The optimal quantization of output binary-input discrete memoryless channels is considered, whereby the optimal quantizer preserves at least a constant$\alpha $-fraction of the original mutual information, with the smallest output cardinality. Two recursive methods with top-down and bottom-up approaches are developed; these methods lead to a new necessary condition for the recursive quantizer design. An efficient algorithm with linear complexity, based on dynamic programming and the new necessary optimality condition, is proposed.

This paper proposes an efficient simulation method based on importance sampling to estimate the random-coding error probability of coded modulation. The technique is valid for complex-valued modulations over Gaussian channels, channels with memory, and naturally extends to fading channels. The simulation method is built on two nested importance samplers to respectively estimate the pairwise error probability and generate the channel input and output. The effect of the respective number of samples on the overall bias and variance of the estimate of the error probability is characterized. For a memoryless channel, the estimator is shown to be consistent and with a small variance, growing with the square root of the code length, rather than the exponential growth of a standard Monte Carlo estimator.

Saddlepoint approximations to the error probability are derived for multiple-cost-constrained random coding ensembles where codewords satisfy a set of constraints. Constant-composition inputs over a binary symmetric channel are studied as a particular case. For codewords with equiprobable empirical distribution, the analysis recovers the same error exponent and pre-exponential polynomial decay as the uniform i.i.d. ensemble and provides an explicit formula for the loss in prefactor (third-order term) incurred by the constant-composition ensemble.

This paper studies the random-coding exponent of joint source-channel coding for the multiple-access channel with correlated sources. For each user, by defining a threshold, the messages of each source are partitioned into two classes. The achievable exponent for correlated sources with two message-dependent input distributions for each user is determined and shown to be larger than that achieved using only one input distribution for each user. A system of equations is presented to determine the optimal thresholds maximizing the achievable exponent. The obtained exponent is compared with the one derived for the MAC with independent sources.

Channel output quantization to a smaller number of outputs is modeled as a mismatched decoding problem. The conditions that a mismatched decoding metric should satisfy in order to represent an output quantizer are derived. In addition, a mismatched decoding metric and hypothesis test that minimizes the average error probability are found. It is shown that the best possible mismatched decoder is equivalent to maximum-likelihood decoding for the channel between the channel input and the quantized output. This gives a class of mismatched decoding problems where the mismatch capacity is known. This result supports previous studies on quantizer design and optimization over the quantized channel.

Saddlepoint approximations and expansions of the random coding union bound are derived for the i.i.d. random coding ensemble. Using the inverse Laplace transform of lattice and strongly non-lattice distributions, our results recover the random coding error exponent and refine the pre-exponential coefficient of the error probability. Explicit characterization of the terms are given for the binary symmetric channel and for the binary input AWGN channel.

Saddlepoint approximations to the pairwise error probability and to the random coding union bound are derived for the cost-constrained random coding ensemble. For the special case of the AWGN channel, an alternative expression to approximate the Shannon bound for optimal spherical codes is found.

In this paper, an achievable error exponent for the multiple-access channel with two independent sources is derived. For each user, the source messages are partitioned into two classes and codebooks are generated by drawing codewords from an input distribution depending on the class index of the source message. The partitioning thresholds that maximize the achievable exponent are given by the solution of a system of equations. We also derive both lower and upper bounds for the achievable exponent in terms of Gallager's source and channel functions. Finally, a numerical example shows that using the proposed ensemble gives a noticeable gain in terms of exponent with respect to independent identically distributed codebooks.

This paper studies channel coding for the discrete memoryless multiple-access channel with a given (possibly suboptimal) decoding rule. A multi-letter successive decoding rule depending on an arbitrary non-negative decoding metric is considered, and achievable rate regions and error exponents are derived both for the standard MAC (independent codebooks), and for the cognitive MAC (one user knows both messages) with superposition coding. In the cognitive case, the rate region and error exponent are shown to be tight with respect to the ensemble average. The rate regions are compared with those of the commonly considered decoder that chooses the message pair maximizing the decoding metric, and numerical examples are given for which successive decoding yields a strictly higher sum rate for a given pair of input distributions.

This paper provides an asymptotic expansion of the error probability, as the codeword length n goes to infinity, in quasi-static binary symmetric channels. After the leading term, namely the outage probability, the next two terms are found to be proportional to and respectively. Explicit characterizations of the respective coefficients are given. The resulting expansion gives an approximation to the random-coding union bound, accurate even at small codeword lengths.

