In our increasingly tech-saturated world, from mobile apps and health sensors to autonomous cars and factory robots, we expect these devices to seamlessly integrate into our lives, enhancing safety and convenience. However, as these devices proliferate and their autonomy grows, ensuring they provide unobtrusive, yet effective support becomes crucial. The Horizon Europe KST multi-partner project “Distributed Multi-Sensor Systems for Human Safety and Health” (DistriMuSe) intends to support human health and safety by improved sensing of human presence, behaviour, and vital signs in a collaborative or common environment by means of multi-sensor systems, distributed processing and MachinelDeep Learning (ML/DL) techniques. In this paper, we focus on the DistriMuSe's approach on sports performance and health assessment, focusing on monitoring the physical activity of non-professional and hobby athletes, people who like sports and care about their health, elderly healthy people, and subjects affected by neurological disability (e.g., Parkinson's disease). The overall goal is to measure activity and exertion, estimating performance levels and determining maximum effort. We discuss the overall system-of-systems architecture, focusing on the adopted technologies.

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

In this paper, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs.

We consider downlink communications between a Base Station (BS) and various mobile stations, equipped with multiple antennas, based on Orthogonal Frequency Division Multiplexing (OFDM). Transmission is compliant with the Long Term Evolution (LTE) standard operating in Frequency Division Duplex (FDD) mode. Since ideal feedback of channel state information to the BS may be cumbersome, we consider two suboptimal channel estimation algorithms, denoted as Resource Block (RB) and Resource Block Group (RBG). Both approaches approximate the channel as constant over multiples of the fundamental LTE block, known as Physical Resource Block (PRB). Our results show that RB and RBG incur a limited performance loss, yet guaranteeing significant saving in the amount of feedback information.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separatea posterioriprobability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

This study discusses synchronisation in phase noise‐impaired spectrally efficient communication systems employing high‐order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off‐the‐shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low‐density parity‐check‐coded pilot symbol‐aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted through additive white Gaussian noise (AWGN) channels to an access point (AP). At the AP, component decoders, which are associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use “off-the-shelf” channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes, is discussed.

In this paper, the performance of a cooperative spectrum sensing (CSS) is evaluated, considering maximal ratio combining (MRC) fusion in the presence of Rayleigh fading. A cognitive radio (CR) senses the primary users (PUs) using an energy detector (ED) in the sensing channel (S-channel). If a CR is selected to transmit its local decisions to a fusion centre (FC), it does so using a reporting (R)-channel with binary phase shift keying (BPSK) signaling. In the present work, we consider censoring of CRs based on a characterization of Rayleigh faded R-channels. In other words, CRs, whose estimated R-channel fading coefficients towards the FC exceed a predefined threshold (censoring threshold), are allowed to transmit. MRC fusion is considered at the FC to estimate the performance in terms of average missed detection and total error probabilities for various values of (i) the censoring threshold, (ii) the number of CRs, (iii) the average S-channel and R-channel signal-to-noise ratios (SNRs), under both perfect and imperfect channel estimations. The performance of CSS system with MRC fusion is also compared, for various values of the network parameters, with the performance with majority logic fusion.

In this paper, we present a novel approach to early diagnosis, through a video processing-based approach, of the presence of neonatal seizures. In particular, image processing and gesture recognition techniques are first used to characterize typical gestures of neonatal seizures. More precisely, gesture trajectories are characterized by extracting some relevant features. In particular, selecting the point with the maximum amplitude of the optical flow vector of the video frame sequence, during a newborn movement, is selected and then tracked through an algorithm based on template matching and optical flow. The observed features are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed approach allows to efficiently differentiate pathological repetitive movements (e.g., clonic and subtle seizures) from random ones.

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

In this paper, we consider the problem of fusing decisions in a distributed detection system when the local binary decisions made at the sensors, relative to observations of a common binary phenomenon, are transmitted over wireless links subject to Rayleigh flat fading and additive noise. A training-based channel estimator is used at the fusion center (FC) to estimate the complex Gaussian fading coefficients characterizing the channels between the sensors and the FC. We use channel state information (CSI) on the fading coefficients for censoring the sensors. Locally optimal decision threshold is considered for binary quantization at the sensors. The detection error probability, using a maximal ratio combining (MRC) fusion rule, is selected as a qualitative measure of system performance and is evaluated by means of simulations. We also use majority logic fusion at the FC to compare detection error probability with MRC after censoring sensors. We study the effects, on the system performance, of the channel estimation error, the channel signal-to-noise ratio (SNR), the sensor SNR, and the number of selected sensors.

