This work considers a hybrid laser-battery powered uncrewed aerial vehicle (UAV) data collection system serving a passive Internet of Things deployment via monostatic backscatter communications. In this paper, we highlight the merits of the hybrid scheme over the laser only or battery only powered devices UAVs in terms of improved reach and durability. In addition, we study the laser energy consumption - destination battery level retention tradeoff optimization problem. Throughout this process, we optimize the single-rotor UAV’s three-dimensional trajectory, the UAV’s and the laser’s radiated power profiles, and the temporal battery usage profile while adopting path discretization. The resulting non-convex problem is solved via single-block successive convex approximation, for which novel bounds for the UAV propulsion energy, harvested energy, and collected data assuming a probabilistic line-of-sight channel model are derived. Finally, the simulation results show significant data collection gains, battery energy savings, and laser energy consumption reductions compared with a baseline scheme and highlight the complexity-optimality tradeoff.

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s (i) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of (i) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems in a multi-cell indoor scenario. We investigate the energy harvesting and data rate performance of multiple users while meeting the lighting constraints. To this end, we develop optimization frameworks that tune the parameters of transmitters or receivers to improve the SLIPT system performance. Firstly, we model the relationship between tunable lens-based optical receivers concentrator gain and their fields of view. Next, we develop algorithms to maximize the spectral efficiency (SE) considering per-user minimum harvested energy requirements and lightning constraints by controlling the average light emitting diode (LED) excitation current or tuning the optical receivers' fields of view. Then, we study the joint system performance limits of SE and total harvested energy. Towards this aim, we formulate multi-objective optimization problems and propose algorithms to find the best SE - energy harvesting tradeoff for both designing strategies. Finally, we present some extensive simulations to explore the benefits of the proposed algorithms comparing with basic benchmarks. Besides, we monitor the effect of changing several system parameters on the two objectives and the behavior of the inherent trade-off between them under both transmitter and receiver sides designing strategies.

The continuous improvement in optical energy harvesting devices motivates the development of visible light communication systems that utilize such available free energy. In this paper, an outdoor visible light communications (VLC) system is considered where a VLC base station sends data to multiple users that are capable of harvesting optical energy. The proposed VLC system serves multiple users using time division multiple access (TDMA) with unequal time and power allocation, which are allocated to achieve the system communications and illumination objectives. In an outdoor setup, the system lighting objective is to maximize the average illumination flux, while the communication design objective is to maximize the spectral efficiency (SE). A multiobjective optimization problem is formulated to obtain the Pareto front of the SE-illumination region. To this end, the marginal optimization problems are solved first using low complexity algorithms. Then, based on the proposed algorithms, a Karush-Kuhn-Tucker-based algorithm is developed to obtain an inner bound of the Pareto front for the SE-illumination tradeoff. The inner bound for the Pareto-front is shown to be close to the optimal Pareto-frontier via several simulation scenarios for different system parameters.

Visible light communications (VLCs), in general, and resource allocation for VLC networks in particular, have gained lots of attention recently. In this paper, we consider the resource allocation problem of a VLC downlink transmission system employing dynamic time division multiple access, where time and power variables are tuned to maximize the downlink spectral efficiency (SE). As for the operational conditions, we impose constraints on the average optical intensity, the energy budget, and the quality-of-service. To solve this non-convex problem, we transform the objective function into a difference of concave functions by solving a second-order differential inequality. Then, we propose a low-complexity algorithm to solve the resource allocation problem. Finally, we show by simulations the SE performance gains achieved by optimizing time and power allocation over the initial total power minimization solution for the considered system.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems of multi-cell indoor scenario. We aim to investigate the energy harvesting and data rate performance of multiple users while meeting the lightning constraints. To this end, we develop optimization frameworks and tune the light emitting diodes average currents to improve the performance of the SLIPT system. Firstly, we propose an algorithm to maximize the spectral efficiency (SE) subject to lighting and minimum harvested energy per user requirements. The proposed algorithm can be implemented in a distributed fashion with a reduced computational burden at each node. Then, we consider the energy harvesting maximization problem to investigate the maximum possible energy gain and its corresponding SE performance. Finally, we present some extensive simulations to explore the benefit of the optimization frameworks with respect to standard equal allocation setting. In addition, we monitor the effect of changing several system parameters on the two objectives and highlight the underlying trade-off between them.

Multi-tier heterogeneous networks have become an essential constituent for next-generation cellular networks. Meanwhile, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for a two-tier phantom cellular network. The optimization framework includes both EE and SE. We consider densely deployed phantom cellular networks and model the EE optimization problem considering the inevitable interference in this setup and imperfect channel estimation impairments. To this end, we propose three resource allocation strategies aiming at optimizing this network EE performance metric. Furthermore, we investigate the effect of changing some system parameters on the performance of these strategies, such as phantom cells resource units share, number of deployed phantom cells within a macro cell, number of pilots, and the phantom cells transmission power budget. It is found that increasing the number of pilots will deteriorate the EE performance of the whole setup, while increasing phantom cells transmission power budget will not affect the EE of the whole setup significantly. In addition, we observed that it is always useful to allocate most of the network resource units to the phantom cells tier.

Multi-tier heterogeneous networks have become an essential constituent for next generation cellular networks. Mean-while, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for the recently proposed two-tier network architecture known as phantom cellular networks. The optimization framework includes both EE and SE, where we propose an algorithm that finds the SE and EE resource allocation strategies for phantom cellular networks. Then, we compare the performance of both design strategies versus the number of users, and phantom cells share of the total number of available resource blocks. We aim to investigate the effect of some system parameters to achieve improved SE performance at a non-significant loss in EE performance, or vice versa. It was found that increasing phantom cells share of resource blocks decreases the SE performance loss due to EE optimization when compared with the optimized SE performance.