Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Collaborative Data Transmission in Wireless Sensor Networks

grant TR32043 grant III44003 grant III43002Collaborative beamforming (CBF) is a promising technique aimed at improving energy efficiency of communication in wireless sensor networks (WSNs) which has attracted considerable attention in the research community recently. It is based on a fact that beampattern with stable mainlobe can be formed, if multiple sensors synchronize their oscillators and jointly transmit a common message signal. In this paper, we consider application of CBF with one bit of feedback in different communication scenarios and analyze the impact of constraints imposed by simple sensor node hardware, on the resulting signal strength. First, we present a CBF scheme capable of reducing interference levels in the nearby WSN clusters by employing joint feedback from multiple base stations that surround the WSN of interest. Then, we present a collaborative power allocation and sensor selection algorithm, capable of achieving beamforming gains with transmitters that are not able to adjust their oscillators' signal phase. The performance of the algorithms is assessed by means of achieved beamforming gain which is given as a function of algorithm iterations. The presented results, which are based on numerical simulations and mathematical analysis, are compared with the ideal case without constraints and with negligible noise at the Base Station (BS).publishersversionpublishe

Antenna Array Enabled Space/Air/Ground Communications and Networking for 6G

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays with various beamforming technologies in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

Bat algorithm–based beamforming for mmWave massive MIMO systems

© 2019 John Wiley & Sons, Ltd. In this paper, an optimized analog beamforming scheme for millimeter-wave (mmWave) massive MIMO system is presented. This scheme aims to achieve the near-optimal performance.by searching for the optimized combination of analog precoder and combiner. In order to compensate for the occurrence of attenuation in the magnitude of mmWave signals, the codebook-dependent analog beamforming in conjunction with precoding at transmitting end and combining signals at the receiving end is utilized. Nonetheless, the existing and traditional beamforming schemes involve a more difficult and complicated search for the optimal combination of analog precoder/combiner matrices from predefined codebooks. To solve this problem, we have referred to a modified bat algorithm to find the optimal combination value. This algorithm will explore the possible pairs of analog precoder/combiner as a way to come up with the best match in order to attain near-optimal performance. The analysis shows that the optimized beamforming scheme presented in this paper can improve the performance that is very close to the beam steering benchmark that we have considered.Published versio