawn significant attentions as an important solution for providing air-to-ground communication services in such situations. In this paper, we research the flight direction policy (velocity vector) of the UAVs where every UAV acts as the base station to serve the multi-users communications. As the trajectory of UAVs have a huge impact on the performance of communication, we investigate an adaptive coverage problem, that all the UAVs can adjust their velocities to increase the number of served users. However, such behavior may cause larger flight energy consumption. We propose a discretetime mean-field game (MFG) framework that each UAV adjusts its velocity in order to minimize the flight energy consumption. In this framework, each UAV evolves according to the dynamic equation and seeks to minimize its flight energy consumption containing the average distribution of all UAVs. We investigate a deterministic function ϕ to approximate the average distribution of all UAVs as the number of UAVs tends to infinity. Furthermore, the optimal velocity vectors generate a certain asymptotic Nash equilibrium as time tends to infinity, which implies that the flight energy consumption of each UAV can reach its minimal value as the number of UAVs increases to infinity. The simulation results show the optimal trajectory and optimal flight tendency of the UAVs. Moreover, we show that as users move, the amount of the users served is maintained at a relatively stable range, which represents met the demand of user’s adaptive coverage.

Wireless powered relay (WPR) is an emerging application scenario for simultaneous wireless information and power transfer (SWIPT), which has drawn significant attentions. In this paper, we propose a cognitive two-path relay (TPR) network with the capability of power splitting (PS)-SWIPT. With the aid of PS, the successive source and relay transmissions can overcome spectral efficiency loss compared with half-duplex (HD) relay-assisted PS-SWIPT networks. An optimization problem is formulated, with jointly optimizing the PS factors of two relays to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at the destination during two successive time slots. Through ratiocination and proving, the optimal solutions can be obtained by iterative methods. In consideration of inter-relay interference (IRI) cancellation, we also investigate the optimal PS factors under the condition of sufficiently small IRI channel gains. The simulation results demonstrate that, in the proposed scheme, the optimal PS factors with IRI cancellation are almost identical with the solutions with IRI when the IRI channel gains lower than a certain threshold, which can be set as the initial values of the iteration to reduce the iteration times in practical networks.