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.

Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user's devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.INDEX TERMS Energy efficient wireless communication, power allocation, D2D communication, multiagent reinforcement learning, deep reinforcement learning.

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This paper proposes an exhaustive analysis of a particle swarm optimization (PSO)-based configuration applied in non-orthogonal multiple access (NOMA) systems in order to perform user aggregation along different sub-channels. The idea behind this is to highlight the main characteristics of this PSO-based configuration for understanding how this policy enables the transmitter to require the minimum downlink transmitting power while guaranteeing the quality-of-service (QoS) constraint of each user. The analysis is carried out for two representative power-constrained scenarios, i.e., disaster relief network communications and unmanned aerial vehicle (UAV) communications, in which performing low-power transmissions represent an important aspect. Our results find applicability in the definition of explicit channel-state-aware strategies for user multiplexing in NOMA systems, which, at the date, represents a research field that remains to be investigated more in depth. Insightful discussions are provided from our analysis. For instance, depending on the number of available sub-channels and the channel gains experienced by each users along with the whole available bandwidth, it is shown how users must be multiplexed by the transmitter in order to reach the minimum transmitting power.

The lack of communication between local authorities, first aid responders, and the population that are present in a natural disaster area, represents critical aspects which can compromise relief operations in saving human lives. During natural disasters (earthquakes/tsunamis), the typical telecommunications network infrastructure in the affected area could be damaged or unfunctional. This can seriously compromise the efficiency of first aid operations. In this paper, we propose a device-to-device (D2D)-based framework which, starting from some basic information such as positions and battery level of victim's devices, could provide communication from a disaster area towards a functional area. This framework, utilized by a base station located in a functional area, organizes users of disaster area into clusters of users and for each cluster select a gateway. This framework permits also, to evaluate the optimal transmission power for each gateway in order to maximize the energy efficiency in the area and to create a multi-hop path from the disaster area to relay node minimizing the end-to-end delay. The simulations results demonstrate that our proposed approach outperforms either random policy assignment and static policies assignment in both power allocation and routing path creations.