This paper derives an achievable random-coding error exponent for joint source-channel coding over a multiple access channel with correlated sources. The codebooks are generated by drawing codewords from a multi-letter distribution that depends on the composition of the source message.

This paper studies expurgated random-coding bounds and error exponents for joint source-channel coding (JSCC). We extend Gallager's expurgation techniques for channel coding to the JSCC setting, and derive a non-asymptotic bound that recovers two exponents derived by Csiszár using the method of types. Our approach has the notable advantage of being directly applicable to channels with continuous alphabets.

This paper studies the random-coding union (RCU) bound to the error probability in quasi-static fading channels. An asymptotic expansion and a normal approximation to the RCU bound suggest that the error probability converges to the outage probability as 1/n, where n is the codeword blocklength. We particularize our results for Rayleigh fading, and compare them with the conventional normal approximation.

This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with a channel code that depends on the class index. The code performance is analyzed by means of random-coding error exponents and validated by simulation of a low-complexity implementation using existing source and channel codes. While each class code can be seen as a concatenation of a source code and a channel code, the overall performance improves on that of separate source-channel coding and approaches that of joint source-channel coding when the number of classes increases.

This paper studies multiuser random coding techniques for channel coding with a given (possibly suboptimal) decoding rule. For the mismatched discrete memoryless multiple-access channel, an error exponent is obtained that is tight with respect to the ensemble average, and positive within the interior of Lapidoth's achievable rate region. This exponent proves the ensemble tightness of the exponent of Liu and Hughes in the case of maximum-likelihood decoding. An equivalent dual form of Lapidoth's achievable rate region is given, and the latter is shown to immediately extend to channels with infinite and continuous alphabets. In the setting of single-user mismatched decoding, similar analysis techniques are applied to a refined version of superposition coding, which is shown to achieve rates at least as high as standard superposition coding for any set of random-coding parameters.

Two alternative exact characterizations of the minimum error probability of Bayesian M-ary hypothesis testing are derived. The first expression corresponds to the error probability of an induced binary hypothesis test and implies the tightness of the meta-converse bound by Polyanskiy et al.; the second expression is a function of an information-spectrum measure and implies the tightness of a generalized Verdú-Han lower bound. The formulas characterize the minimum error probability of several problems in information theory and help to identify the steps where existing converse bounds are loose.

This paper shows that bit-interleaved coded modulation (BICM) over the Gaussian channel can achieve information rates larger than the so-called BICM capacity. For some labelings the improvement with respect to the BICM capacity is significant, especially at low and medium signal-to-noise ratios (SNR). Specifically, natural binary labeling is found to be both first- and second-order optimal at low SNR.

This paper studies likelihood decoding for channel coding over discrete memoryless channels. It is shown that the likelihood decoder recovers the same random-coding error exponents as the maximum-likelihood decoder for i.i.d. and constant-composition random codes. The role of mismatch in likelihood decoding is studied, and the notion of the mismatched likelihood decoder capacity is introduced. It is shown, both in the case of random coding and optimized codebooks, that the mismatched likelihood decoder can lead to strictly worse achievable rates and error exponents compared to the corresponding mismatched maximum-metric decoder.

This paper studies the fixed-error asymptotics of constant-composition codes for discrete memoryless channels. An achievable asymptotic expansion is derived with a third-order term that can be as high as 1/2 log n, while being lower when (i) a certain feasibility-decoding condition fails, or (ii) the channel is a sum channel. Converse bounds are used to provide conditions under which each of these losses is unavoidable.

We derive the channel-coding sphere-packing exponent under a per-codeword cost constraint. The proof is based on hypothesis testing and holds for continuous memoryless channels.

The asynchronous-communication model is studied by means of i.i.d. codes and ML decoding. A random-coding bound to the joint probability of decoding and synchronization error is determined and used to recover the region of achievable information rates and asynchrony exponents.

This paper studies the second-order asymptotics of coding rates for the discrete memoryless multiple-access channel (MAC) with a fixed target error probability. Using constant-composition random coding, coded time-sharing, and a variant of Hoeffding's combinatorial central limit theorem, an inner bound on the set of locally achievable second-order coding rates is given for each point on the boundary of the capacity region. It is shown that the inner bound for constant-composition random coding includes that recovered by independent identically distributed random coding, and that the inclusion may be strict. The inner bound is extended to the Gaussian MAC via an increasingly fine quantization of the inputs.

