We consider the problem of bounding the information rate of intersymbol interference channels via simulation-based algorithms. The adopted approach, which is based on a general class of reduced-complexity receivers that includes several previously studied receivers as special cases, leads to provable upper and lower bounds on the information rate of interest. As a by-product of the information-theoretic investigations, novel insights on the design of efficient reduced-complexity receivers are also provided, since the proposed lower bounds are known to be achievable by practical receivers. In many scenarios, our novel approach significantly outperforms the existing ones, for all practical values of the signal-to-noise ratio.

We present novel bounds on the capacity of binary channels with independent and identically distributed insertions, deletions, and substitutions. The proposed bounds are obtained by exploiting an auxiliary system where the channel is the same as the one in the system of interest, but the receiver is provided with (partial) genie-aided information on the insertion/deletion process. In particular, we show that, when this information is revealed, we obtain a memoryless channel whose capacity, evaluated by means of the Blahut-Arimoto algorithm, gives an upper bound on the capacity of interest. We also show that capacity lower bounds can be derived as well, by exploiting the same auxiliary system and resorting to suitable information-theoretical inequalities. In most scenarios, the proposed bounds improve the existing ones, and significantly narrow the region to which the actual capacity can belong.

In this paper we propose new bounds on the achievable information rate for discrete-time Gaussian channels with intersymbol interference (ISI) and independent and uniformly distributed (i.u.d.) channel input symbols drawn from finite-order modulation alphabets. Specifically, we are interested in developing new bounds on the achievable rates for sparse channels with long memory. We obtain a lower bound which can be achieved by practical receivers, based on MMSE channel shortening and suboptimal symbol detection for a reduced-state channel. An upper bound is given in the form of a semi-analytical solution derived using basic information theoretic inequalities, by a grouping of the channel taps into several clusters resulting in a newly defined single-input multiple-output (SIMO) channel. We show that the so obtained time-dispersive SIMO channel can be represented by an equivalent single-input single-output (SISO) channel with a significantly shorter channel memory. The reduced computational complexity allows the use of the BCJR algorithm for the newly defined channel. The proposed bounds are illustrated through several sparse channel examples and i.u.d. input symbols, showing that the upper bound significantly outperforms existing bounds. Performance of our lower bound strongly depends on the channel structure, showing best results for minimum-phase and maximum-phase systems.

We investigate a promising coding scheme over channels impaired by insertion, deletion, and substitution errors, i.e., interleaved concatenation of an outer low-density parity-check (LDPC) code with error-correction capabilities and an inner marker code for synchronization purposes. To limit the decoding latency, we start with a single-pass decoding algorithm, that is, marker code-based synchronization is performed only once per received packet and iterative decoding with information exchange between the inner decoder and outer decoder is not allowed. Through numerical evaluations, we first find the marker code structures which offer the ultimate achievable rate when standard bit-level synchronization is performed. Then, to exploit the correlations in the likelihoods corresponding to different transmitted bits, we introduce a novel symbol-level synchronization algorithm that works on groups of consecutive bits, and show how it improves the achievable rate along with the error rate performance by capturing part of the rate loss due to interleaving. When decoding latency is not an issue and multiple-pass decoding is performed, we utilize extrinsic information transfer (EXIT) charts to analyze the convergence behavior of the receiver, which leads to design of outer LDPC codes with good degree distributions. Finally, design examples are provided along with simulation results which confirm the advantage of the newly designed codes over the ones optimized for the standard additive white Gaussian noise (AWGN) channels, especially for channels with severe synchronization problems.

We study two-receiver degraded binary broadcast channels (DBBCs), focusing on the capacity region and the encoding schemes that achieve its boundary. First, we derive the conditions for a general binary broadcast channel to be degraded, and show that only a very limited subset of the possible degraded configurations have been investigated in the previous literature. Then, we show how to design a capacity-achieving encoding scheme for a general DBBC, and give a detailed specific example. The designed scheme turns out to be very general and to include, as special cases, the encoding schemes that achieve the capacity boundary of the DBBCs previously studied in the literature.

We investigate two methods for estimating the matched signal transformations caused by time-varying underwater acoustic channels in orthogonal frequency division multiplexing (OFDM) communication systems. The underwater acoustic channel for this 12-20 kHz medium frequency range OFDM system is best modeled using multipath and wideband Doppler scale changes on the transmitted signal. As a result, our first channel estimation method is based on discretizing the wideband spreading function time-scale representation of the channel output using the Mellin transform. The second method is based on extracting the time-scale features of distinct ray paths in the received signal using a modified matching pursuit decomposition algorithm. We validate and discuss both methods using data from the recent Kauai Acomms MURI 2008 (KAM08) underwater acoustic communication experiment.

