This work studies resource allocation policies for a multi-antenna access point that is powered by a combination of harvested energy and the power grid, communicating with multiple single-antenna users. We propose a non-convex problem to directly solve the throughput maximization problem. Though the problem is challenging to solve, we first propose an iterative algorithm based on the first-order Taylor expansion and block coordinate descent for the scenario that full channel state information (CSI) and energy arrival information (EAI) are assumed to be known. Then, inspired by this scenario, we study a case in which statistical CSI and EAI are only required. Simulation results demonstrate that the energy-performance trade-off as well as the performance of the statistical case is comparable to the full CSI and EAI scenario, which supports the practical aspect of the proposed policies.

This paper studies resource allocation policies for energy harvesting (EH) multi-user multiple input single output (MU- MISO) communication systems. The multi-antenna EH base station (BS) is equipped with a limited-capacity battery. Though employing the multi-antenna at the BS provides the channel diversity, this leads to the co-channel interference which makes the resource allocation problem hard to solve. For this challenging scenario, we first consider off-line policies based on full channel state information (CSI) and energy arrival information (EAI) to obtain the best performance for any feasible resource allocation policies. We propose an iterative algorithm using generalized linear fractional programming to obtain an optimal policy. To achieve a low-complexity sub-optimal policy, we propose another iterative algorithm using the successive convex approximation. Based on the off-line policies, we develop on-line policies in which only statistical CSI and EAI are available. The complexity of the proposed policies is derived. Finally, simulation results evaluate the performance of the proposed approaches and show that the proposed polices outperform the benchmark.

This work proposes a framework to design a cost-efficient unmanned aerial vehicle (UAV)-based energy-neutral (EN) system deployed to harvest data from a set of internet-of-things (IoT) nodes. The energy-neutrality refers to the zero-sum balance between energy harvested, stored, and consumed during operation, which is a game-changer when a connection to the electricity grid is not available/feasible. This involves employing an off-grid charging station (CS) comprising of photovoltaic (PV) panels and batteries that provide enough energy to recharge the UAV-based aerial access points (AAPs). The investment cost is determined by the number of AAPs, PV panels, and ground battery units. Its minimization cannot be achieved using conventional optimization tools due to the non-tractable form of the CS load. Therefore, a novel wave-based method is proposed to represent the load profile as a proportional function of the required number of AAPs, so as to directly relate the CS design to the trajectory optimization. Compared to baseline scenarios, the proposed trajectory design can halve the time and energy consumption; the investment cost varies with the time and season of service; the off-grid CS is particularly advantageous in rural areas, while in urban areas its cost is comparable to that of a grid-connected alternative.

Over the past few years, there has been a growing interest in using unmanned aerial vehicles (UAVs) for high-rate wireless communication systems due to their highly flexible deployment and maneuverability. The aim of this article is to propose a 3-D multi-UAV deployment approach to provide Quality-of-Service (QoS) requirements for different types of user distributions in the presence of co-channel interference by maximizing the minimum achievable system throughput for all of the ground users. The proposed approach is divided into two separate algorithms. In the first algorithm, by using the mean-shift technique and prior knowledge of users' positions provided by the global positioning system (GPS), it has been shown that one can simultaneously find xy coordinates of UAVs, which are associated with the maximum of users' density and schedule users to UAVs. Once the xy-Cartesian coordinates of UAVs are determined, UAVs' altitudes and transmit powers are separately optimized. Since these problems are nonconvex optimizations, the successive convex optimization technique has been applied to approximate their nonconvex constraints. In the second algorithm, the block coordinate descent technique is leveraged to jointly optimize UAVs altitudes and transmit powers by tightening the bounds obtained for approximations. It is then proven that the suggested algorithm is guaranteed to converge. The computational complexity of the proposed placement approach is derived. Numerical experiments are carried out to evaluate the performance of our technique and show its superiority to conventional benchmarks.

A new estimator for sparse time dispersive channels in pilot aided orthogonal frequency division multiplexing (OFDM) systems is developed by considering prior knowledge on channel time dispersions. The authors propose a weighted atomic norm minimisation (WANM) in order to incorporate the prior information into the estimator. The dual of the WANM is then converted to a tractable semidefinite programming using positive trigonometric polynomial theory. After solving the dual problem, the channel response is identified by solving a least squares approach. In this work, they assume that time dispersions associated delays can take any value with a mild minimum separation condition on the normalised interval . The performance of the new estimator is compared with conventional approaches. With respect to the pilot number and signal to noise ratio (SNR), simulation results reveal that the proposed estimator performs superior to that of traditional methods. It is shown that both a lower SNR and number of pilots are required to achieve the same mean square error reported in previous works.