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See detailDiscrete-Phase Sequence Design with Stopband and PSL Constraints for Cognitive Radar
Alaeekerahroodi, Mohammad UL; Kumar, Sumit UL; Mysore Rama Rao, Bhavani Shankar UL et al

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 ▲]

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See detailTerminal-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks
Dazhi, Michael UL; Al-Hraishawi, Hayder UL; Mysore Rama Rao, Bhavani Shankar UL et al

Scientific Conference (2022, December)

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 ▲]

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See detailSpatio-Temporal Rainfall Estimation from Communication Satellite Data using Graph Neural Networks
Krebs, Julian UL; Mishra, Kumar Vijay; Gharanjik, Ahmad et al

Scientific Conference (2022, May)

Detailed reference viewed: 33 (2 UL)
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See detailUplink Capacity Optimization for High Throughput Satellites using SDN and Multi-Orbital Dual Connectivity
Dazhi, Michael UL; Al-Hraishawi, Hayder UL; Mysore Rama Rao, Bhavani Shankar UL et al

Scientific Conference (2022)

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for ... [more ▼]

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications, it is not been widely explored in satellite systems. In this paper, Dual Connectivity is implemented in a multi-orbital satellite network, where a network model is developed by employing the diversity gains from Dual Connectivity and Carrier Aggregation for the enhancement of satellite uplink capacity. An introduction of software defined network controller is performed at the network layer coupled with a carefully designed hybrid resource allocation algorithm which is implemented strategically. The algorithm performs optimum dynamic flow control and traffic steering by considering the availability of resources and the channel propagation information of the orbital links to arrive at a resource allocation pattern suitable in enhancing uplink system performance. Simulation results are shown to evaluate the achievable gains in throughput and latency; in addition we provide useful insight in the design of multi-orbital satellite networks with implementable scheduler design. [less ▲]

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See detailPhysics-Based Cognitive Radar Modeling and Parameter Estimation
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Mishra, Kumar Vijay et al

in IEEE Radar Conference (2022)

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the ... [more ▼]

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the similarity between the channel impulse responses (CIRs) of the adjacent channels. We develop a constrained CIR estimation (CCIRE) algorithm enhancing estimation performance compared to the unconstrained CIR estimation where the similarity between the CIRs of the adjacent channels is not employed. Further, we we derive the Cram\'{e}r-Rao bound (CRB) for the CCIRE and show the optimality of the proposed CCIRE through comparing its performance with the derived CRB. [less ▲]

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See detailA family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO.
Elbir, Ahmet M.; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Cognitive Communications and Networking (2021)

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive ... [more ▼]

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment. [less ▲]

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See detailHeterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation
Wu, Linlong; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2021, November 15)

Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneously-distributed joint radar ... [more ▼]

Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneously-distributed joint radar-communication system is promising due to its flexibility and compatibility of existing architectures. In this paper, we focus on a heterogeneous radar and communication network (HRCN), which consists of various generic radars for multiple target tracking (MTT) and wireless communications for multiple users. We aim to improve the MTT performance and maintain good throughput levels for communication users by a well-designed resource allocation. The problem is formulated as a Bayesian Cramér-Rao bound (CRB) based minimization subjecting to resource budgets and throughput constraints. The formulated nonconvex problem is solved based on an alternating descent-ascent approach. Numerical results demonstrate the efficacy of the proposed allocation scheme for this heterogeneous network. [less ▲]

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See detailEnhanced Automotive Target Detection through Radar and Communications Sensor Fusion
Dokhanchi, Sayed Hossein UL; Mysore Rama Rao, Bhavani Shankar UL; Mishra, Kumar Vijay et al

in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021, May 13)

This paper shows the enhancement in detection performance in an automotive scenario by leveraging the backscattered communication signals from vehicles at the target scene. A sensor fusion algorithm is ... [more ▼]

This paper shows the enhancement in detection performance in an automotive scenario by leveraging the backscattered communication signals from vehicles at the target scene. A sensor fusion algorithm is proposed to benefit from the information from radar and communication to improve the final range estimates. We demonstrate theoretically and illustrate through simulation that our proposed scheme enhances the radar detection performance. Thus the proposed scheme offers a solution for augmenting existing sensing capabilities to enhance detecting capabilities in a dynamic automotive scenario. [less ▲]

