7 research outputs found
Sampling of stochastic operators
We develop sampling methodology aimed at determining stochastic operators
that satisfy a support size restriction on the autocorrelation of the operators
stochastic spreading function. The data that we use to reconstruct the operator
(or, in some cases only the autocorrelation of the spreading function) is based
on the response of the unknown operator to a known, deterministic test signal
Noncoherent Capacity of Underspread Fading Channels
We derive bounds on the noncoherent capacity of wide-sense stationary
uncorrelated scattering (WSSUS) channels that are selective both in time and
frequency, and are underspread, i.e., the product of the channel's delay spread
and Doppler spread is small. For input signals that are peak constrained in
time and frequency, we obtain upper and lower bounds on capacity that are
explicit in the channel's scattering function, are accurate for a large range
of bandwidth and allow to coarsely identify the capacity-optimal bandwidth as a
function of the peak power and the channel's scattering function. We also
obtain a closed-form expression for the first-order Taylor series expansion of
capacity in the limit of large bandwidth, and show that our bounds are tight in
the wideband regime. For input signals that are peak constrained in time only
(and, hence, allowed to be peaky in frequency), we provide upper and lower
bounds on the infinite-bandwidth capacity and find cases when the bounds
coincide and the infinite-bandwidth capacity is characterized exactly. Our
lower bound is closely related to a result by Viterbi (1967).
The analysis in this paper is based on a discrete-time discrete-frequency
approximation of WSSUS time- and frequency-selective channels. This
discretization explicitly takes into account the underspread property, which is
satisfied by virtually all wireless communication channels.Comment: Submitted to the IEEE Transactions on Information Theor
Unified Capacity Limit of Non-coherent Wideband Fading Channels
In non-coherent wideband fading channels where energy rather than spectrum is
the limiting resource, peaky and non-peaky signaling schemes have long been
considered species apart, as the first approaches asymptotically the capacity
of a wideband AWGN channel with the same average SNR, whereas the second
reaches a peak rate at some finite critical bandwidth and then falls to zero as
bandwidth grows to infinity. In this paper it is shown that this distinction is
in fact an artifact of the limited attention paid in the past to the product
between the bandwidth and the fraction of time it is in use. This fundamental
quantity, called bandwidth occupancy, measures average bandwidth usage over
time. For all signaling schemes with the same bandwidth occupancy, achievable
rates approach to the wideband AWGN capacity within the same gap as the
bandwidth occupancy approaches its critical value, and decrease to zero as the
occupancy goes to infinity. This unified analysis produces quantitative
closed-form expressions for the ideal bandwidth occupancy, recovers the
existing capacity results for (non-)peaky signaling schemes, and unveils a
trade-off between the accuracy of approximating capacity with a generalized
Taylor polynomial and the accuracy with which the optimal bandwidth occupancy
can be bounded.Comment: Accepted for publication in IEEE Transactions on Wireless
Communications. Copyright may be transferred without notic
Sparsity-Aware Low-Power ADC Architecture with Advanced Reconstruction Algorithms
Compressive sensing (CS) technique enables a universal sub-Nyquist sampling of sparse and compressible signals, while still guaranteeing the reliable signal recovery. Its potential lies in the reduced analog-to-digital conversion rate in sampling broadband and/or multi-channel sparse signals, where conventional Nyquist-rate sampling are either technology impossible or extremely hardware costly.
Nevertheless, there are many challenges in the CS hardware design. In coherent sampling, state-of-the-art mixed-signal CS front-ends, such as random demodulator and modulated wideband converter, suffer from high power and nonlinear hardware. In signal recovery, state-of-the-art CS reconstruction methods have tractable computational complexity and probabilistically guaranteed performance. However, they are still high cost (basis pursuit) or noise sensitive (matching pursuit).
In this dissertation, we propose an asynchronous compressive sensing (ACS) front-end and advanced signal reconstruction algorithms to address these challenges. The ACS front-end consists of a continuous-time ternary encoding (CT-TE) scheme which converts signal amplitude variations into high-rate ternary timing signal, and a digital random sampler (DRS) which captures the ternary timing signal at sub-Nyquist rate. The CT-TE employs asynchronous sampling mechanism for pulsed-like input and has signal-dependent conversion rate. The DRS has low power, ease of massive integration, and excellent linearity in comparison to state-of-the-art mixed-signal CS front-ends.
We propose two reconstruction algorithms. One is group-based total variation, which exploits piecewise-constant characteristics and achieves better mean squared error and faster convergence rate than the conventional TV scheme with moderate noise. The second algorithm is split-projection least squares (SPLS), which relies on a series of low-complexity and independent l2-norm problems with the prior on ternary-valued signal. The SPLS scheme has good noise robustness, low-cost signal reconstruction and facilitates a parallel hardware for real-time signal recovery.
In application study, we propose multi-channel filter banks ACS front-end for the interference-robust radar. The proposed receiver performs reliable target detection with nearly 8-fold data compression than Nyquist-rate sampling in the presence of -50dBm wireless interference. We also propose an asynchronous compressed beamformer (ACB) for low-power portable diagnostic ultrasound. The proposed ACB achieves 9-fold data volume compression and only 4.4% contrast-to-noise ratio loss on the imaging results when compared with the Nyquist-rate ADCs
Middle Atmosphere Program. Handbook for MAP. Volume 30: International School on Atmospheric Radar
Broad, tutorial coverage is given to the technical and scientific aspects of mesosphere stratosphere troposphere (MST) meteorological radar systems. Control issues, signal processing, atmospheric waves, the historical aspects of radar atmospheric dynamics, incoherent scatter radars, radar echoes, radar targets, and gravity waves are among the topics covered