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones.

In this paper, we present a novel pragmatic approach, referred to as detection by multiple trellises, to perform trellis-based detection over realistic channels. More precisely, we consider channels with unknown parameters and apply the concept of detection by multiple trellises to forward-backward (FB) algorithms. The key idea of our approach consists, first, of properly quantizing the channel parameters and, then, considering replication of coherent FB algorithms operating on parallel trellises, one per hypothetical quantized value. In order to make the receiver robust against a possibly time-varying channel parameters, the proposed soft-output algorithms perform a proper "manipulation" of the forward and backward metrics computed by the parallel FB algorithms at regularly spaced trellis steps. We consider two significant examples of application: detection over (i) phase-uncertain channels and (ii) fading channels. The performance of the proposed algorithms is investigated considering differentially encoded (DE) quaternary phase shift keying (QPSK) and iterative detection schemes based on low-density parity-check (LDPC) codes. Besides having a low complexity, the proposed soft-output algorithms turn out to be robust, flexible, blind, in the sense that no knowledge of the channel parameter statistics is required, and highly parallelizable, as it is desirable in high-throughput future wireless communication systems.

In this paper, we investigate the ultimate performance limits, in terms of achievable information rate (IR), of communication systems impaired by impulse noise. We compare single carrier (SC) and multi-carrier (MC) transmission systems employing quadrature amplitude modulation (QAM) formats. More precisely, we consider SC schemes with coded modulations and MC systems based on orthogonal frequency division modulation (OFDM). For the MC schemes, we introduce a theoretically equivalent channel model which makes the computation of the IR feasible. This simple channel model will be referred to as interleaved MC. We show that, in the presence of impulse noise and except for systems operating at very high spectral efficiency, the IR of MC schemes is lower than that of SC schemes. More precisely, use of MC schemes may lead to an unavoidable fundamental loss with respect to SC schemes at typical coding rates, whereas MC schemes are to be preferred for very high coding rates or in uncoded systems. These results hold for additive white Gaussian noise (AWGN) and dispersive channels, either considering plain OFDM or MC schemes employing water-filling and bit-loading algorithms. In order to validate our theoretical results, we also obtain the bit error rate (BER) performance of SC and MC schemes through Monte Carlo simulations. A few trellis-coded modulation (TCM) and low-density parity-check (LDPC)-coded schemes are considered. The obtained SNR loss in the BER curves between the AWGN and impulse noise channels matches well with the corresponding IR gap.

In this paper, a proposed oversampled detector for digital storage channels affected by transition noise is analyzed in terms of information rates. To this purpose, a simulation-based method is applied to evaluate the information rates. The proposed analysis extends work on the information rates of magnetic recording channels affected by colored Gaussian thermal noise, intersymbol interference and signal-dependent transition noise. The results suggest that oversampling may represent an effective and practical solution for transition-noise-limited digital storage channels.

In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of soft-input soft-ouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encompassing unknown parameters, either stochastic or deterministic. The proposed solutions represent effective ways to reach this goal. The described algorithms are grouped into three categories: i) we first introduce algorithms for adaptive iterative detection, where the unknown channel parameters are explicitly estimated; ii) then, we consider finite-memory iterative detection algorithms, based on ad hoc truncation of the channel memory and often interpretable as based on an implicit estimation of the channel parameters; and iii) finally, we present a general detection-theoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.

In the last decade, significant research has been performed on detection algorithms capable of mitigating the effects of colored Gaussian thermal noise and transition noise in digital storage systems. In this paper, we present a new Maximum A-Posteriori Probability (MAP) sequence detection scheme based on oversampling and linear prediction. The proposed solution improves the Bit Error Rate (BER) performance with respect to conventional systems and makes the detector more robust against transition noise. The results obtained for a magnetic channel can be also extended to optical storage systems.

