Discrete-Phase Sequence Design with Stopband and PSL Constraints for Cognitive Radar

in Proceedings of EuRAD 2020 (in press)

We present the design of discrete-phase sequences considering simultaneously the peak sidelobe level (PSL) and avoiding reserved frequency bands which are occupied by narrowband interferers or ... [more ▼]

We present the design of discrete-phase sequences considering simultaneously the peak sidelobe level (PSL) and avoiding reserved frequency bands which are occupied by narrowband interferers or communications. We use the coordinate descent framework and propose an algorithm to design discrete-phase sequences with spectral power suppressed in arbitrary bands and with low auto-correlation sidelobes in terms of PSL. Our proposed algorithm exploits fast Fourier transform and is, therefore, computationally efficient. The over-the-air experiments using implementation on software-defined radio show reasonable agreement with numerical simulations and feasibility for field-deployment [less ▲]

Terminal-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks

in IEEE Global Communications Conference (Globecom) (2023, January 12)

This paper introduces the concept of multi-connectivity (MC) to the multi-orbit non-terrestrial networks (NTNs), where user terminals can be served by more than one satellite to achieve higher peak ... [more ▼]

This paper introduces the concept of multi-connectivity (MC) to the multi-orbit non-terrestrial networks (NTNs), where user terminals can be served by more than one satellite to achieve higher peak throughput. MC is a technique initially introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications in 4G and 5G, it has shown much gain in the terrestrial domain and this paper explores areas where this concept can benefit the satellite domain. MC can increase throughput, but this entails increased power consumption at user terminal for uplink transmissions. The energy efficiency of uplink communications can be improved by designing efficient scheduling schemes, and to this end, we developed a terminal aware multi-connectivity scheduling algorithm. This proposed algorithm uses the available radio resources and propagation information to intelligently define a dynamic resource allocation pattern, that optimally routes traffic so as to maximize uplink data rate while minimizing the energy consumption at the UT. The algorithm operates with the terminal differentiating multi-layer NTN resource scheduling architecture, which has a softwarized dispatcher at the network layer that classifies and differentiates the packets based on terminal type. The performance of the proposed algorithm was compared with round robin and joint carrier schedulers in terms of uplink data rate and energy efficiency. We also provide architectural design of implementable schedulers for multi-orbital satellite networks that can operate with different classes of terminals. [less ▲]

Resource Allocation in Heterogeneously-Distributed Joint Radar-Communications Under Asynchronous Bayesian Tracking Framework

in IEEE Journal on Selected Areas in Communications (2022), 40(7), 2026-2042

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources ... [more ▼]

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our a lter n ating allo c ation of h eterogene o us r esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions. [less ▲]

Spatio-Temporal Rainfall Estimation from Communication Satellite Data using Graph Neural Networks

Scientific Conference (2022, May)

Contactless radar-based breathing monitoring of premature infants in the neonatal intensive care unit

in Scientific Reports (2022), 12(1), 1--15

Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital ... [more ▼]

Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital parameters and sensors need to be directly attached to their fragile skin. Besides mobility restrictions and stress, these sensors often cause skin irritation and may lead to pressure necrosis. In this work, we show that a contactless radar-based approach is viable for breathing monitoring in the Neonatal intensive care unit (NICU). For the first time, different scenarios common to the NICU daily routine are investigated, and the challenges of monitoring in a real clinical setup are addressed through different contributions in the signal processing framework. Rather than just discarding measurements under strong interference, we present a novel random body movement mitigation technique based on the time-frequency decomposition of the recovered signal. In addition, we propose a simple and accurate frequency estimator which explores the harmonic structure of the breathing signal. As a result, the proposed radar-based solution is able to provide reliable breathing frequency estimation, which is close to the reference cabled device values most of the time. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a completely contactless solution for vital signs monitoring. [less ▲]

Joint Multislot Scheduling and Precoding for Unicast and Multicast Scenarios in Multiuser MISO Systems

in IEEE Transactions on Wireless Communications (2022), 21(7), 5004-5018

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO ... [more ▼]

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO downlink systems. In the literature, the joint design of scheduling and precoding is typically undertaken based on feedback from previous slots. In a system with time-varying channels and QoS requirements, joint multislot designs can achieve better performance since they have the flexibility to schedule users over multiple slots and also can split users across slots efficiently. Further, a joint multislot design can provide a feasible solution even when the sequential design fails. In this paper, scheduling is represented by a binary matrix where the rows represent users, columns represent slots and entries represent scheduling of users in the slots. Noticing that the users may not be permuted across slots for time-varying channels, service time needed for scheduling is rendered as the highest column index corresponding to non-zero columns. With the help of binary scheduling matrix, service time minimization is formulated as a structured mixed-Boolean fractional programming. Further, by exploiting the hidden convex-concave structure in the problem, a convex-concave procedure-based iterative algorithm is proposed. Finally, we vindicate the necessity and illustrate the superiority in performance of joint multislot design over the sequential solution through Monte-Carlo simulations. [less ▲]

Recurrent Design of Probing Waveform for Sparse Bayesian Learning Based DOA Estimation

in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL ... [more ▼]

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL-based estimation error is related to the transmitted probing waveform. Therefore, it is expected to improve the estimation by waveform optimization. In this paper, we propose a recurrent scheme of waveform design by sequentially leveraging on the previous-round SBL estimates. Within this scheme, we formulate the waveform design problem as a minimization of the SBL estimation variance, which is non-convex and then solved by a majorization-minimization based algorithm. The simulations demonstrate the efficacy of the proposed design scheme in terms of avoiding incorrect detection and accelerating the DOA estimation convergence. Further, the results indicate that the waveform design is essentially a beampattern shaping methodology. [less ▲]

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

in IEEE Transactions on Vehicular Technology (2022), 71(4), 4359-4372

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time ... [more ▼]

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a u nified op t imization fra m ework for o ptimal s ensor placemen t (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework. [less ▲]

Statistical Performance Analysis of Radar-Based Vital-Sign Processing Techniques

Scientific Conference (2022)

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a ... [more ▼]

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a formal comparison between them. In addition, collection of real data is a time-consuming process and therefore most of the proposed solutions are only evaluated under very limited scenarios. In this paper we present a simulation framework and a selection of results which allow easy performance comparison between radar-based vital-sign processing techniques. The proposed simulation tool scans over multiple breathing and heartbeat frequencies, and the combined effects along the entire signal processing chain can be analyzed, for different combinations of scenarios and techniques. The results have shown specific limitations for each method, thus indicating a need for proper selection based on operating conditions. In addition, while breathing estimation performance is only limited by noise, heartbeat estimation is limited by the presence of breathing harmonics and, despite promising results at specific breathing/heartbeat frequencies, the presented methods fail to fully mitigate this type of interference in all scenarios. [less ▲]

Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

in IEEE Journal of Selected Topics in Signal Processing (2022)

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a ... [more ▼]

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements. [less ▲]