Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks

Joint Collaborative Radar Selection and Transmit Resource Allocation in Multiple Distributed Radar Networks with Imperfect Detection Performance

In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms

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

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

OFDM passive radar employing compressive processing in MIMO configurations

A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability

Mobile 5G millimeter-wave multi-antenna systems

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Universitat Politècnica de Catalunya's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.Tesi en modalitat de compendi de publicacionsMassive antenna architectures and millimeter-wave bands appear on the horizon as the enabling technologies of future broadband wireless links, promising unprecedented spectral efficiency and data rates. In the recently launched fifth generation of mobile communications, millimetric bands are already introduced but their widespread deployment still presents several feasibility issues. In particular, high-mobility environments represent the most challenging scenario when dealing with directive patterns, which are essential for the adequate reception of signals at those bands. Vehicular communications are expected to exploit the full potential of future generations due to the massive number of connected users and stringent requirements in terms of reliability, latency, and throughput while moving at high speeds. This thesis proposes two solutions to completely take advantage of multi-antenna systems in those cases: beamwidth adaptation of cellular stations when tracking vehicular users based on positioning and Doppler information and a tailored radiation diagram from a panel-based system of antennas mounted on the vehicle. Apart from cellular base stations and vehicles, a third entity that cannot be forgotten in future mobile communications are pedestrians. Past generations were developed around the figure of human users and, now, they must still be able to seamlessly connect with any other user of the network and exploit the new capabilities promised by 5G. The use of millimeter-waves is already been considered by handset manufacturers but the impact of the user (and the interaction with the phone) is drastically changed. The last part of this thesis is devoted to the study of human user dynamics and how they influence the achievable coverage with different distributed antenna systems on the phone.Les arquitectures massives d'antenes i les bandes mil·limètriques apareixen a l'horitzó com les tecnologies que impulsaran els futurs enllaços sense fils amb gran ample de banda i prometen una eficiència espectral i velocitat de transmissió sense precedents. A la recent cinquena generació de comunicacions mòbils, les bandes mil·limètriques ja en són una part constitutiva però el seu desplegament encara presenta certes dificultats. En concret, els entorns d'alta mobilitat representen el major repte quan es fan servir diagrames de radiació directius, els quals són essencials per una correcta recepció del senyal en aquestes bandes. S'espera que les comunicacions vehiculars delimitin les capacitats de les xarxes en futures generacions degut al gran nombre d'usuaris simultanis i els requeriments estrictes en termes de fiabilitat, retard i flux de dades mentre es mouen a grans velocitats. Aquesta tesi proposa dues solucions per tal d'explotar al màxim els sistemes de múltiples antenes en tals casos: un ample de feix adaptatiu de les estacions bases quan estiguin fent el seguiment d'un vehicle usuari basat en informació de la posició i el Doppler i el disseny d'un diagrama de radiació adequat al costat del vehicle basat en una estructura de múltiples panells muntats a l'estructura del mateix. A més de les estacions base i els vehicles, un tercer element que no pot ser obviat en aquests escenaris són els vianants. Les generacions anteriors van ser desenvolupades al voltant de la figura d'usuaris humans i ara han de seguir tenint la capacitat de connexió ininterrumpuda amb la resta d'usuaris i explotar les capacitats de 5G. L'ús de frequències mil·limètriques també es té en compte en la fabricació de telèfons mòbils però l'impacte de l'usuari és completament diferent. La última part de la tesis tracta l'estudi de les dinàmiques de l'usuari humà i com influeixen en la cobertura amb diferent sistemes distribuïts d'antenes.Postprint (published version

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