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See detailAdaptive Waveform Design for Automotive Joint Radar-Communication Systems
Dokhanchi, Sayed Hossein; Mysore Rama Rao, Bhavani Shankar UL; Alaeekerahroodi, Mohammad UL et al

in IEEE Transactions on Vehicular Technology (2021), 70(5), 4273-4290

Unified waveform design for automotive joint radar-communications (JRC) leverages the scarce spectrum efficiently and has become a key topic for investigation of late. Designing such a waveform ... [more ▼]

Unified waveform design for automotive joint radar-communications (JRC) leverages the scarce spectrum efficiently and has become a key topic for investigation of late. Designing such a waveform necessitates meeting the requirements of both systems, thereby making it a challenging task. The contribution of this paper is to formulate the JRC design problem into an optimization problem and propose an algorithm to maximize the signal-to-clutter-plus-noise-ratio (SCNR) of radar system and signal-to-noise-ratio (SNR) at communicating vehicle, simultaneously. Central to this are the exploitation of the communication link to acquire environment/ channel information and enhance radar tasks, flexibility to impart trade-off between the two systems during design as well the formulation of the optimization problem to include sidelobe constraints and yield solutions robust to Doppler shifts. The designed waveforms exhibit enhanced radar performance in terms of probability of detection and communication performance in terms of bit error rate (BER), while taking into account the trade-off between two systems. The numerical simulations corroborate the claim of optimized performance with environment/ channel information, ease of effecting trade-off and the use of design flexibility. [less ▲]

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See detailMulticasting Precoder Design for Vehicular Joint Radar-Communication Systems
Dokhanchi, Sayed Hossein UL; Mysore Rama Rao, Bhavani Shankar UL; Kobayashi, Mari et al

in 2021 1st IEEE International Online Symposium on Joint Communications & Sensing (JC&S) (2021, March 16)

We consider the problem of multicasting a single data stream to multiple vehicles in a vehicular network from a joint radar and communication (JRC) equipped vehicle that simultaneously aims to detect ... [more ▼]

We consider the problem of multicasting a single data stream to multiple vehicles in a vehicular network from a joint radar and communication (JRC) equipped vehicle that simultaneously aims to detect multiple targets and estimate their localization parameters such as ranges, Doppler shifts and angles. Assuming channel state information (CSI) is known at the JRC car, we design a precoder that exploits to maximize multicasting rate while simultaneously maximizing the radar Signal to Clutter plus Noise Ratio (SCNR) at the JRC vehicle. [less ▲]

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See detailAutomotive Squint-forward-looking SAR: High Resolution and Early Warning
Hu, Ruizhi UL; Mysore Rama Rao, Bhavani Shankar UL; Murtada, Ahmed Abdelnaser Elsayed UL et al

in IEEE Journal of Selected Topics in Signal Processing (2021)

Forward-looking automotive radars can sense long-distant targets to enable early warning, but the lateral resolution is limited. Synthetic aperture radar (SAR) techniques can achieve very high azimuth ... [more ▼]

Forward-looking automotive radars can sense long-distant targets to enable early warning, but the lateral resolution is limited. Synthetic aperture radar (SAR) techniques can achieve very high azimuth resolution but cannot resolve targets in the forward direction. As a trade-off, squint-forward-looking SAR (SFL-SAR) can perform 2D imaging on a distant area squint to the moving direction, providing both high resolution and early warning. In this paper, we analyzed and derived the constraints of automotive SFL-SAR to satisfy both the required resolution and braking distance. Simulations and imaging results verified the analysis. [less ▲]

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See detailPrecoding for Satellite Communications: Why, How and What next?
Mysore Rama Rao, Bhavani Shankar UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2021)

Detailed reference viewed: 126 (17 UL)
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See detailSpatial- and Range- ISLR Trade-off in MIMO Radar Systems via Waveform Design
Raei, Ehsan UL; Alaeekerahroodi, Mohammad UL; Mysore Rama Rao, Bhavani Shankar UL

in IEEE Transactions on Signal Processing (2021)

This paper aims to design a set of transmit waveforms in cognitive colocated Multi-Input Multi-Output (MIMO) radar systems considering the simultaneous minimization of the contradictory objectives of ... [more ▼]

