5,264 research outputs found
On Convexity of Error Rates in Digital Communications
Convexity properties of error rates of a class of decoders, including the maximum-likelihood/min-distance one as a special case, are studied for arbitrary constellations, bit mapping, and coding. Earlier results obtained for the additive white Gaussian noise channel are extended to a wide class of noise densities, including unimodal and spherically invariant noise. Under these broad conditions, symbol and bit error rates are shown to be convex functions of the signal-to-noise ratio (SNR) in the high-SNR regime with an explicitly determined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case. The known high/low-SNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. The high-SNR bound fits closely into the channel coding theorem: all codes, including capacity-achieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem), satisfy this high-SNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacity-achieving codes have convex error rates. Convexity properties in signal amplitude and noise power are also investigated. Some applications of the results are discussed. In particular, it is shown that fading is convexity-preserving and is never good in low dimensions under spherically invariant noise, which may also include any linear diversity combining
On Convexity of Error Rates in Digital Communications
Convexity properties of error rates of a class of decoders, including the maximum-likelihood/min-distance one as a special case, are studied for arbitrary constellations, bit mapping, and coding. Earlier results obtained for the additive white Gaussian noise channel are extended to a wide class of noise densities, including unimodal and spherically invariant noise. Under these broad conditions, symbol and bit error rates are shown to be convex functions of the signal-to-noise ratio (SNR) in the high-SNR regime with an explicitly determined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case. The known high/low-SNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. The high-SNR bound fits closely into the channel coding theorem: all codes, including capacity-achieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem), satisfy this high-SNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacity-achieving codes have convex error rates. Convexity properties in signal amplitude and noise power are also investigated. Some applications of the results are discussed. In particular, it is shown that fading is convexity-preserving and is never good in low dimensions under spherically invariant noise, which may also include any linear diversity combining
Error Rates of the Maximum-Likelihood Detector for Arbitrary Constellations: Convex/Concave Behavior and Applications
Motivated by a recent surge of interest in convex optimization techniques,
convexity/concavity properties of error rates of the maximum likelihood
detector operating in the AWGN channel are studied and extended to
frequency-flat slow-fading channels. Generic conditions are identified under
which the symbol error rate (SER) is convex/concave for arbitrary
multi-dimensional constellations. In particular, the SER is convex in SNR for
any one- and two-dimensional constellation, and also in higher dimensions at
high SNR. Pairwise error probability and bit error rate are shown to be convex
at high SNR, for arbitrary constellations and bit mapping. Universal bounds for
the SER 1st and 2nd derivatives are obtained, which hold for arbitrary
constellations and are tight for some of them. Applications of the results are
discussed, which include optimum power allocation in spatial multiplexing
systems, optimum power/time sharing to decrease or increase (jamming problem)
error rate, an implication for fading channels ("fading is never good in low
dimensions") and optimization of a unitary-precoded OFDM system. For example,
the error rate bounds of a unitary-precoded OFDM system with QPSK modulation,
which reveal the best and worst precoding, are extended to arbitrary
constellations, which may also include coding. The reported results also apply
to the interference channel under Gaussian approximation, to the bit error rate
when it can be expressed or approximated as a non-negative linear combination
of individual symbol error rates, and to coded systems.Comment: accepted by IEEE IT Transaction
Symbol Error Rates of Maximum-Likelihood Detector: Convex/Concave Behavior and Applications
Convexity/concavity properties of symbol error rates (SER) of the maximum
likelihood detector operating in the AWGN channel (non-fading and fading) are
studied. Generic conditions are identified under which the SER is a
convex/concave function of the SNR. Universal bounds for the SER 1st and 2nd
derivatives are obtained, which hold for arbitrary constellations and are tight
for some of them. Applications of the results are discussed, which include
optimum power allocation in spatial multiplexing systems, optimum power/time
sharing to decrease or increase (jamming problem) error rate, and implication
for fading channels.Comment: To appear in 2007 IEEE International Symposium on Information Theory
(ISIT 2007), Nice, June 200
Spectrum optimization in multi-user multi-carrier systems with iterative convex and nonconvex approximation methods
Several practical multi-user multi-carrier communication systems are
characterized by a multi-carrier interference channel system model where the
interference is treated as noise. For these systems, spectrum optimization is a
promising means to mitigate interference. This however corresponds to a
challenging nonconvex optimization problem. Existing iterative convex
approximation (ICA) methods consist in solving a series of improving convex
approximations and are typically implemented in a per-user iterative approach.
However they do not take this typical iterative implementation into account in
their design. This paper proposes a novel class of iterative approximation
methods that focuses explicitly on the per-user iterative implementation, which
allows to relax the problem significantly, dropping joint convexity and even
convexity requirements for the approximations. A systematic design framework is
proposed to construct instances of this novel class, where several new
iterative approximation methods are developed with improved per-user convex and
nonconvex approximations that are both tighter and simpler to solve (in
closed-form). As a result, these novel methods display a much faster
convergence speed and require a significantly lower computational cost.
Furthermore, a majority of the proposed methods can tackle the issue of getting
stuck in bad locally optimal solutions, and hence improve solution quality
compared to existing ICA methods.Comment: 33 pages, 7 figures. This work has been submitted for possible
publicatio
Discrete multitone modulation with principal component filter banks
Discrete multitone (DMT) modulation is an attractive method for communication over a nonflat channel with possibly colored noise. The uniform discrete Fourier transform (DFT) filter bank and cosine modulated filter bank have in the past been used in this system because of low complexity. We show in this paper that principal component filter banks (PCFB) which are known to be optimal for data compression and denoising applications, are also optimal for a number of criteria in DMT modulation communication. For example, the PCFB of the effective channel noise power spectrum (noise psd weighted by the inverse of the channel gain) is optimal for DMT modulation in the sense of maximizing bit rate for fixed power and error probabilities. We also establish an optimality property of the PCFB when scalar prefilters and postfilters are used around the channel. The difference between the PCFB and a traditional filter bank such as the brickwall filter bank or DFT filter bank is significant for effective power spectra which depart considerably from monotonicity. The twisted pair channel with its bridged taps, next and fext noises, and AM interference, therefore appears to be a good candidate for the application of a PCFB. This is demonstrated with the help of numerical results for the case of the ADSL channel
Robustness maximization of parallel multichannel systems
Bit error rate (BER) minimization and SNR-gap maximization, two robustness
optimization problems, are solved, under average power and bit-rate
constraints, according to the waterfilling policy. Under peak-power constraint
the solutions differ and this paper gives bit-loading solutions of both
robustness optimization problems over independent parallel channels. The study
is based on analytical approach with generalized Lagrangian relaxation tool and
on greedy-type algorithm approach. Tight BER expressions are used for square
and rectangular quadrature amplitude modulations. Integer bit solution of
analytical continuous bit-rates is performed with a new generalized secant
method. The asymptotic convergence of both robustness optimizations is proved
for both analytical and algorithmic approaches. We also prove that, in
conventional margin maximization problem, the equivalence between SNR-gap
maximization and power minimization does not hold with peak-power limitation.
Based on a defined dissimilarity measure, bit-loading solutions are compared
over power line communication channel for multicarrier systems. Simulation
results confirm the asymptotic convergence of both allocation policies. In non
asymptotic regime the allocation policies can be interchanged depending on the
robustness measure and the operating point of the communication system. The low
computational effort of the suboptimal solution based on analytical approach
leads to a good trade-off between performance and complexity.Comment: 27 pages, 8 figures, submitted to IEEE Trans. Inform. Theor
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