BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization

@article{Xi2021BiLiMOBM,
  title={BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization},
  author={Feng Xi and Nir Shlezinger and Yonina C. Eldar},
  journal={IEEE Transactions on Signal Processing},
  year={2021},
  volume={69},
  pages={6267-6282}
}
Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high performance sensing by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the… 
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References

SHOWING 1-10 OF 56 REFERENCES
Task-Based Quantization with Application to MIMO Receivers
TLDR
This work surveys the theory and design approaches to task-based quantization, presenting model-aware designs as well as data-driven implementations, and shows how one can implement a task- based bit-constrained MIMO receiver.
SUMMeR: Sub-Nyquist MIMO Radar
TLDR
This paper presents a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets’ parameters, without degrading the time and spatial resolutions.
Asymptotic Task-Based Quantization With Application to Massive MIMO
TLDR
This paper focuses on the task of recovering a desired signal statistically related to the high-dimensional input, and analyzes two quantization approaches, and considers vector quantization, which is typically computationally infeasible, and the optimal performance achievable with this approach.
Deep Task-Based Quantization †
TLDR
The results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model.
Joint Angle and Doppler Frequency Estimation for MIMO Radar with One-Bit Sampling: A Maximum Likelihood-Based Method
TLDR
A maximum likelihood (ML)-based method that first iteratively maximizes the likelihood function to recover a virtual array data matrix and then jointly estimates the angle and Doppler parameters from the recovered matrix is proposed.
One-Bit Quantization Design and Channel Estimation for Massive MIMO Systems
TLDR
Simulation results show that the proposed adaptive and random quantization schemes presents a significant performance improvement over the conventional fixed quantization scheme that uses a fixed (typically zero) threshold.
Dynamic Metasurface Antennas for MIMO-OFDM Receivers With Bit-Limited ADCs
TLDR
This work presents a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs).
Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds
TLDR
Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.
Sub-Nyquist radar prototype: Hardware and algorithm
TLDR
This work presents for the first time a design and implementation of an Xampling-based hardware prototype that allows sampling of radar signals at rates much lower than Nyquist, and demonstrates by real-time analog experiments that the system is able to maintain reasonable recovery capabilities, while sampling radar signals that require sampling at a rate of about 30 MHz at a total rate of 1 MHz.
Learning Task-Based Analog-to-Digital Conversion for MIMO Receivers
TLDR
This work designs task-oriented analog-to-digital converters (ADCs) which operate in a data-driven manner, namely they learn how to map an analog signal into a sampled digital representation such that the system task can be efficiently carried out.
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