This paper studies the mismatched decoding problem for binary-input discrete memoryless channels. An example is provided for which an achievable rate based on superposition coding exceeds the Csiszár-Körner-Hui rate, thus providing a counter-example to a previously reported converse result. Both numerical evaluations and theoretical results are used in establishing this claim.

We study a source-channel coding scheme in which source messages are assigned to classes and encoded using a channel code that depends on the class index. While each class code can be seen as a concatenation of a source code and a channel code, the overall performance improves on that of separate source-channel coding and approaches that of joint source-channel coding as the number of classes increases. The performance of this scheme is studied by means of random-coding bounds and validated by simulation of a low-complexity implementation using existing source and channel codes.

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.

This paper studies channel coding for the discrete memoryless multiple-access channel with a given (possibly suboptimal) decoding rule. A multi-letter successive decoding rule depending on an arbitrary non-negative decoding metric is considered, and achievable rate regions and error exponents are derived both for the standard MAC (independent codebooks), and for the cognitive MAC (one user knows both messages) with superposition coding. In the cognitive case, the rate region and error exponent are shown to be tight with respect to the ensemble average. The rate regions are compared with those of the commonly considered decoder that chooses the message pair maximizing the decoding metric, and numerical examples are given for which successive decoding yields a strictly higher sum rate for a given pair of input distributions.

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.

This paper considers the problem of channel coding with a given (possibly suboptimal) maximum-metric decoding rule. A cost-constrained random-coding ensemble with multiple auxiliary costs is introduced, and is shown to achieve error exponents and second-order coding rates matching those of constant-composition random coding, while being directly applicable to channels with infinite or continuous alphabets. The number of auxiliary costs required to match the error exponents and second-order rates of constant-composition coding is studied, and is shown to be at most two. For independent identically distributed random coding, asymptotic estimates of two well-known non-asymptotic bounds are given using saddlepoint approximations. Each expression is shown to characterize the asymptotic behavior of the corresponding random-coding bound at both fixed and varying rates, thus unifying the regimes characterized by error exponents, second-order rates, and moderate deviations. For fixed rates, novel exact asymptotics expressions are obtained to within a multiplicative 1+o(1) term. Using numerical examples, it is shown that the saddlepoint approximations are highly accurate even at short block lengths.

This paper studies expurgated random-coding bounds and exponents for channel coding with a given (possibly suboptimal) decoding rule. Variations of Gallager's analysis are presented, yielding several asymptotic and nonasymptotic bounds on the error probability for an arbitrary codeword distribution. A simple nonasymptotic bound is shown to attain an exponent of Csiszár and Körner under constant-composition coding. Using Lagrange duality, this exponent is expressed in several forms, one of which is shown to permit a direct derivation via cost-constrained coding that extends to infinite and continuous alphabets. The method of type class enumeration is studied, and it is shown that this approach can yield improved exponents and better tightness guarantees for some codeword distributions. A generalization of this approach is shown to provide a multiletter exponent that extends immediately to channels with memory.

Mismatched decoding theory is applied to study the error exponents (both random-coding and expurgated) and achievable rates for bit-interleaved coded modulation (BICM). The gains achieved by constant-composition codes with respect to the the usual random codes are highlighted.

An achievable rate is given for discrete memoryless channels with a given (possibly suboptimal) decoding rule. The result is obtained using a refinement of the superposition coding ensemble. The rate is tight with respect to the ensemble average, and can be weakened to the LM rate of Hui and Csiszár-Körner, and to Lapidoth's rate based on parallel codebooks.

This paper considers channel coding for the memoryless multiple-access channel with a given (possibly suboptimal) decoding rule. Non-asymptotic bounds on the error probability are given, and a cost-constrained random-coding ensemble is used to obtain an achievable error exponent. The achievable rate region recovered by the error exponent coincides with that of Lapidoth in the discrete memoryless case, and remains valid for more general alphabets.

We show that the meta-converse bound derived by Polyanskiy et al. provides the exact error probability for a fixed joint source-channel code and an appropriate choice of the bound parameters. While the expression is not computable in general, it identifies the weaknesses of known converse bounds to the minimum achievable error probability.

This paper determines the range of feasible values of standard error exponents for binary-input memoryless symmetric channels of fixed capacity$C$and shows that extremes are attained by the binary symmetric and the binary erasure channel. The proof technique also provides analogous extremes for other quantities related to Gallager's$E_{0}$function, such as the cutoff rate, the Bhattacharyya parameter, and the channel dispersion.

We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset.

