The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wireless devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a rate-splitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep  reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and ratesplitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.

The unprecedented growth in the number of connected devices has given rise to the Internet-of-Things (IoT) and led to an increasing demand for additional computational and communication resources. Within this context, unmanned aerial vehicles (UAVs) have shown to provide extended coverage, flexibility, and reachability. Motivated by this, in this paper, we develop a UAV-assisted data dissemination framework for IoT networks.  To this end, we formulate a joint optimization problem that aims to minimize the total energy expenditure, i.e., the sum of the energy consumed by the UAV and all the spatially-distributed IoT devices. We propose a deep reinforcement learning approach to solve the joint device classification, device association, and path planning optimization problem. In particular, we aim to 1) train a double deep Q-network agent in order to classify devices into two classes, and then using this classification we 2) develop an association algorithm based on the nearest-neighbor heuristic for device association, and 3) develop a path planning algorithm based on the Lin-Kernighan heuristic. Simulation results show that the proposed approach efficiently reduces the energy consumption as compared with the benchmark approaches, i.e., brute force approach and baseline approach. Furthermore, obtained results show that our approach provides a near optimum solution with a fraction of the time required compared to the brute force approach.