In this letter, we discuss the performance of low-density parity-check (LDPC) codes on memoryless channels. Using a recently proposed analysis technique based on extrinsic information transfer (EXIT) charts, we present an interpretation of the known fact that the bit-error rate (BER) performance of an ensemble of LDPC codes shows little dependence on the specific memoryless channel. This result has been partially observed in the literature for symmetric channels and is here extended to asymmetric channels. We conjecture and demonstrate that the performance of an ensemble of LDPC codes depends primarily and solely on the mutual information (MI) between the input and the output of the channel. As a validation of this conjecture, we compare the performance of a few LDPC codes with various rates for five representative memoryless (both symmetric and asymmetric) channels, obtaining results in excellent agreement with the EXIT chart-based prediction

In this paper, we consider the asymptotic (for high signal-to-noise ratio, SNR) behavior of the mutual information (MI) between the input and the output of a two-dimensional additive white Gaussian noise (AWGN) channel with continuous input distributions. In particular, the obtained results apply to modulation formats with dense constellations. The accuracy of the presented results is the higher the denser are the considered constellations. We find two simple expressions relating the MI of the considered continuous input distribution, as a function of the SNR, to that of the Gaussian input distribution, in one- and two-dimensional cases, respectively.

In this paper, we present a novel class of simple soft-input soft-output (SISO) detection algorithms for transmission over phase-uncertain channels. In order to limit the complexity, the set of possible phase values (i.e., the interval [0,2pi)) is properly quantized. We then use a forward-backward (FB) algorithm in correspondence to each quantized phase value. The forward and backward metrics computed by the different FB algorithms are properly combined to make the receiver robust against the time-varying phase. We apply the proposed SISO algorithms to perform joint detection and decoding of serially concatenated low-density parity-check (LDPC) codes and differentially encoded (DE) quaternary phase shift keying (QPSK), transmitted over a phase-uncertain channel. As a validation of the effectiveness of the proposed algorithms, we show that the performance loss with respect to that of the corresponding ideal (i.e., perfectly coherent) system is limited. Besides having a limited complexity, the proposed SISO algorithms are blind, i.e., no knowledge of the channel phase statistics is required, and highly parallelizable, as desirable in high-throughput future wireless communication systems

A new simulation-based method to evaluate the information rates of multidimensional front-ends applied to digital storage channels with transition noise is presented. First, we propose an algorithm which extends recent work on the information rates of magnetic recording channels affected by colored Gaussian thermal noise, intersymbol interference and signal-dependent transition noise, by using linear prediction and state reduction techniques. Moreover, following a previous study on statistical sufficiency, we extend this algorithm to magnetic channels with a multidimensional front-end. The results suggest that significant gains may be achievable by multidimensional signal processing techniques in transition-noise limited digital storage channels

In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm, forward-backward, and sum-product algorithms, shows that a basic metric emerges naturally under very general causality and finite-memory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-memory condition. This conditional Markov property, although seldom met exactly in practice, is shown to represent a reasonable and useful approximation in all considered cases. We consider, as examples, linear predictive and noncoherent detection schemes. While good performance for increasing complexity can often be achieved with a finite-memory detection strategy, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity.

In this paper, we consider serially concatenated schemes with outer novel and efficient low-density parity-check (LDPC) codes and inner modulations effective against channel impairments. With a pragmatic approach, we show how to design LDPC codes tailored for simple and robust modulation formats, such as differentially encoded (DE) modulations. The LDPC codes are optimized through the use of a recently proposed analysis technique based on extrinsic information transfer (EXIT) charts. In particular, we optimize, through a "clever" random walk in the parametric space, the degree distributions of the outer LDPC codes, obtaining significant insights on the impact of such distributions on the performance of the proposed concatenated schemes. The optimization is carried out for transmission over both the additive white Gaussian noise channel and a noncoherent channel. The performance predicted by the EXIT chart-based optimization is confirmed by results obtained via computer simulations, considering phase-shift keying and quadrature amplitude modulation at the transmitter side, and iterative demodulation/decoding at the receiver side. The significance of the proposed optimized design of LDPC-coded schemes with DE modulations is validated by the fact that standard nonoptimized LDPC codes perform poorly when used together with inner DE modulations.