We determine the loss in capacity incurred by using signal constellations with a bounded support over general complex-valued additive-noise channels for suitably high signal-to-noise ratio. Our expression for the capacity loss recovers the power loss of 1.53dB for square signal constellations.

We consider bit-interleaved coded modulation (BICM) schemes where, instead of the true bit or symbol probabilities and the constellation used at the transmitter, the decoder uses arbitrary probabilities or reference constellations. We study the corresponding low- and high- signal-to-noise-ratio regimes and show that even in the presence of this extra sources of mismatch, BICM has a negligible penalty with respect to coded modulation.

Random-coding exact characterizations and bounds to the error probability of joint source-channel coding are presented. In particular, upper bounds using maximum-a-posteriori and threshold decoding are derived as well as a lower bound motivated by Verdú-Han's lemma.

We show that Gallager's random coding error exponent of an arbitrary binary-input memoryless symmetric channel is upper-bounded by that of the binary erasure channel and lower-bounded by that of the binary-symmetric channel of the same capacity. We apply the result to find the extremes of the channel dispersion for the aforementioned class of channels.

This paper considers random-coding bounds to the decoding error probability with threshold decoders. A slightly improved version of the dependence-testing bound is derived. A loosening of this bound generates a family of Feinstein-like bounds, which improve on Feinstein's original version. The error exponents of these bounds are determined and simple, yet accurate, saddlepoint approximations to the corresponding error probabilities are derived.

Communication across an additive exponential noise (AEN) channel is studied. The AEN channel is derived from a continuous-time Gaussian channel by assuming decoherence between the signal and noise components. Decoherence renders impossible the use of the complex amplitude and information is transmitted by modulating the signal energy. The AEN channel is shown to perform closely—in terms of channel, information rate, and error probability of binary codes, including the use of bit-interleaved coded modulation—to an equivalent discrete-time Gaussian channel of identical signal-to-noise ratio. Constellations are designed for low signal-to-noise ratios, where the minimum energy per bit is not universally attained by all modulations, and for high signal-to-noise ratios, where an equiprobable nonuniform constellation 0.76 dB away from capacity is described. The multiple-access AEN channel is also considered: its capacity region coincides with that of the Gaussian equivalent; feedback does not, however, enlarge the capacity region.

The performance of bit-interleaved coded modulation (BICM) with shaping (i.e., non-equiprobable bit probabilities) is studied. For the AWGN channel, the rates achievable with BICM and shaping are practically identical to those of coded modulation or multilevel coding, virtually closing the gap that made BICM suboptimal in terms of information rates.

In the above titled paper (ibid., vol. 54, no. 12, pp. 5447-5455, Dec. 08), there are three errors that are corrected here.

The information rates achievable by using electromagnetic radiation affected by thermal noise and signal decoherence are studied. The classical coherent-state Gaussian model is compared with an alternative photon gas model which represents lack of a shared phase reference between transmitter and receiver. At any frequency, the information rates in the presence of decoherence essentially coincide with those over a Gaussian model when the signal-to-noise ratio is below a threshold. Only above the threshold does decoherence cause a significant loss in information rates; the loss can amount to half of the capacity. The threshold exceeds 40 dB for radio frequencies and vanishes at optical frequencies.

A simple lower bound to the capacity of the discrete-time Poisson channel with average number of quanta of energy epsivsis derived. The rate 1/2 log(1 + epsivs) is shown to be the generalized mutual information of a modified minimum-distance decoder, when the input follows a gamma distribution of parameter 1/2 and mean epsivs.

This paper studies the average error probability of bit-interleaved coded modulation with uniform interleaving in fully-interleaved fading channels. At large signal-to-noise ratio, the dominant pairwise error events are mapped into symbols with Hamming weight larger than one, causing a flattening of the error probability. Closed-form expressions for the error probability with general modulations are provided. For interleavers of practical length, the flattening is noticeable only at very low values of the error probability.

