2,752 research outputs found
Asynchronous CDMA Systems with Random Spreading-Part I: Fundamental Limits
Spectral efficiency for asynchronous code division multiple access (CDMA)
with random spreading is calculated in the large system limit allowing for
arbitrary chip waveforms and frequency-flat fading. Signal to interference and
noise ratios (SINRs) for suboptimal receivers, such as the linear minimum mean
square error (MMSE) detectors, are derived. The approach is general and
optionally allows even for statistics obtained by under-sampling the received
signal.
All performance measures are given as a function of the chip waveform and the
delay distribution of the users in the large system limit. It turns out that
synchronizing users on a chip level impairs performance for all chip waveforms
with bandwidth greater than the Nyquist bandwidth, e.g., positive roll-off
factors. For example, with the pulse shaping demanded in the UMTS standard,
user synchronization reduces spectral efficiency up to 12% at 10 dB normalized
signal-to-noise ratio. The benefits of asynchronism stem from the finding that
the excess bandwidth of chip waveforms actually spans additional dimensions in
signal space, if the users are de-synchronized on the chip-level. The analysis
of linear MMSE detectors shows that the limiting interference effects can be
decoupled both in the user domain and in the frequency domain such that the
concept of the effective interference spectral density arises. This generalizes
and refines Tse and Hanly's concept of effective interference.
In Part II, the analysis is extended to any linear detector that admits a
representation as multistage detector and guidelines for the design of low
complexity multistage detectors with universal weights are provided
On optimum parameter modulation-estimation from a large deviations perspective
We consider the problem of jointly optimum modulation and estimation of a
real-valued random parameter, conveyed over an additive white Gaussian noise
(AWGN) channel, where the performance metric is the large deviations behavior
of the estimator, namely, the exponential decay rate (as a function of the
observation time) of the probability that the estimation error would exceed a
certain threshold. Our basic result is in providing an exact characterization
of the fastest achievable exponential decay rate, among all possible
modulator-estimator (transmitter-receiver) pairs, where the modulator is
limited only in the signal power, but not in bandwidth. This exponential rate
turns out to be given by the reliability function of the AWGN channel. We also
discuss several ways to achieve this optimum performance, and one of them is
based on quantization of the parameter, followed by optimum channel coding and
modulation, which gives rise to a separation-based transmitter, if one views
this setting from the perspective of joint source-channel coding. This is in
spite of the fact that, in general, when error exponents are considered, the
source-channel separation theorem does not hold true. We also discuss several
observations, modifications and extensions of this result in several
directions, including other channels, and the case of multidimensional
parameter vectors. One of our findings concerning the latter, is that there is
an abrupt threshold effect in the dimensionality of the parameter vector: below
a certain critical dimension, the probability of excess estimation error may
still decay exponentially, but beyond this value, it must converge to unity.Comment: 26 pages; Submitted to the IEEE Transactions on Information Theor
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Processing and Transmission of Information
Contains research objectives and reports on seven research projects.Lincoln Laboratory, Purchase Order DDL-B222Air Force under Air Force Contract AF19(604)-5200Office of Naval Research under Contract Nonr-1841(57
Quantum-mechanical communication theory
Optimum signal reception using quantum-mechanical communication theor
Deep Neural Networks for Energy and Position Reconstruction in EXO-200
We apply deep neural networks (DNN) to data from the EXO-200 experiment. In
the studied cases, the DNN is able to reconstruct the relevant parameters -
total energy and position - directly from raw digitized waveforms, with minimal
exceptions. For the first time, the developed algorithms are evaluated on real
detector calibration data. The accuracy of reconstruction either reaches or
exceeds what was achieved by the conventional approaches developed by EXO-200
over the course of the experiment. Most existing DNN approaches to event
reconstruction and classification in particle physics are trained on Monte
Carlo simulated events. Such algorithms are inherently limited by the accuracy
of the simulation. We describe a unique approach that, in an experiment such as
EXO-200, allows to successfully perform certain reconstruction and analysis
tasks by training the network on waveforms from experimental data, either
reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure
Detection of variable frequency signals using a fast chirp transform
The detection of signals with varying frequency is important in many areas of
physics and astrophysics. The current work was motivated by a desire to detect
gravitational waves from the binary inspiral of neutron stars and black holes,
a topic of significant interest for the new generation of interferometric
gravitational wave detectors such as LIGO. However, this work has significant
generality beyond gravitational wave signal detection.
We define a Fast Chirp Transform (FCT) analogous to the Fast Fourier
Transform (FFT). Use of the FCT provides a simple and powerful formalism for
detection of signals with variable frequency just as Fourier transform
techniques provide a formalism for the detection of signals of constant
frequency. In particular, use of the FCT can alleviate the requirement of
generating complicated families of filter functions typically required in the
conventional matched filtering process. We briefly discuss the application of
the FCT to several signal detection problems of current interest
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