1,694 research outputs found
Identification of Parametric Underspread Linear Systems and Super-Resolution Radar
Identification of time-varying linear systems, which introduce both
time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task
in many engineering applications. This paper studies the problem of
identification of underspread linear systems (ULSs), whose responses lie within
a unit-area region in the delay Doppler space, by probing them with a known
input signal. It is shown that sufficiently-underspread parametric linear
systems, described by a finite set of delays and Doppler-shifts, are
identifiable from a single observation as long as the time bandwidth product of
the input signal is proportional to the square of the total number of delay
Doppler pairs in the system. In addition, an algorithm is developed that
enables identification of parametric ULSs from an input train of pulses in
polynomial time by exploiting recent results on sub-Nyquist sampling for time
delay estimation and classical results on recovery of frequencies from a sum of
complex exponentials. Finally, application of these results to super-resolution
target detection using radar is discussed. Specifically, it is shown that the
proposed procedure allows to distinguish between multiple targets with very
close proximity in the delay Doppler space, resulting in a resolution that
substantially exceeds that of standard matched-filtering based techniques
without introducing leakage effects inherent in recently proposed compressed
sensing-based radar methods.Comment: Revised version of a journal paper submitted to IEEE Trans. Signal
Processing: 30 pages, 17 figure
Compressed Sensing Based Multi-User Millimeter Wave Systems: How Many Measurements Are Needed?
Millimeter wave (mmWave) systems will likely employ directional beamforming
with large antenna arrays at both the transmitters and receivers. Acquiring
channel knowledge to design these beamformers, however, is challenging due to
the large antenna arrays and small signal-to-noise ratio before beamforming. In
this paper, we propose and evaluate a downlink system operation for multi-user
mmWave systems based on compressed sensing channel estimation and conjugate
analog beamforming. Adopting the achievable sum-rate as a performance metric,
we show how many compressed sensing measurements are needed to approach the
perfect channel knowledge performance. The results illustrate that the proposed
algorithm requires an order of magnitude less training overhead compared with
traditional lower-frequency solutions, while employing mmWave-suitable
hardware. They also show that the number of measurements need to be optimized
to handle the trade-off between the channel estimate quality and the training
overhead.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 201
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
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