We revisit the information-theoretic analysis of bit-interleaved coded modulation (BICM) by modeling the BICM decoder as a mismatched decoder. The mismatched decoding model is well defined for finite, yet arbitrary, block lengths, and naturally captures the channel memory among the bits belonging to the same symbol. We give two independent proofs of the achievability of the BICM capacity calculated by Caire, where BICM was modeled as a set of independent parallel binary-input channels whose output is the bitwise log-likelihood ratio. Our first achievability proof uses typical sequences, and shows that due to the random coding construction, the interleaver is not required. The second proof is based on the random coding error exponents with mismatched decoding, where the largest achievable rate is the generalized mutual information. Moreover, the generalized mutual information of the mismatched decoder coincides with the infinite-interleaver BICM capacity. We show that the error exponent—and hence the cutoff rate—of the BICM mismatched decoder is upper-bounded by that of coded modulation and may thus be lower than in the infinite-interleaved model; for binary reflected Gray mapping in Gaussian channels the loss in error exponent is small. We also consider the mutual information appearing in the analysis of iterative decoding of BICM with extrinsic information transfer (EXIT) charts: if the symbol metric has knowledge of the transmitted symbol, EXIT mutual information admits a representation as a pseudo-generalized mutual information, which is in general not achievable. A different symbol decoding metric, for which the extrinsic side information refers to the hypothesized symbol, induces a generalized mutual information lower than the coded modulation capacity. In this case, perfect extrinsic side information turns the mismatched-decoder error exponent into that of coded modulation.

This paper studies the average error probability of bit-interleaved coded modulation with uniform interleaving in fully-interleaved fading channels. At large signal-to-noise ratio, the dominant pairwise error events are mapped into symbols with Hamming weight larger than one, causing a flattening of the error probability. Closed-form expressions for the error probability with general modulations are provided. For interleavers of practical length, the flattening is noticeable only at very low values of the error probability.

We revisit the information-theoretic analysis of bit-interleaved coded modulation (BICM) by modeling the BICM decoder as a mismatched decoder. The mismatched-decoding model is well-defined for finite, yet arbitrary, block lengths, and captures the channel memory among the bits belonging to the same symbol. The generalized mutual information of the mismatched decoder coincides with the infinite-interleaver BICM capacity, where BICM is modeled as a set of independent parallel binary-input channels whose output is the bitwise log-likelihood ratio. The error exponent —and hence the cutoff rate— of the BICM mismatched decoder is upper bounded by that of coded modulation and may thus be lower than in the infinite-interleaved model. For binary reflected Gray mapping in Gaussian channels the loss in error exponent is small.

Communication across an additive exponential noise (AEN) channel is studied. Constellations are designed for low signal-to-noise ratio, where the minimum energy per bit is not universally attained by all modulations, and for high signal-to-noise ratio, where an equiprobable non-uniform constellation 0.76 dB away from capacity is described. The pairwise error probability for binary codes is similar to that in an equivalent discrete-time Gaussian channel of identical signal-to-noise ratio.

The wideband regime of bit-interleaved coded modulation (BICM) in Gaussian channels is studied. The Taylor expansion of the coded modulation capacity for generic signal constellations at low signal-to-noise ratio (SNR) is derived and used to determine the corresponding expansion for the BICM capacity. Simple formulas for the minimum energy per bit and the wideband slope are given. BICM is found to be suboptimal in the sense that its minimum energy per bit can be larger than the corresponding value for coded modulation schemes. The minimum energy per bit using standard Gray mapping on$M$-PAM or$M^2$-QAM is given by a simple formula and shown to approach${-}$0.34 dB as$M$increases. Using the low SNR expansion, a general tradeoff between power and bandwidth in the wideband regime is used to show how a power loss can be traded off against a bandwidth gain.

This paper studies two models derived from the Gaussian Multiple-Access Channel. In each, information is sent by energy modulation, rather than on the complex signal amplitude. For continuous energy, the model is the additive exponential noise (AEN) channel, whose capacity region equals that of the equivalent Gaussian channel at identical noise and energy levels. Differently from the Gaussian case, its capacity region is not enlarged by using feedback; the source of this discrepancy is linked to a conservation of energy law. The second model is a quantized version of the AEN channel. An outer bound to its capacity region is derived, and shown to be strictly contained in the region for the additive exponential/Gaussian noise channel.

This paper studies the wideband regime of bit-interleaved coded modulation (BICM) in Gaussian channels. Simple formulas for the minimum energy per bit and the wideband slope, both for coded modulation and for bit-interleaved coded modulation, are given. The wideband slope can be decomposed into the product of two terms, respectively due to the fading characteristics and the modulation and binary labeling rule. BICM is found to be suboptimal in the sense that its minimum energy per bit can be larger than the corresponding value for coded modulation schemes. The minimum energy per bit using standard Gray mapping on M-PAM, or M2-QAM is given by a simple formula, and shown to approach -0.34 dB as M increases.

