Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

Unmanned aerial vehicle-aided cooperative regenerative relaying network under various environments

This paper studies a cooperative relay network that comprises an unmanned aerial vehicle (UAV) enabling amplify-and-forward (AF) and power splitting (PS) based energy harvesting. The considered system can be constructed in various environments such as suburban, urban, dense urban, and high-rise urban where the air-to-ground channels are model by a mixture of Rayleigh and Nakagami-m fading. Then, outage probability and ergodic capacity are provided under different environment-based parameters. Optimal PS ratios are also provided under normal and high transmit power regimes. Finally, the accuracy of the analytical results is validated through Monte Carlo methods

Outage Analysis of Energy Harvested Relay-Aided Device-to-Device Communications in Nakagami Channel

In this paper, we obtain a low-complexity closed-form formula for the outage probability of the energy-harvested decode-and-forward (DF) relay-aided underlay Device-to-device (D2D) communications in Nakagami fading channel. By proposing a new idea which finds the power splitting factor in simultaneous wireless information and power transfer (SWIPT) energy-harvesting system such that the transmit power of the relay node in the second time slot is fixed in a pre-defined value, the obtained closed-form expression is valid for both energy-harvested and non-energy-harvested scenarios. This formula is based on n-point generalized Gauss-Laguerre and m-point Gauss-Legendre solutions. It is shown that n is more effective than m for reducing the formula complexity. In addition to a good agreement between the simulation results and numerical analysis based on normalized mean square error (NMSE), it is indicated that (n, m)=(1, 4) and (n, m)=(1, 2) are the appropriate choices, respectively for 0.5≤ µ <0.7 and µ ≥0.7, where µ is the fading factor. As shown in this investigation, increasing the average distance between D2D pairs and cellular user (lower interference), is the reason for decreasing the outage probability. Furthermore, it is clear that increasing the Nakagami fading factor is the reason for decreasing the outage probability

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU