Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

Abstract

Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UEs), several issues need to be addressed to enhance cellular UAVs' services. In this paper, we propose a realistic communication model on the downlink, and we show that the Quality of Service (QoS) for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which is known to be NP-hard, we introduce a solution based on game theory. First, we argue that separating between UAVs and UEs in terms of the assigned sub-carriers reduces the interference impact on the users. This is materialized through a matching game. Moreover, in order to boost the partition, we propose a coalitional game that considers the outcome of the first one and enables users to change their coalitions and enhance their QoS. Furthermore, a power optimization solution is introduced, which is considered in the two games. Performance evaluations are conducted, and the obtained results demonstrate the effectiveness of the propositions.

RETRACTED ARTICLE: Positioning of UAV Base Stations Using 5G and Beyond Networks for IoMT Applications

“…The study in [ 20 ] suggested a path optimization strategy for drones to minimize their delay in contact and conflict with IoMT applications. The authors in [ 21 ] have analyzed drone-UE's base station coverage accuracy communicating with terrestrial base stations. The researchers reported a pathway technical architecture in [ 21 ] to minimize a UAV-mission UT's duration.…”

“…The authors in [ 21 ] have analyzed drone-UE's base station coverage accuracy communicating with terrestrial base stations. The researchers reported a pathway technical architecture in [ 21 ] to minimize a UAV-mission UT's duration. Additionally, in the hopping chance and average attainable throughput, the authors characterized the success of drone- UTs in uplink contact with ground BSs.…”

5G Experimentation for Public Safety: Technologies, Facilities and Use Cases

“…The optimization problem is particularly challenging with respect to spectrum sharing and offloading strategies in settings combining macro and small terrestrial BS cells with large UABS fleet deployments [49], [55]. Some promising research directions address the use of AI for autonomy, distributed deployment optimization and swarm coordination [30], [41], [69], and approaches involving for example game theory, genetic algorithms and deep learning are investigated in this respect [49], [58], [70].…”

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

“…2, MLP is a deep learning neural network including an input layer, several hidden layers and an output layer, and each layer has an arbitrary number of neurons that propagate an output to the next layer through a nonlinear activation function. Mathematically, this can be formulated as (3) where x and y denote the input vector and the output vector, respectively. ω is the weight vector and θ is the bias.…”

“…More recently, cellular-enabled UAVs have been successfully armed and controlled remotely over 5G advanced facilities, which introduces a new formula to overcome the aforementioned shortcomings [3]. Nevertheless, the safe and secure navigation of UAVs is crucial for those remote operations.…”