References of "Mishra, Kumar Vijay"
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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 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)

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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 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 ▲]

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See detailInformation Theoretic Approach for Waveform Design in Coexisting MIMO Radar and MIMO Communications
Alaeekerahroodi, Mohammad UL; Mysore Rama Rao, Bhavani Shankar UL; Mishra, Kumar Vijay et al

in Information Theoretic Approach for Waveform Design in Coexisting MIMO Radar and MIMO Communications (2020, May 14)

We investigate waveform design for coexistence between a multipleinput multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in ... [more ▼]

We investigate waveform design for coexistence between a multipleinput multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is nonconvex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary. [less ▲]

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See detailLocalization Performance of 1-Bit Passive Radars in NB-IoT Applications
Sedighi, Saeid UL; Mishra, Kumar Vijay; Shankar, Bhavani UL et al

in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2019, December 14)

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the ... [more ▼]

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the network with additional active sensors. The massive number of devices, low power requirement, and low bandwidths restrict the sampling rates of NB-IoT receivers. In this paper, we propose a novel low-complexity approach for NB-IoT target delay estimation in cases where one-bit analog-to-digital-converters (ADCs) are employed to sample the received radar signal instead of high-resolution ADCs. This problem has potential applications in the design of inexpensive NB-IoT radar and sensing devices. We formulate the target estimation as a multivariate fractional optimization problem and solve it via Lasserre's semi-definite program relaxation. Numerical experiments suggest feasibility of the proposed approach yielding high localization accuracy with a very low number of 1-bit samples. [less ▲]

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See detailOptimum Design for Sparse FDA-MIMO Automotive Radar
Sedighi, Saeid UL; Shankar, Bhavani UL; Mishra, Kumar Vijay et al

in Asilomar Conference on Signals, Systems, and Computers (2019, November 03)

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs ... [more ▼]

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs and desired radar size limits the usage of more antennas in the array. Similar trade-off is encountered while attempting to achieve high range resolution which is limited by the signal bandwidth. However, nowadays given the demand for spectrum from communications services, wide bandwidth is not readily available. To address these issues, we propose a sparse variant of Frequency Diverse Array MIMO (FDA-MIMO) radar which enjoys the benefits of both FDA and MIMO techniques, including fewer elements, decoupling, and efficient joint estimation of target parameters. We then employ the Cram\'{e}r-Rao bound for angle and range estimation as a performance metric to design the optimal antenna placement and carrier frequency offsets for the transmit waveforms. Numerical experiments suggest that the performance of sparse FDA-MIMO radar is very close to the conventional FDA-MIMO despite 50\% reduction in the bandwidth and antenna elements. [less ▲]

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See detailToward Millimeter-Wave Joint Radar Communications: A Signal Processing Perspective
Mishra, Kumar Vijay; Shankar, Bhavani UL; Koivunen, Visa et al

in IEEE Signal Processing Magazine (2019), 36(5), 100-114

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint ... [more ▼]

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design. [less ▲]

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See detailMeeting the Lower Bound on Designing Set of Unimodular Sequences with Small Aperiodic/Periodic ISL
Alaee-Kerahroodi, Mohammad UL; Shankar, Bhavani; Mishra, Kumar Vijay et al

in 2019 20th International Radar Symposium (IRS) (2019)

Detailed reference viewed: 96 (2 UL)
See detailDiscrete-Phase Sequence Design for Coexistence of MIMO Radar and MIMO Communications
Alaee-Kerahroodi, Mohammad UL; Mishra, Kumar Vijay; Shankar, M. R. Bhavani et al

in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2019)

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See detailLearning-based rainfall estimation via communication satellite links
Gharanjik, Ahmad UL; Mishra, Kumar Vijay; Shankar, Bhavani UL et al

in 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018)

We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and ... [more ▼]

We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and exploits the nearly linear relation between the rain rate and the specific attenuation at Ka-band frequencies. Although our experimental setup is not intended to achieve high resolutions as millimeter wavelength weather radars, it is instructive because of easy availability of millions of satellite ground terminals throughout the world. The received signal is obtained over a passive link. Therefore, traditional weather radar signal processing to derive parameters for rainfall estimation algorithms is not feasible here. We overcome this disadvantage by employing neural network learning algorithms to extract relevant information. Initial results reveal that rainfall accumulations obtained through our method are 85% closer to the in situ rain gauge estimates than the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar. [less ▲]

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