Novel Models and Algorithms Paving the Road towards RF Convergence

After decades of rapid evolution in electronics and signal processing, the technologies in communications, positioning, and sensing have achieved considerable progress. Our daily lives are fundamentally changed and substantially defined by the advancement in these technologies. However, the trend is challenged by a well-established fact that the spectrum resources, like other natural resources, are gradually becoming scarce. This thesis carries out research in the field of RF convergence, which is regarded as a mean to intelligently exploit spectrum resources, e.g., by finding novel methods of optimising and sharing tasks between communication, positioning, and sensing. The work has been done to closely explore opportunities for supporting the RF convergence. As a supplement for the electromagnetic waves propagation near the ground, ground-to-air channel models are first proposed and analysed, by incorporating the atmospheric effects when the altitude of aerial users is higher than 300 m. The status quos of techniques in communications, positioning, and sensing are separately reviewed, and our newly developments in each field are briefly introduced. For instance, we study the MIMO techniques for interference mitigation on aerial users; we construct the reflected echoes, i.e., the radar receiving, for the joint sensing and communications system. The availability of GNSS signals is of vital importance to the GNSS-enabled services, particularly the life-critical applications. To enhance the resilience of GNSS receivers, the RF fingerprinting based anti-spoofing techniques are also proposed and discussed. Such a guarantee on GNSS and ubiquitous GNSS services drive the utilisation of location information, also needed for communications, hence the proposal of a location-based beamforming algorithm. The superposition coding scheme, as an attempt of the waveform design, is also brought up for the joint sensing and communications. The RF convergence will come with many facets: the joint sensing and communications promotes an efficient use of frequency spectrum; the positioning-aided communications encourage the cooperation between systems; the availability of robust global positioning systems benefits the applications relying on the GNSS service

Exploiting the location information for adaptive beamforming in transport systems

As mobile communication systems evolve, the demand for enhanced network efficiency and pinpoint accuracy in user localization grows, particularly in the context of dynamic environments such as transport systems. This thesis is motivated by the critical challenge of adapting beamforming techniques to the rapidly changing positions of users, a task analogous to hitting a moving target with precision. The aim is to significantly improve cellular network performance by leveraging advanced beamforming and machine learning (ML) for precise user localization. A novel dataset, crucial to this endeavor, has been developed from simulations in open spaces and a digital twin of the University of Glasgow campus, incorporating vital parameters such as direction of arrival (DoA), time of arrival (ToA), and received signal strength indicators (RSSI). Our investigation commences with the deployment of Maximum Ratio Transmission (MRT) and Zero Forcing (ZF) beamforming techniques to evaluate their effectiveness in enhancing network efficiency through both real and simulated user locations. The application of an adaptive MRT algorithm in our beamforming strategy resulted in a remarkable 53% increase in Signal-to-Noise Ratio (SNR), showcasing the potential of contextual beamforming (Cont-BF) using location information. Furthermore, to refine localization accuracy, deep neural networks were employed, achieving a localization error of less than 1 meter surpassing conventional methods in accuracy. This research also introduces technique for user-assisted beam alignment in high-speed scenarios, addressing the challenges in dynamic transport systems. Venturing beyond traditional approaches, it explores the integration of user locations into beamforming decisions via a P4 switch, crafting a dynamic system responsive to user mobility. This is complemented by extensive data collection from 5G base stations (BS) using a TSMA 6 scanner, which enriches our analysis with detailed parameters for precision localization. Moreover, the study evaluates various MIMO beamforming techniques in 5G networks, demonstrating an average throughput increase from 9 Mbps to 14 Mbps, thereby underscoring the effectiveness of our proposed solutions. The potential of low-cost Software Defined Radios (SDR) forDoA estimation and the design of a beam steering setup was also assessed, aiming to evaluate their utility in highfrequency beamforming. Despite uncovering limitations in sub-6GHz environments, this exploration led to the successful development of a DoA estimation setup using USRPs and antennas, alongside a beam steering system crafted through the design of phase shifters and antennas. By integrating precise location information into adaptive beamforming techniques, especially within the dynamic context of transport systems, this thesis underscores the imperative role of such integration in significantly enhancing communication efficiency. Our findings, which include significant improvements in signal-to-interference-to-noise ratio (SINR) (up to 50%) and received power (up to 40%) through advanced beamforming methods, are pivotal for advancing high-demand applications, including smart vehicles and immersive virtual reality. This marks a crucial advancement towards the realization of next-generation cellular networks, paving the way for more efficient and reliable performance in an evolving technological landscape