We propose a general framework for trellis-based detection over channels with infinite memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that trellis-based maximum a posteriori (MAP) symbol detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this memory-truncation assumption. A general approach to the design of truncated-memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy.

The subject of this paper is the asymptotic optimality of finite-memory detection for transmission over a channel characterized by a single multiplicative time-invariant stochastic parameter (e.g., block frequency nonselective fading). It is known that any finite-memory detection algorithm, either trellis-based or graph-based, is characterized by a single basic metric. We present a theorem which proves that this metric tends, asymptotically, to that of a receiver with perfect channel state information

We propose a general framework for detection over channels with infinite memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific detection strategy.

In this paper we consider iterative detection over bandpass channels which introduce an unknown phase rotation in the transmitted signal. We first introduce a unified formulation of adaptive forward-backward algorithms for channels with parametric uncertainty, including both recursive and non-recursive estimation strategies, and then apply this framework to a phase noncoherent channel. Two main classes of adaptive forward-backward algorithms are then considered and compared: closed-loop algorithms, which use explicit recursive phase estimation, and open-loop algorithms, which use implicit non-recursive phase estimation. We consider schemes with combined detection and decoding. Pilot symbols are inserted in order to cope with the unknown time-varying channel phase.

Provides an abstract of articles to be presented in a forthcoming issue.

Noncoherent sequence detection algorithms, previously proposed by the authors, have a performance which approaches that of coherent detectors and are robust to phase and frequency instabilities. These schemes exhibit a negligible performance loss in the presence of a frequency offset, provided this offset does not exceed an order of 1% of the signaling frequency. For higher values, the performance rapidly degrades. In this paper, detection schemes are proposed, characterized by high robustness to frequency offsets and capable of tolerating offset values up to 10% of the signaling frequency. More generally, these detection schemes are very robust to rapidly varying phase and frequency instabilities. The general case of coded linear modulations is addressed, with explicit reference to M-ary phase shift keying and quadrature amplitude modulation.

A new maximum-likelihood sequence detection receiver for spectrally efficient linear modulations on bandlimited bandpass nonlinear channels is proposed. The receiver is based on oversampling the received signal corrupted by noise and nonlinear distortion. Contrary to other solutions in the literature, in the proposed technique there is no need for a bank of matched filters, and the receiver front end reduces to a single lowpass filter. For a given peak power level, a performance gain can be achieved over more traditional approaches to transmission on nonlinear channels, such as those based on predistortion, if a moderate spectral expansion is allowed. To analyze the receiver performance, the concept of distance spectrum is employed, since the minimum distance alone cannot account for a reliable performance evaluation. Both analysis and simulation are carried out for realistic narrowband nonlinear channels, possibly employing reduced-state sequence detection. Appreciable gain margins are confirmed to be possible in these realistic cases.

In a paper by Cavallini et al. (see IEEE Trans. Commun., vol. 45, p.456-63, Apr. 1997), chip-level differential encoding/detection for direct-sequence spread-spectrum signals was proposed to cope with frequency-nonselective fast fading channels. It was shown that, unlike in the additive white Gaussian noise channel, in time-varying fading channels the system performance may be considerably improved, especially when the spreading factor is increased. In this paper, noncoherent sequence detection, recently proposed by the authors, is the starting point for the derivation of receivers with improved performance with respect to that of standard differential detection. For M-ary phase-shift keying signals, a theoretical analysis is performed and the results are confirmed by means of computer simulation. The performance advantage of taking into account a larger phase memory, with respect to the minimum accounted for by differential detection, is demonstrated. In particular, the amount of phase memory is optimized as a function of the Doppler spread for a Rayleigh frequency-nonselective fading channel. The robustness in the presence of phase noise is also investigated by means of computer simulation.

We consider possible solutions for noncoherent decoding of concatenated codes with spectrally efficient modulations. Serially concatenated coding structures and possible schemes derived from turbo trellis coded modulation (T-TCM) are considered. In both cases, at the receiver side we consider joint detection and decoding. Since taking into account an augmented channel memory leads to an intolerable trellis size, we consider a recently proposed state-reduction technique.

We propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms. with a structure similar to that of the well-known algorithm by Bahl, Cocke, Jelinek, and Raviv (1974). This work is inspired by reduced-state sequence detection (RSSD). The key idea is the construction, during one of the recursions in the reduced-state trellis, of a "survivor map" to be used in the other recursion. In a more general setting, two distinct survivor maps could be determined in the two recursions and used jointly to approximate the a posteriori probabilities. Three examples of application to iterative decoding are shown: (1) coherent detection for intersymbol interference (ISI) channels; (2) noncoherent detection based on an algorithm previously proposed by the authors; and (3) detection based on linear prediction for Rayleigh fading channels. As in classical RSSD, the proposed algorithm allows significant state-complexity reduction with limited performance degradation.

We address the use of the extrinsic information generated by each component decoder in an iterative decoding process. The BJCR algorithm proposed by Bahl et al. (1974) and the soft-output Viterbi algorithm (SOVA) are considered as component decoders. In both cases, we consider, in a unified view, various feedback schemes which use the extrinsic information in different fashions. Numerical results for a classical rate-1/2 turbo code and a serially concatenated code transmitted over a memoryless additive white Gaussian noise (AWGN) channel are provided. The performance of the considered schemes leads to interesting remarks about the nature of the extrinsic information.

We address maximum likelihood sequence detection (MLSD) for communications on flat Rayleigh fading channels. Making use of linear prediction we derive the structure of an oversampled detector which is computationally efficient and can easily accommodate a time varying Doppler frequency. This goal is achieved through a specific analytical derivation of the prediction coefficients which appear in the metric of the sequence detector. For fractionally-spaced observations, we derive the constraints which allows one to establish an explicit relationship between the set of observation predictors and the set of fading predictors. Then, the equivalence between different classes of prediction-based detectors is shown. The numerical results give useful indications to balance the complexity of these detectors in terms of prediction order, description of intersymbol interference and oversampling.

We propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms with a structure similar to that of the well-known algorithm by Bahl, Cocke, Jelinek and Raviv (1974). This work is inspired by reduced-state sequence detection (RSSD). The key idea is the construction, during one of the recursions, of a "survivor map", on a reduced-state trellis, to be used in the other recursion. Two examples of application of the proposed technique to iterative decoding structures are shown, namely coherent detection over intersymbol interference (ISI) channels and noncoherent detection based on an algorithm previously proposed by the authors.

The previously proposed noncoherent sequence detection (NSD) algorithms have a performance which approaches that of coherent detectors and are robust to phase negligible performance loss in the presence of a frequency offset, provided this offset does not exceed an order of 1% of the signaling frequency. For higher values, the performance rapidly degrades. In this paper, two detection schemes are proposed, characterized by high robustness to frequency offsets and capable of tolerating offset values up to 10% of the signaling frequency. More generally, these detection schemes are very robust to rapidly varying phase and frequency instabilities. The general case of coded linear modulations is addressed, with explicit reference to M-ary phase shift keying (M-PSK) and quadrature amplitude modulation (M-QAM).

A new class of noncoherent sequence detection (NSD) algorithms for combined demodulation and decoding of any coded linear and continuous phase modulations, transmitted over additive white Gaussian noise (AWGN) channels, has been previously presented. In this paper, this class is generalized to the case of frequency nonselective Rayleigh or Rice slowly fading channels, in the presence or absence of channel state information. Coded linear modulations, namely M-ary phase shift keying (M-PSK) and quadrature amplitude modulation (M-QAM), are considered. The proposed detection schemes have a performance which approaches that of coherent detectors, are very robust to phase and frequency instabilities, and compare favorably to other solutions previously proposed in the technical literature.

Previously, noncoherent sequence detection schemes for coded linear and continuous phase modulations have been proposed, which deliver hard decisions by means of a Viterbi algorithm. The current trend in digital transmission systems toward iterative decoding algorithms motivates an extension of these schemes. In this paper, we propose two noncoherent soft-output decoding algorithms. The first solution has a structure similar to that of the well-known algorithm by Bahl et al. (1974), whereas the second is based on noncoherent sequence detection and a reduced-state soft-output Viterbi algorithm. Applications to the combined detection and decoding of differential or convolutional codes are considered. Further applications to noncoherent iterative decoding of turbo codes and serially concatenated interleaved codes are also considered. The proposed noncoherent detection schemes exhibit moderate performance loss with respect to corresponding coherent schemes and are very robust to phase and frequency instabilities.