This paper aims to design a set of transmit waveforms in cognitive colocated Multi-Input Multi-Output (MIMO) radar systems considering the simultaneous minimization of the contradictory objectives of spatial- and the range- Integrated Sidelobe Level Ratio (ISLR). The design problem is formulated as a bi-objective Pareto optimization under practical constraints on the waveforms, namely total transmit power, peak-to-average-power ratio (PAR), constant modulus, and discrete phase alphabet. A Coordinate Descent (CD) based approach is proposed where the solution in each iteration is handled through novel methodologies designed in the paper. The simultaneous optimization leads to a trade-off between the two ISLRs and the simulation results illustrate significantly improved trade-off offered by the proposed methodologies. [less ▲]

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See detailTransmit Beampattern Synthesis for Planar Array with One-bit DACs
Wei, Tong UL; Wu, Linlong UL; Alaeekerahroodi, Mohammad UL et al

Scientific Conference (2021)

Detailed reference viewed: 23 (7 UL)
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See detailOn the Performance of One-Bit DoA Estimation via Sparse Linear Arrays
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE Transactions on Signal Processing (2021)

Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in ... [more ▼]

Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in resolving uncorrelated source signals. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has emerged as an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. In this paper, we study the problem of DoA estimation from one-bit measurements received by an SLA. Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements. Towards determining the performance limits of DoA estimation from one-bit quantized data, we derive a pessimistic approximation of the corresponding Cram\'{e}r-Rao Bound (CRB). This pessimistic CRB is then used as a benchmark for assessing the performance of one-bit DoA estimators. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of the asymptotic distribution of the DoA estimation errors and show that it outperforms the existing algorithms in the literature. Numerical simulations are provided to validate the analytical derivations and corroborate the resulting performance improvement. [less ▲]

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See detailDoA Estimation Using Low-Resolution Multi-BitSparse Array Measurements
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE Signal Processing Letters (2021)

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit ... [more ▼]

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimization-based framework for retrieving the covaraince matrix of unquantized array data from low-resolution few-bit measurements. The MUSIC algorithm is then applied to an augmented version of the recovered covariance matrix to find the source DoAs. The simulation results show that increasing the sampling resolution to $2$ or $4$ bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling regime while the power consumption and implementation costs is still much lower in comparison to the high-resolution sampling implementations. [less ▲]

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See detailOn the Asymptotic Performance of One-Bit Co-Array-Based Music
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021)

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees ... [more ▼]

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees of freedom for DoAs that can be resolved. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has become an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. Although the problem of DoA estimation form one-bit SLA measurements has been studied in some prior works, its analytical performance has not yet been investigated and characterized. In this paper, to provide valuable insights into the performance of DoA estimation from one-bit SLA measurements, we derive an asymptotic closed-form expression for the performance of One-Bit Co-Array-Based MUSIC (OBCAB-MUSIC). Further, numerical simulations are provided to validate the asymptotic closed-form expression for the performance of OBCAB-MUSIC and to show an interesting use case of it in evaluating the resolution of OBCAB-MUSIC. [less ▲]

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See detailEfficient Radar Imaging Using Partially Synchronized Distributed Sensors
Murtada, Ahmed Abdelnaser Elsayed UL; Hu, Ruizhi UL; Alaeekerahroodi, Mohammad UL et al

in 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA May 2021 (2021)

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See detailLocalization Performance of 1-Bit Passive Radars in NB-IoT Applications using Multivariate Polynomial Optimization
Sedighi, Saeid UL; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Signal Processing (2021), 69

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the ... [more ▼]

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks in the framework of passive sensing devices that receive signals from the target-of-interest. We consider the limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method using constrained-weighted least squares minimization. To support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are then converted to one-bit data. At the FC, we propose target localization with the aggregated one-bit range vector using both optimal and sub-optimal techniques. The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint r\textit{an}ge-\textit{tar}get location \textit{es}timation (ANTARES) algorithm. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing. Numerical experiments demonstrate feasibility of the proposed one-bit approach with a 0.6\% increase in the normalized localization error for the small set of 20-60 nodes over the full-precision case. When the number of nodes is sufficiently large (>80), the one-bit methods yield the same performance as the full precision. [less ▲]

Detailed reference viewed: 87 (3 UL)