A vast array of potential applications is emerging for drones and other devices to collaborate from disaster relief and search and rescue missions to smart agriculture and IoT systems. As drones move across multiple altitudes, they must have the ability to communicate across any direction in a three-dimensional (3D) space. However, due to the heterogeneous nature of the drone body and its interaction with the mounted antennas, different antenna positions on the drone can result in variations in the radiation pattern. While there have been a fair number of airborne communication works, few consider the role that antenna positioning has on the resulting transmission along the azimuth and elevation planes. In this work, we study the effects of the drone body and various antenna placements on the radiation pattern and fading of drone-based channels. Through systematic anechoic chamber and in-field measurements, we show that the drone body alters the radiation pattern of the mounted antennas, rendering the common assumption of a constant azimuth radiation pattern invalid. In addition, the body increases polarization mixing of drone-based channels, resulting in significant degradation of the cross-polarization discrimination (XPD). Hence, we propose using effective radiation pattern and XPD values instead of relying on measurements and/or assumptions that disregard drone-antenna interaction. We then analyze the shadowing and losses associated with the drone body for many antenna setups at various elevation angles and show that when mounted on the opposite side from the ground transmitter, shadowing increases with relatively-higher drone elevations. To account for these body-induced effects, we introduce rotational loss that results in better prediction results of the large-scale fading behavior compared to conventional models that neglect these body effects. Then, we analyze the small-scale fading for various antenna setups and show that the Rician K-factor is strongly dependent on elevation for polarization-matched vertical links, while it is approximately flat for cross-polarized links. To do so, we conduct a set of drone-to-drone (DtD) experiments at high altitudes with no surrounding reflectors and compare results to those obtained by our ground-to-drone (GtD) measurements. We find that, while at low elevations the ground can reduce the K-factor by 10 dB, at higher elevations, small-scale fading is dominated by the antennas, not the ground.

Accurate channel state information (CSI) is essential to increasing throughput in multi-input, multi-output (MIMO) systems with digital beamforming. CSI can be acquired by channel estimation and reported via feedback mechanisms. While the training and feedback overhead are typically proportional to the number of antennas, uplink measurements can be utilized to predict downlink CSI assuming perfect channel reciprocity in a time division duplex (TDD) mode. However, many works make the assumption that channels are perfectly reciprocal, which is often not the case in practice due to poor channel estimation and physical channel asymmetry. In this work, we investigate the key challenges in channel feedback performance, including TX-RX imbalance, channel coherence, and interference for mobile systems. We evaluate IEEE 802.11ac-based implicit and explicit feedback schemes with both emulated and in-field MIMO channels, particularly in regards to drone-based transmissions. Our analysis with channel emulation shows that implicit feedback is susceptible to channel reciprocity errors, while explicit feedback is more sensitive to Doppler effects. We propose a hybrid feedback mechanism that increases the throughput by 32% over conventional feedback methods. We additionally evaluate the impact of frequency offset asymmetry on the performance of distributed mobile systems. Our in-field experiments demonstrate that explicit feedback can provide better throughput improvement than implicit feedback in highly-mobile air-to-ground channels. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding the impacts of asymmetric factors of channel reciprocity for drone-based beamforming systems.