A theoretical performance analysis of noncoherent sequence detection schemes previously proposed by the authors for combined detection and decoding of coded M-ary phase-shift keying (M-PSK) is presented. A method for the numerical evaluation of the pairwise error probability-for which no closed-form expressions exist-is described, the classical union bound is computed, and results are compared with computer simulations. An upper bound on this pairwise error probability is also presented. This upper bound may be effectively used for the definition of an equivalent distance, which may be useful in exhaustive searches for optimal codes. Using this bound, it is proven that, in the general coded case, the considered noncoherent decoding schemes perform as close as desired to an optimal coherent receiver when a phase memory parameter is sufficiently large. In the case of differentially encoded M-PSK, a simple expression of the asymptotic bit-error probability is derived, which is in agreement with simulations for high as well as low signal-to-noise ratio (SNR).

The capacity of a random-phase additive white Gaussian noise (AWGN) channel, referred to as noncoherent channel, is investigated in the case of a transmission of N information symbols. The non-Gaussianity of the capacity achieving distribution is shown and a lower bound on the channel capacity is derived. For increasing values of the number of transmitted symbols N, the capacity of a noncoherent channel is shown to asymptotically approach that of a coherent channel, i.e., a known-phase AWGN channel. The asymptotical Gaussianity of the capacity-achieving distribution is also shown. Based on the derived lower bound, the inherent capacity loss of a noncoherent channel, as compared to a coherent one may be considered very limited for values of N of a few units or a ten. This result may be viewed as the information theoretic counterpart of a similar conclusion derived by many authors with reference to the probability of detection error.

Colavolpe and Raheli have proposed (see Proc. IEEE Intern. Conf. Univ. Pers. Commun. (ICUPC'98), Florence, Italy, October 1998, and Proc. Global Commun. Conf., (GLOBECOM'98), Sydney, Australia, 1998), noncoherent sequence detection schemes for any coded modulation. These schemes produce hard decisions via a Viterbi algorithm. The current trend in digital transmission systems toward iterative decoding algorithms motivates an extension of these schemes. We propose a noncoherent soft-output algorithm for coded linear modulations whose structure is similar to that of the well-known algorithm by Bahl et al. (1974). The application to the case of combined detection and decoding of differential or convolutional codes is considered and comparisons with noncoherent sequence detection are performed. As an example of application to iterative processing, noncoherent decoding of turbo codes is considered.

In this paper, noncoherent sequence detection, proposed in a companion paper by Colavolpe and Raheli (see ibid. vol.47, no.9, p.1376-85, 1999), is extended to the case of continuous phase modulations (CPMs). The results in the companion paper on linear modulations with intersymbol interference (ISI) are used here because a CPM signal is mathematically equivalent to a sum of ISI-affected linearly modulated components, according to the Laurent decomposition. The proposed suboptimal detection schemes have a performance which approaches that of coherent detection with acceptable complexity, allow for time-varying phase models, and compare favorably with previously proposed solutions.

New noncoherent sequence detection algorithms for combined demodulation and decoding of coded linear modulations transmitted over additive white Gaussian noise channels, possibly affected by intersymbol interference, are presented. Optimal sequence detection in the presence of a random rotation of the signal phase, assumed to be constant during the entire transmission, requires a receiver complexity exponentially increasing with the duration of the transmission. Based on proper approximations, simple suboptimal detection schemes based on the Viterbi algorithm are presented, whose performance approaches that of coherent detection. In a companion paper by Colavolpe and Raheli (see ibid., vol.47, no.9, p.1303-7, 1999), noncoherent sequence detection is extended to continuous phase modulations. In the proposed schemes, the tradeoff between complexity and performance is simply controlled by a parameter, referred to as implicit phase memory, and the number of states of a trellis diagram. Besides being realizable, these schemes have the convenient feature of allowing us to remove the constant phase assumption and encompass time-varying phase models. The proposed schemes compare favorably with other solutions previously proposed in the technical literature.