This paper describes the application of a new hardware architecture to the design of a decoder for low-density parity-check (LDPC) codes. Thanks to the systematic use of the built-in stopping rule in the decoder, the decoder runs the minimum number of iterations on each packet of received data. The addition of a small buffer on the decoder input allows the exploitation of the variations in the decoding time of different packets, in a spirit similar to statistical multiplexing of the data flow. Analysis and simulations are presented using the decoder for the next generation satellite digital video broadcasting (DVB-S2) as a case study, to show that the throughput may be doubled with only two extra buffer locations, at almost no cost in chip area.

This letter presents a simple closed-form expression to evaluate the error probability of binary fully-interleaved fading channels. The proposed expression does not require a numerical Laplace transform inversion, numerical integration or similar techniques, and captures the role of the relevant system parameters in the overall error performance. The expression has the same asymptotic behavior as the Bhattacharyya (Chernoff)-union bound but closes the gap with the simulation results. Its precision is numerically validated for coded and uncoded transmission over generic Nakagami-m fading channels.

This paper investigates the transmission of information by placing balls in urns. Alice, the transmitter, encodes her message in her choice of the number of balls at each urn, which follows a Poisson distribution, and is subject to an average constraint. Two adversaries, Marvin and Charlie, also put balls into the urns, respectively with geometric and Poisson distributions. Bob, the receiver, retrieves the message by counting the balls in all urns. We provide upper and lower bounds to the channel capacity. We further illustrate the links between this model and electromagnetic radiation, by replacing balls by photons and urns by frequencies or colours

This correspondence presents a simple method to accurately compute the error probability of bit-interleaved coded modulation (BICM). Thanks to the binary-input output-symmetric (BIOS) nature of the channel, the pairwise error probability (PEP) is equal to the tail probability of a sum of random variables with a particular distribution. This probability is in turn computed with a saddlepoint approximation. Its precision is numerically validated for coded transmission over standard Gaussian noise and fully interleaved fading channels for both convolutional and turbo-like codes.

This paper investigates the performance of M-ary amplitude-phase shift keying (APSK) digital modulation over typical nonlinear satellite channels. The effect of the satellite nonlinearity is studied, and distortion pre- and post-compensation techniques for coded APSK are presented. Moreover, clock timing, signal amplitude and carrier phase recovery schemes are discussed. For the latter, a new class of non turbo decoder-aided closed-loop phase synchronizers featuring good performance and low complexity is studied. Finally, an end-to-end coded APSK system simulator inclusive of the satellite channel model and synchronization sub-systems is discussed and its performance compared to standard trellis-coded QAM concatenated with Reed-Solomon codes, showing a remarkable gain in both power and spectral efficiency. Coded APSK, recently selected for the new standard -DVB-S2- for digital video broadcasting and interactive broadband satellite services, is shown to represent a powerand spectral-efficient solution for satellite nonlinear channels.

This paper presents a general methodology to extend Gallager bounds on the maximum-likelihood decoding error probability to arbitrary binary-input output-symmetric memoryless channels. Based on the log-likelihood ratios, a new space is constructed in which the signals naturally lie on a sphere, and for which geometric analysis is straightforward. In particular, we focus on Poltyrev's tangential-sphere bound, and we illustrate its connections with the Engdahl-Zigangirov bound. Approximations to these bounds are shown to be very tight.

The increasing capability of mobile handsets, advancing multimedia processing, improving codec technology (e.g., mp3, JPEG, MPEG-4 SP, H.264), and bigger communication pipes have the potential for providing increasing volumes of multimedia data to mobile users. The problem of intelligently managing multimedia content is becoming increasingly prevalent in this space. For example, applications that archive and retrieve personal content as well as those that search and stream commercial content over wired and wireless channels need to provide a compelling user experience while transparently and efficiently handling the vast amounts of underlying data. This work provides a user-intuitive, standards-based approach for managing multimedia content on mobile handsets.

This paper deals with the application of the "simple" bound technique to a new family of serially concatenated turbo codes and to random codes with the goal of a fair comparison of the maximum likelihood (ML) performance in AWGN channels. Reported results confirm that outer code improvement causes great advantages both for the waterfall and the floor. In particular, it is shown that, for a given rate and block size, better outer encoders make concatenated codes very close to random codes down to the floor. Moreover, the BER floor is shown to be related to the weight distribution. As this becomes more and more binomial (random-code-like), the BER and FER curves improve. Finally, punctured communications are taken into account and a new upper bound of the ML performance is introduced; in particular, a new strategy, called "uniform puncturing", is considered with remarkable results for random codes and, also, for the considered turbo-like codes.