105 research outputs found
Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels
We study the information rates of non-coherent, stationary, Gaussian,
multiple-input multiple-output (MIMO) flat-fading channels that are achievable
with nearest neighbour decoding and pilot-aided channel estimation. In
particular, we analyse the behaviour of these achievable rates in the limit as
the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest
neighbour decoding and pilot-aided channel estimation achieves the capacity
pre-log - which is defined as the limiting ratio of the capacity to the
logarithm of SNR as the SNR tends to infinity - of non-coherent multiple-input
single-output (MISO) flat-fading channels, and it achieves the best so far
known lower bound on the capacity pre-log of non-coherent MIMO flat-fading
channels.Comment: 5 pages, 1 figure. To be presented at the IEEE International
Symposium on Information Theory (ISIT), St. Petersburg, Russia, 2011.
Replaced with version that will appear in the proceeding
Large-System Analysis of Multiuser Detection with an Unknown Number of Users: A High-SNR Approach
We analyze multiuser detection under the assumption that the number of users
accessing the channel is unknown by the receiver. In this environment, users'
activity must be estimated along with any other parameters such as data, power,
and location. Our main goal is to determine the performance loss caused by the
need for estimating the identities of active users, which are not known a
priori. To prevent a loss of optimality, we assume that identities and data are
estimated jointly, rather than in two separate steps. We examine the
performance of multiuser detectors when the number of potential users is large.
Statistical-physics methodologies are used to determine the macroscopic
performance of the detector in terms of its multiuser efficiency. Special
attention is paid to the fixed-point equation whose solution yields the
multiuser efficiency of the optimal (maximum a posteriori) detector in the
large signal-to-noise ratio regime. Our analysis yields closed-form approximate
bounds to the minimum mean-squared error in this regime. These illustrate the
set of solutions of the fixed-point equation, and their relationship with the
maximum system load. Next, we study the maximum load that the detector can
support for a given quality of service (specified by error probability).Comment: to appear in IEEE Transactions on Information Theor
Mismatched Binary Hypothesis Testing: Error Exponent Sensitivity
We study the problem of mismatched binary hypothesis testing between i.i.d.
distributions. We analyze the tradeoff between the pairwise error probability
exponents when the actual distributions generating the observation are
different from the distributions used in the likelihood ratio test, sequential
probability ratio test, and Hoeffding's generalized likelihood ratio test in
the composite setting. When the real distributions are within a small
divergence ball of the test distributions, we find the deviation of the
worst-case error exponent of each test with respect to the matched error
exponent. In addition, we consider the case where an adversary tampers with the
observation, again within a divergence ball of the observation type. We show
that the tests are more sensitive to distribution mismatch than to adversarial
observation tampering.Comment: arXiv admin note: text overlap with arXiv:2001.0391
Irregular Turbo Codes in Block-Fading Channels
We study irregular binary turbo codes over non-ergodic block-fading channels.
We first propose an extension of channel multiplexers initially designed for
regular turbo codes. We then show that, using these multiplexers, irregular
turbo codes that exhibit a small decoding threshold over the ergodic
Gaussian-noise channel perform very close to the outage probability on
block-fading channels, from both density evolution and finite-length
perspectives.Comment: to be presented at the IEEE International Symposium on Information
Theory, 201
A Sphere-Packing Error Exponent for Mismatched Decoding
We derive a sphere-packing error exponent for coded transmission over
discrete memoryless channels with a fixed decoding metric. By studying the
error probability of the code over an auxiliary channel, we find a lower bound
to the probability of error of mismatched decoding. The bound is shown to decay
exponentially for coding rates smaller than a new upper bound to the mismatch
capacity. For rates higher than the new upper bound, the error probability is
shown to be bounded away from zero. The new upper bound is shown to improve
over previous upper bounds to the mismatch capacity
Generalized Random Gilbert-Varshamov Codes: Typical Error Exponent and Concentration Properties
We find the exact typical error exponent of constant composition generalized
random Gilbert-Varshamov (RGV) codes over DMCs channels with generalized
likelihood decoding. We show that the typical error exponent of the RGV
ensemble is equal to the expurgated error exponent, provided that the RGV
codebook parameters are chosen appropriately. We also prove that the random
coding exponent converges in probability to the typical error exponent, and the
corresponding non-asymptotic concentration rates are derived. Our results show
that the decay rate of the lower tail is exponential while that of the upper
tail is double exponential above the expurgated error exponent. The explicit
dependence of the decay rates on the RGV distance functions is characterized.Comment: 60 pages, 2 figure
The Saddlepoint Approximation: Unified Random Coding Asymptotics for Fixed and Varying Rates
This paper presents a saddlepoint approximation of the random-coding union
bound of Polyanskiy et al. for i.i.d. random coding over discrete memoryless
channels. The approximation is single-letter, and can thus be computed
efficiently. Moreover, it is shown to be asymptotically tight for both fixed
and varying rates, unifying existing achievability results in the regimes of
error exponents, second-order coding rates, and moderate deviations. For fixed
rates, novel exact-asymptotics expressions are specified to within a
multiplicative 1+o(1) term. A numerical example is provided for which the
approximation is remarkably accurate even at short block lengths.Comment: Accepted to ISIT 2014, presented without publication at ITA 201
The Error Probability of Generalized Perfect Codes
This paper has been presented at : IEEE International Symposium on Information Theory 2018We introduce a definition of perfect and quasi-perfect codes for symmetric channels parametrized by an auxiliary output distribution. This new definition generalizes previous definitions and encompasses maximum distance separable codes. The error probability of these codes, whenever they exist, is shown to attain the meta-converse lower bound.This work has been funded in part by the European Research Council (ERC) under grants 714161 and 725411, by the Spanish Ministry of Economy and Competitiveness under Grants TEC2016-78434-C3 and IJCI-2015-27020, by
the National Science Foundation under Grant CCF-1513915 and by the Center for Science of Information, an NSF Science and Technology Center under Grant CCF-0939370
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