At ultra-low altitudes, an unmanned aerial vehicle (UAV) can act as a personal base station where it communicates only with one user. User equipment (UE) can be inside the pocket of a user or near their chest while facing or facing-away from the flying base station. In these scenarios, the wireless channel can go through different fading levels, depending on the UAV's location, user orientation, the location of the UE near the user's body, and the frequency of the transmitted signal. In this work, we provide measurement results and investigate how the human body affects the Air-to-Ground (AtG) channel characteristics under various use cases of holding a device. These channel characteristics include the average signal strength, shadowing, multipath, and the Rician K-factor. We target three different use cases of holding the device: Near-Chest Facing, In-pocket Facing, and Near-Chest Facing-away from the transmitting UAV. We perform this study at carrier frequencies of 900 MHz and 2.5 GHz and in Line-of-Sight conditions. First, we conduct a set of baseline experiments to understand ground-to-ground and AtG channels in free space. Second, we conduct AtG experiments with the user holding the device and show that the human body can induce either gains or losses compared to free space, depending on its relative orientation to the UAV. Third, we find that there are two distinct regions of operation, one in which the channel characteristics are mainly affected by the UAV and another that is dominated by the user's body. Fourth, we address the time-varying nature of the K-factor and analyze the user's body impact on its mean and standard deviation. We find that if a user changes their orientation to face away from the transmitting UAV, their body can cause extreme fluctuations in the K-factor value over time and reduce its mean value by an average and a maximum of 6.8 and 15 dB, respectively. Finally, to demonstrate the impact of our findings on the design of deployment strategies of UAVs, we considered how the human body and the relative UAV hovering position can affect the physical layer security in a UAV-assisted network. We show that the secrecy rate for a UAV-based network can be heavily influenced by the human body orientation relative to the UAV hovering location, consequently resulting in a different optimal deployment strategy compared to existing schemes that employ free-space pathloss models that neglect such human-induced effects.

In this article, we establish a novel framework for a multi-user mobile edge computing (MEC) network in which a set of users with high downlink rate demands and a set of users with intensive computation tasks can collaborate to achieve a mutually-beneficial scenario such that completion time of the tasks is reduced and the base station (BS) can send more information at a higher rate to the downlink users. Specifically, by leveraging non-orthogonal multiple access (NOMA) for uplink and downlink traffic, the user with the computation task can offload shares of the computation task to the edge cloud and the downlink user. At the same time, this user forwards the information it receives from the BS to the downlink user. In this set up, we jointly optimize the communication resources, computational resources at the edge cloud and user devices, pairings among the two sets of users, the shares of computation tasks, and relay bits to minimize the total task completion time while satisfying downlink users’ incentive requirements. For a network with a single computation demanding user and a single downlink user, the optimal solution to the problem is provided. For a network with multiple users, the problem is non-convex and computationally challenging. Hence, we propose an efficient, low complexity algorithm that utilizes the bottleneck matching algorithm, convex optimization, and the block coordinate descent scheme to obtain a locally-optimal solution. Simulation results demonstrate that, as compared with the state-of-the-art, the total task completion time is greatly reduced (32%–51%), and a large computational energy savings at the edge cloud (38%–55%) is achieved. Simultaneously, the downlink users’ rates improve compared to the orthogonal transmission.

In this paper, we propose a multi-band medium access control (MAC) protocol for an infrastructure-based network with an access point (AP) that supports In-Band full-duplex (IBFD) and multiuser transmission to multi-band-enabled stations. The Multi-Band Full Duplex MAC (MB-FDMAC) protocol mainly uses the sub-6 GHz band for control-frame exchange, transmitted at the lowest rate per IEEE 802.11 standards, and uses the 60 GHz band, which has significantly higher instantaneous bandwidth, exclusively for data-frame exchange. We also propose a selection method that ensures fairness among uplink and downlink stations. Our result shows that MB-FDMAC effectively improves the spectral efficiency in the mmWave band by 324%, 234%, and 189% compared with state-of-the-art MAC protocols. In addition, MB-FDMAC significantly outperforms the combined throughput of sub-6 GHz and 60 GHz IBFD multiuser MIMO networks that operate independently by more than 85%. In addition, we study multiple network variables such as the number of stations in the network, the percentage of mmWave band stations, the size of the contention stage, and the selection method on MB-FDMAC by evaluating the change in the throughput, packet delay, and fairness among stations. Finally, we propose a method to improve the utilization of the high bandwidth of the mmWave band by incorporating time duplexing into MB-FDMAC, which we show can enhance the fairness by 12.5 % and significantly reduces packet delay by 80%.