We consider serially-concatenated coding schemes over channels impaired by insertion, deletion, and substitution errors. Specifically, we focus on the interleaved concatenation of an outer channel code with error-correction capabilities and an inner marker code with synchronization capabilities. To limit the decoding latency, marker code-based synchronization is performed only once per received packet, i.e., iterations with the outer decoder are not allowed. We first numerically evaluate, through mutual information analyses, the ultimate rate achievable by this concatenated scheme when standard bit-level synchronization is performed. Then, we introduce a novel symbol-level synchronization algorithm that works on groups of consecutive bits, and show that it improves the achievable rate. Besides the achievable rate analyses, which allow us to optimize the marker code, we also report error-rate simulation results that confirm the superiority of symbol-level synchronization.

Unequal error-protection schemes obtained by means of two-level superposition coding are considered. Their performance over Gaussian broadcast channels (GBCs) is investigated with optimal maximum-likelihood decoding as well as with a suboptimal decoding strategy based on interference cancellation. We focus on GBCs without fading and, assuming that linear codes are used, we evaluate, for both decoding strategies, analytical approximations of the word-error rate based on a suitable application of the union bound. As in the case of turbo codes and turbo-coded modulations in schemes without superposition, the derivation of the approximations exploits the concept of uniform interleaving. The analytical expressions obtained are in excellent agreement with the simulation results, and thus provide a useful tool for analysis and design of practical superposition-coding schemes. Unlike the existing design tools, which rely on the assumption of infinite-length superposition codes, the proposed approach allows us to study the effectiveness of finite-length coding schemes with known distance spectrum.

We present novel bounds on the capacity of the independent and identically distributed binary deletion channel. Four upper bounds are obtained by providing the transmitter and the receiver with genie-aided information on suitably-defined random processes. Since some of the proposed bounds involve infinite series, we also introduce provable inequalities that lead to more manageable results. For most values of the deletion probability, these bounds improve the existing ones and significantly narrow the gap with the available lower bounds. Exploiting the same auxiliary processes, we also derive, as a by-product, two simple lower bounds on the channel capacity, which, for low values of the deletion probability, are almost as good as the best existing lower bounds.

We consider unequal error-protection schemes obtained by means of two-level superposition coding. The performance over additive white Gaussian noise channels is investigated for optimal maximum-likelihood decoding as well as for a suboptimal decoding strategy based on interference cancellation. Assuming that linear codes are used, we evaluate, for both strategies, analytical approximations of the word-error rate, based on the union bound. As in the case of turbo codes and turbo-coded modulations, the derivation exploits the concept of uniform interleaving, and the bounds are in excellent agreement with the simulation results obtained using iterative decoding. The analytical expressions are useful for code design and for the selection of decoding strategies providing a suitable performance/complexity tradeoff.

We consider unequal error-protection schemes obtained by two-level superposition coding. While the existing schemes implement superposition by means of linear modulations, we propose superposition implemented through continuous phase modulations (CPMs). In the considered scheme, unlike in the linearly-modulated ones, the transmitted signal has constant envelope and thus the system does not rely on the presence of expensive amplifiers. We investigate the potential of CPMs for multi-rate transmissions over channels impaired by additive white Gaussian noise. Particularly, we derive the relevant algorithm for maximum-a-posteriori symbol detection, evaluate the ultimate information rate, and design practical coding schemes that perform fairly close to the theoretical limits.

We present two upper bounds on the capacity of the binary deletion channel. Both bounds are obtained by providing the transmitter and the receiver with genie-aided information on suitably-defined random processes. Since the closed-form expressions of the proposed bounds involve infinite series, we also introduce provable inequalities that lead to more manageable results. For most values of the deletion probability, these bounds improve the existing ones and significantly narrow the gap with the available lower bounds.

We present novel bounds on the capacity of binary channels with independent and identically distributed deletions and substitutions. The proposed bounds are obtained by exploiting an auxiliary system where the channel is the same as in the system of interest, but the receiver is provided with (partial) genie-aided information on the deletion/substitution process. In the case of the deletion channel, that is, when no substitutions occur, the proposed upper bound improves the existing ones for most values of the deletion probability, while the proposed lower bound does not. On the other hand, when the channel model also includes substitution errors, both proposed bounds improve the existing ones, significantly narrowing the region to which the actual capacity can belong.

