Optimal network planning for wireless communication systems requires detailed knowledge of the channel parameters of the target coverage area. Channel parameters can be estimated through extensive measurements in the environment. Alternatively, ray tracing simulations can be done if the 3D model of the environment is available. One drawback of ray tracing simulations is the high computational complexity; therefore, ray tracing is not suitable for real-time coverage optimization. In this paper, we present a deep convolutional neural network based approach to estimate channel parameters (specifically, path loss exponent and standard deviation of shadowing) directly from 2D satellite images. While deep learning methods require high computational resources for training and large amount of training data; once trained, the network can make predictions fast. Also, unlike the ray tracing simulations, there is no need for 3D model generation; so it can be applied easily using images obtained from satellites or aerial vehicles. These make the proposed method a computationally efficient and reliable alternative to ray tracing simulations. Experimental results show that path loss exponent and large-scale shadowing factor at 900 MHz can be correctly classified by 88% and 76% accuracy, respectively.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes (WSNs). In such a network, a UAV transmits wireless power to a group of WSNs which exploits the harvested power to transfer their own information towards the UAV in the uplink. For practical scenarios, a nonlinear energy harvesting saturation model is considered at WSNs. We aim to design resource allocation which maximizes the achievable sum rate in the uplink and minimizes the downlink transmit power simultaneously. The design is formulated as a multi-objective optimization problem (MOOP) which optimizes the UAV's 3D position, its transmit power, the time splitting ratio, the uplink transmission time and the beamwidth angle of the UAV. The formulated problem is a non-convex problem which is generally intractable. To address the MOOP, a weighted Tchebycheff method is proposed. The numerical results show a trade-off between the minimum power in the downlink and the maximum rate as well as energy efficiency in the uplink.

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.

UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance.

Full-duplex (FD) communication in many-antenna base stations (BSs) is hampered by self-interference (SI). This is because a FD node’s transmitting signal generates significant interference to its own receiver. Recent works have shown that it is possible to reduce/eliminate this SI in fully digital many-antenna systems, e.g., through transmit beamforming by using some spatial degrees of freedom to reduce SI instead of increasing the beamforming gain. On a parallel front, hybrid beamforming has recently emerged as a radio architecture that uses multiple antennas per FR chain. This can significantly reduce the cost of the end device (e.g., BS) but may also reduce the capacity or SI reduction gains of a fully digital radio system. This is because a fully digital radio architecture can change both the amplitude and phase of the wireless signal and send different data streams from each antenna element. Our goal in this paper is to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity, particularly in multi-user MIMO setups. To do so, we experimentally compare the performance of a state-of-the-art fully digital many antenna FD solution to a hybrid beamforming architecture and compare the corresponding performance metrics leveraging a fully programmable many-antenna testbed and collecting over-the-air wireless channel data. We show that SI cancellation through beam design on a hybrid beamforming radio architecture can achieve capacity within 16% of that of a fully digital architecture. The performance gap further shrinks with a higher number of quantization bits in the hybrid beamforming system.

Unmanned Aerial Vehicles (UAVs) have emerged as highly adaptable equipment in modern agriculture, transforming traditional farming practices with data-driven decision-making to improve crop yields. While UAVs are mainly employed for aerial imaging and surveillance, their potential for collecting underground (UG) sensor data is underexplored. This paper investigates the path loss and fading characteristics between UAV and UG nodes using outdoor measurements, to facilitate energy-efficient data collection for air-to-underground wireless links. We show significant impacts from UAV antenna position, UAV 3D location, and soil properties on both path loss and fading. A novel channel model is developed that estimates the path loss with an average RMSE improvement of 3.16 dB and a maximum improvement of up to 10.45 dB over previous models in various 3D positions of the UAV. The analysis extends to the fading distribution within the channel, which conforms to the Rician distribution, where the Rician-K is contingent upon the UAV’s altitude, elevation angle, and antenna configurations. Specifically, two Gaussian functions are derived to capture the effects attributed to the altitude and elevation angle, with root mean square errors (RMSEs) of 2.86 dB – 4.38 dB and 2.1 dB – 6.1 dB, respectively. The developed model is employed to drive an energy-efficient data collection strategy, UAV-Collect, for UAV-aided Internet of Underground Things. UAV-Collect substantially reduces the energy use of UG sensor nodes. Our approach has the potential to optimize resources on UAV and UG nodes, enabling efficient agricultural monitoring.