Autonomous Swarm Navigation

Robotic swarm systems attract increasing attention in a wide variety of applications, where a multitude of self-organized robotic entities collectively accomplish sensing or exploration tasks. Compared to a single robot, a swarm system offers advantages in terms of exploration speed, robustness against single point of failures, and collective observations of spatio-temporal processes. Autonomous swarm navigation, including swarm self-localization, the localization of external sources, and swarm control, is essential for the success of an autonomous swarm application. However, as a newly emerging technology, a thorough study of autonomous swarm navigation is still missing. In this thesis, we systematically study swarm navigation systems, particularly emphasizing on their collective performance. The general theory of swarm navigation as well as an in-depth study on a specific swarm navigation system proposed for future Mars exploration missions are covered. Concerning swarm localization, a decentralized algorithm is proposed, which achieves a near-optimal performance with low complexity for a dense swarm network. Regarding swarm control, a position-aware swarm control concept is proposed. The swarm is aware of not only the position estimates and the estimation uncertainties of itself and the sources, but also the potential motions to enrich position information. As a result, the swarm actively adapts its formation to improve localization performance, without losing track of other objectives, such as goal approaching and collision avoidance. The autonomous swarm navigation concept described in this thesis is verified for a specific Mars swarm exploration system. More importantly, this concept is generally adaptable to an extensive range of swarm applications

Signal Processing for Large Arrays: Convolutional Beamspace, Hybrid Analog and Digital Processing, and Distributed Algorithms

The estimation of the directions of arrival (DOAs) of incoming waves for a passive antenna array has long been an important topic in array signal processing. Meanwhile, the estimation of the MIMO channel between a transmit antenna array and a receive antenna array is a key problem in wireless communications. In many recent works on these array processing tasks, people consider millimeter waves (mmWaves) due to their potential to offer more bandwidth than the already highly occupied lower-frequency bands. However, new challenges like strong path loss at the high frequencies of mmWaves arise. To compensate for the path loss, large arrays, or massive MIMO, are used to get large beamforming gain. It is practical due to the small sizes of mmWave antennas. When large arrays are used, it is important to develop efficient estimation algorithms with low computational and hardware complexity. The main contribution of this thesis is to propose low-complexity DOA and channel estimation methods that are especially effective for large arrays. To achieve low complexity, three main aspects are explored: beamspace methods, hybrid analog and digital processing, and distributed algorithms. First, a new beamspace method, convolutional beamspace (CBS), is proposed for DOA estimation based on passive arrays. In CBS, the array output is spatially filtered, followed by uniform decimation (downsampling) to achieve dimensionality reduction. No DOA ambiguity occurs since the filter output is represented only by the passband sources. CBS enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. Moreover, unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for uniform linear arrays without additional preparation since the Vandermonde structure is preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better estimates than element-space. The idea of hybrid analog and digital processing is then incorporated into CBS, leading to hybrid CBS for DOA estimation. In hybrid processing, an analog combiner is used to reduce the number of radio frequency (RF) chains and thus hardware complexity. Also for lowering hardware cost, the analog combiner is designed as a phase shifter network with unit-modulus entries. It is shown that any general (arbitrary coefficient) CBS filter can be implemented despite the unit-modulus constraints. Moreover, a new scheme of CBS is proposed based on nonuniform decimation and difference coarray method. This allows us to identify more sources than RF chains. The retained samples correspond to the sensor locations of a virtual sparse array, dilated by an integer factor, which results in larger coarray aperture and thus better estimation performance. Besides, with the use of random or deterministic filter delays that vary with snapshots, a new method is proposed to decorrelate sources for the coarray method to work. Next, a 2-dimensional (2-D) hybrid CBS method is developed for mmWave MIMO channel estimation. Since mmWave channel estimation problems can be formulated as 2-D direction-of-departure (DOD) and DOA estimation, benefits of CBS such as low complexity are applicable here. The receiver operation is again filtering followed by decimation. A key novelty is the use of a proper counterpart of CBS at the transmitter—expansion (upsampling) followed by filtering—to reduce RF chains. The expansion and decimation can be either uniform or nonuniform. The nonuniform scheme is used with 2-D coarray method and requires fewer RF chains to achieve the same estimation performance as the uniform scheme. A method based on the introduction of filter delays is also proposed to decorrelate path gains, which is crucial to the success of coarray methods. It is shown that given fixed pilot overhead, 2-D hybrid CBS can yield more accurate channel estimates than previous methods. Finally, distributed (decentralized) algorithms for array signal processing are studied. With the potential of reducing computation and communication complexity, distributed estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years. Applications in array processing have been also indicated in some detail. In this thesis, distributed algorithms are further developed for several well-known methods for DOA estimation and beamforming. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, distributed implementation of the spatial smoothing method for coherent sources, and distributed realization of CBS. The proposed algorithms are fully distributed since average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a finite-time version of AC which converges to the exact solution in a finite number of iterations. This enables the proposed distributed algorithms to achieve the same performance as the centralized counterparts, as demonstrated by simulations.</p

Acronym dictionary

This reference was originally compiled as a tool for abstracters who need to know the expansion of acronyms they may encounter in the texts they are analyzing. It is a general rule of abstracting at the NASA Center For Aerospace Information (CASI) that acronyms are expanded in the abstract to enhance both information content and searchability. Over the last 22 years, abstracters at CASI have recorded acronyms and their expansions as they were encountered in documents. This is therefore an ad-hoc reference, rather than a systematic collection of all acronyms related to aerospace science and technology