We consider the problem of lower bounding the information rate of intersymbol interference channels via Monte Carlo algorithms. We adopt a novel approach based on the Ungerboeck observation model, unlike the existing ones that are all based on the Forney model. The two approaches, yet equivalent in the case of full-complexity detection, lead to different results in the case of reduced-complexity detection, which is the only viable option when the channel memory is large. The proposed approach significantly outperforms the existing ones for all practical values of the signal-to-noise ratio.

We propose a novel simulation-based method to evaluate the performance of Viterbi decoders. In particular, we address scenarios where the error probability is very low, that is, scenarios where classical Monte Carlo simulations would require impractical execution times before producing reliable results. As other recent fast-simulation approaches, the proposed method relies on the multicanonical Monte Carlo technique, but, unlike the existing general-purpose methods, it is specifically designed for Viterbi decoders, the algorithm being driven by a control variable that depends on the state metrics of the various survivors over the trellis. In simple scenarios for which analytical tools are available, the simulation results agree with them, while, in the most common scenarios, no analytical tool is available and the proposed method gives the fastest way for the estimation of low error probabilities.

We investigate the spectral efficiency, achievable by a low-complexity symbol-by-symbol receiver, when linear modulations based on the superposition of uniformly time- and frequency-shifted replicas of a base pulse are employed. Although orthogonal signaling with Gaussian inputs achieves capacity on the additive white Gaussian noise channel, we show that, when finite-order constellations are employed, by giving up the orthogonality condition (thus accepting interference among adjacent signals) we can considerably improve the performance, even when a symbol-by-symbol receiver is used. We also optimize the spacing between adjacent signals to maximize the achievable spectral efficiency. Moreover, we propose a more involved transmission scheme, consisting of the superposition of two independent signals with suitable power allocation and a two-stage receiver, showing that it allows a further increase of the spectral efficiency. Finally, we show that a more involved equalization algorithm, based on soft interference cancellation, allows to achieve an excellent bit-error-rate performance, even when error-correcting codes designed for the Gaussian-noise limited channel are employed, and thus does not require a complete redesign of the coding scheme.

Digital communications over channels impaired by impulse noise are considered. We first address the problem from an information-theoretical viewpoint, discussing the performance limits imposed by the channel model. Then, we describe and compare a couple of practical communication schemes employing powerful channel codes and iterative decoding, with focus on a very simple and robust detection scheme that does not require the estimation of the statistics of the impulse noise.

Digital communications over channels impaired by impulse noise are addressed. We adopt a two-state Markov model that allows to describe the typical bursty nature of the impulse noise, in contrast to the memoryless models generally considered in the literature. For this channel, we evaluate the achievable information rate and propose a couple of practical communication systems based on powerful codes and iterative receivers. Moreover, we discuss the effectiveness of the considered receivers in terms of performance/latency tradeoff as well as in terms of robustness to erroneous channel estimations. The proposed schemes are shown to perform fairly close to the theoretical limits, and significantly better than the conventional schemes employing memoryless detection.

We investigate the spectral efficiency of continuous phase modulations (CPMs). To this end, we need an effective bandwidth definition for a CPM signal, whose power spectral density has in principle an infinite support. The definition we adopt is based on the spacing between adjacent carriers in a frequency division multiplexed CPM system. We consider the inter-channel interference, which depends on the channel spacing, and we evaluate the spectral efficiency achievable by a single-user receiver in the considered multi-channel scenario. We then optimize the channel spacing with the aim of maximizing the spectral efficiency, showing that impressive improvements with respect to the spectral efficiencies reported in the literature and obtained by heuristic approaches can be achieved.

We address the design of low-complexity algorithms for soft-output detection over channels impaired by intersymbol interference. Unlike most works with similar aims, which assume the presence of the whitened matched filter at the receiver (Forney approach), algorithms that can directly work on the matched filter output (Ungerboeck approach) are considered. We introduce a novel (cyclic) factor graph describing the channel and, by applying the sum-product algorithm to it, we derive soft-output detection schemes that can provide impressive complexity reductions with respect to the benchmark algorithms, since their complexity is linear, instead of exponential, in the channel memory. Finally, we report simulation results proving that the performance of the proposed algorithms makes them appealing for turbo equalization in various practical scenarios.

