Antenna Grouping Assisted Spatial Modulation for mmWave-based UAV-BS

—The flexible deployment without new infrastructure makes unmanned aerial vehicles employing as base stations (UAV-BS) promising for many application. Since the signals in millimeter-wave frequencies have very small wavelengths, large antenna arrays can be placed in the UAV-BS. Thus, the UAV-BS is capable of providing abundant spatial resources. Spatial modulation (SM) is an effective technology in exploiting additional capacity of the spatial domain by transmitting antenna indices as virtual bits information. However, a limitation of the classical SM is a single transmit antenna activated at each time slot. As a result, the multiplexing gain offered by the multiple transmit antennas has a significantly loss. Generalised spatial modulation (GSM) allows several antennas to be activated to overcome the problem of SM. However, GSM has a improvement in throughput, while suffers from the performance loss resulting from the channel correlation, which is generated by multiple active antennas. Thus, the grouping SM (GrSM) is utilized to offer spatial capacity for the UAV-BS in mmWave frequency. Specially, the transmit antennas of the UAV-BS are partitioned into groups based on their channel characteristics. The SM is adopted by each group, and the multiplexing gain is achieved across groups. Moreover, the deployment of the UAV-BS has significantly influence on the throughput of the system. In this paper, we formulate a problem to maximize the achievable sum rate of the ground user. The GrSM scheme is utilized to obtain extra throughput in spatial domain. Since the dimension of the UAV position is not very high, a grid based exhaustive search method is adopted to solve the optimization problem. Simulation results demonstrate the proposed solution has an improvement in terms of the sum rate performance

Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-toground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earthorbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, groundand sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight, and shipping data in the North Atlantic region show that the integrated network enhances the coverage quality by reducing the end-to-end (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multiobjective routing algorithm is capable of achieving near Paretooptimal performance

Autonomous electrical current monitoring system for aircraft

Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A – 100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A low-power sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring

Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs.

The localization accuracy demand is ever growing in UAV communication networks. We propose a joint coarse and fine range estimation method using single carrier burst signals with two samples per symbol for UAV networks. The coarse estimation of our joint estimation method exploits multiple preamble symbols for flexible single-carrier frequency-domain equalization (SC-FDE) frame structures to calculate correlation metrics, which are insensitive to frequency offset due to the differential correlation operation. Then, we propose a fine range estimation method using only two samples per symbol with expectation relying on shaping or matched filter. Furthermore, we derive the performance bounds for the ranging system using both raised cosine (RC) and better than raised-cosine (BTRC) pulses. Finally, extensive simulations are conducted to validate the proposed method in terms of estimate bias and variance for different modulations, shaping filters, and fading channels. Our simulation results show that, the root mean square errors of proposed ranging method can reach the order of centimeter at medium-to-high signal-to-noise ratio (SNR) region, whereas the case using BTRC filter is capable of enhancing the ranging performance at low SNRs

Minimum-Delay Routing for Integrated Aeronautical Ad Hoc Networks Relying on Real Flight Data in the North-Atlantic Region

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for enhancing the on-demand connectivity of planes in the air. The goal of the paper is to assess the performance of the classic shortest-path routing algorithm in the context of the real flight data collected in the North-Atlantic Region. Specifically, in this integrated AANET context we investigate the shortest-path routing problem with the objective of minimizing the total delay of the in-flight connection from the ground BS subject to certain minimum-rate constraints for all selected links in support of lowlatency and high-speed services. Inspired by the best-first search and priority queue concepts, we model the problem formulated by a weighted digraph and find the optimal route based on the shortest-path algorithm. Our simulation results demonstrate that aircraft-aided multi-hop communications are capable of reducing the total delay of satellite communications, when relying on real historical flight data

Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.Comment: 19 pages, 14 figures, journa

Adaptive Coding and Modulation Aided Mobile Relaying for Millimeter-Wave Flying Ad-Hoc Networks

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate \emph{agility} and \emph{mobility} of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an $\epsilon$-multiple objective genetic algorithm ($\epsilon$-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering $45.38\%$ more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering $26.86\%$ more data than the benchmark solutions when no stationary relay is available

Physical Layer Security of Spatially Modulated Sparse-Code Multiple Access in Aeronautical Ad-Hoc Networking

For improving the throughput while simultaneously enhancing the security in aeronautical ad-hoc networking (AANET), a channel quality indicator (CQI)-mapped spatially modulated sparse code multiple access (SM-SCMA) scheme is proposed in this paper. On one hand, we exploit the joint benefits of spatial modulation and SCMA for boosting the data rate. On the other hand, a physical-layer secret key is generated by varying the SM-SCMA mapping patterns based on the instantaneous CQI in the desired link. This guarantees the security of AANETs, since this secret key is not exchanged between the source aeroplane and its destination. Due to the line-of-sight (LoS) propagation in the AANET, other aeroplanes or eavesdroppers may detect the signals delivered in the desired link. However, they are unable to translate the detected signals into the original confidential information, even if multiple copies of the signals are recoined over multiple hops of the AANET, because they have no knowledge of the CQI-based SM-SCMA mapping pattern. The performance of the CQI-mapped SM-SCMA is evaluated in terms of both its bit error rate and its ergodic secrecy rate, which substantiates that the proposed scheme secures the confidential information exchange in the multi-hop AANET