We consider digital communications over channels impaired by impulse noise. A two-state Markov channel model is adopted, which, unlike the memoryless models generally considered in the literature, allows to describe the typical bursty nature of the impulse noise. First, by means of information-theoretical arguments, we compute and discuss the ultimate performance limits of such systems. Then, we derive an algorithm for optimal maximum-a-posteriori symbol detection, and explain how to exploit it in receivers employing iterative detection/decoding. Finally, we report simulation results showing the effectiveness of the proposed schemes, as well as an excellent agreement with the computed theoretical limits.

We investigate the spectral efficiency, achievable by a low-complexity symbol-by-symbol receiver, when linear modulations based on the superposition of uniformly time- and frequency-shifted replicas of a base pulse are employed. Although orthogonal signaling with Gaussian inputs achieves capacity on the additive white Gaussian noise channel, we show that, when finite-order constellations are employed, by giving up the orthogonality condition (thus accepting interference among adjacent signals) we can considerably improve the performance, even when a symbol-by-symbol receiver is used. We also optimize the spacing between adjacent signals to maximize the achievable spectral efficiency. Moreover, we propose a more involved system model, consisting of the superposition of two independent signals and a receiver based on successive interference cancellation, showing that it allows a further increase of the spectral efficiency.

We investigate the spectral efficiency of continuous phase modulations (CPMs). To this end, we need an effective bandwidth definition for a CPM signal, whose power spectral density has in principle an infinite support. The definition we adopt is based on the spacing between adjacent carriers in a frequency division multiplexed CPM system. We consider the inter-channel interference, that depends on the channel spacing, and we evaluate the spectral efficiency achievable by a single- user receiver in the considered multi-channel scenario. We then optimize the channel spacing with the aim of maximizing the spectral efficiency, showing that impressive improvements with respect to the spectral efficiency achieved without optimizing the channel spacing can be achieved. I.

We compare low-complexity schemes for soft-output detection of continuous phase modulations (CPMs). In particular, we address the problem of minimizing the complexity of the front end filters and that of the trellis exploited by the detection algorithm, with the aim of assuring a negligible performance degradation with respect to the optimal full-complexity receiver. We show that the approach providing the simplest front end is that based on the CPM decomposition proposed by Moqvist and Aulin. On the other hand, we prove that the most convenient solution in terms of trellis complexity is provided by the CPM decomposition proposed by Mengali and Morelli, possibly combined with suitable techniques for reduced trellis search.

We consider digital communications over channels affected by Class-A impulse noise. First, by means of information theoretic arguments, the performance limits of such systems are derived and discussed. Then, practical communication schemes employing powerful channel codes with iterative decoding are presented. Together with an ideal detection scheme which perfectly knows the statistics of the impulse noise, we describe a sub- optimal scheme which ensures a limited performance degradation even when a reliable statistical characterization of the noise is not available. Finally, by means of computer simulations, we show the effectiveness of the described schemes, whose performance is fairly close to the derived theoretical limits.

We propose novel techniques to reduce the complexity of the well-known Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them being particularly effective over one of the three different classes of channels (minimum-phase, maximum-phase, and mixed-phase channels) affected by intersymbol interference. The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, which are chosen according to the maximuma posterioricriterion. Moreover, some optimization techniques for improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and compare them with other solutions in the literature.

We propose novel techniques to reduce the complexity of the well-known Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original one for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them particularly effective over one of the three different classes of channels affected by intersymbol interference (minimum-phase, maximum-phase, and mixed-phase channels). The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, chosen according to the maximum a posteriori criterion. Moreover, some optimization techniques improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and we compare them with other solutions in the literature.

We present innovative techniques to reduce the complexity of the well known BCJR detection algorithm applied on channels affected by intersymbol interference. On a general channel with mixed phase, we independently perform two reduced trellis searches in the forward and backward recursions, and propose a modified completion stage for combining them. When iterative detection/decoding is performed, the reduced searches are defined on the basis of a probabilistic criterion which provides a noticeable performance improvement with respect to the other reduced-complexity algorithms. On the other hand, for channels with minimum phase, we show that the simplified backward recursion can operate on the same set of paths selected during the forward search. Computer simulations confirm the effectiveness of the proposed algorithms in various turbo equalization schemes.