In this paper, we present a theoretical performance analysis of noncoherent schemes for the combined sequence detection and decoding of coded M-ary phase shift keying (M-PSK) signals (Colavolpe and Raheli 1997). Based on the classical union bound, we describe a method for the numerical evaluation of the pairwise error probability, for which no closed-form expressions exist, and compare results with computer simulations. We also present an upper bound to this pairwise error probability which may be effectively used for an exhaustive search of optimal codes. This bound allows us to prove that the noncoherent sequence detection schemes proposed in Colavolpe and Raheli perform as well as an optimal coherent receiver when an implicit phase memory parameter is sufficiently large. In the case of differentially encoded M-PSK, we derive a simple expression for the asymptotic bit error probability which is in agreement with simulations for high as well as low signal-to-noise ratio.

Based on a polynomial representation of a memoryless bandpass nonlinearity, a new realization of an optimal receiver is proposed to perform maximum likelihood detection of data sequences transmitted over nonlinear, possibly time-dispersive, channels. The receiver employs oversampling of the observed signal to compute proper branch metrics for a Viterbi processor. Error performance is compared to that of an optimal receiver for the linear channel obtained by ideal analog predistortion of the nonlinear device under a peak-power constraint. In the presence of nonlinear distortion, a significant improvement in the symbol error rate is shown to be achievable by optimal detection with respect to ideal predistortion. The numerical results are based on both analytic and simulation methods.

Based on an extension of Laurent (1986) decomposition of continuous phase modulation (CPM) signals into a sum of linearly modulated components, we derive a class of reduced-complexity maximum-likelihood (ML) coherent detection and closed-loop phase synchronization schemes. The complexity of the resulting detection schemes is significantly reduced with respect to that of optimal coherent receivers with negligible performance loss. This result extends a known one valid for the binary case to multilevel CPM. The proposed synchronization schemes are perfectly suitable to be used in conjunction with these receivers.

We present new non-coherent sequence detection algorithms for M-ary phase shift keying (M-PSK) signals transmitted over additive white Gaussian noise (AWGN) channels. We first consider the problem of optimal sequence detection in the presence of an unknown channel phase modeled as stochastic with uniform distribution and constant during the entire transmission. In this case, the complexity of the optimal receiver depends exponentially on the duration of the transmission. We then introduce some approximations in order to realize simple suboptimal detection schemes based on the Viterbi algorithm (VA) whose performance approaches that of coherent detection. Besides being realizable, the proposed suboptimal schemes have the convenient feature of allowing us to remove the assumption of constant phase during transmission. We show that our detection schemes compare favorably with other solutions previously proposed in the literature for both differentially and non-coherent coded modulations.

Based on an extension of Laurent (1986) decomposition of continuous phase modulation (CPM) signals into a sum of linearly modulated components, we derive a class of reduced-complexity maximum-likelihood (ML) coherent detection and closed-loop phase synchronization schemes. The complexity of the resulting detection schemes, expressed in terms of the number of matched filters and states of a Viterbi algorithm, is significantly reduced with respect to that of optimal coherent receivers with negligible performance loss. This result extends a known one valid for the binary case to multilevel CPM. The proposed synchronization schemes do not require an increased number of matched filters, and are perfectly suitable to be used in conjunction with these receivers. Based on the phase-locked loop (PLL) equivalent linear model, a method for optimizing the parameters of digital second-order PLL's is presented. Numerical examples, based on theoretical analysis and computer simulation, are provided for two specific formats in the CPM class: tamed frequency modulation (TFM) and a quaternary raised-cosine (RC) modulation.

In per-survivor processing (PSP), the number of parameter estimators equals the number of retained hypothetical data sequences (survivors). The authors propose an algorithm which uses an arbitrary number of parameter estimators and compromises between the two extremes of tentative decisions (one parameter estimator) and PSP. Specific applications to reduced state sequence estimation (RSSE) and maximum likelihood sequence estimation (MLSE) with adaptive tracking of a fading channel are considered.

Per-survivor processing (PSP) provides a general framework for the approximation of maximum likelihood sequence estimation (MLSE) algorithms whenever the presence of unknown quantities prevents the precise use of the classical Viterbi algorithm. This principle stems from the idea that data-aided estimation of unknown parameters may be embedded into the structure of the Viterbi algorithm itself. Among the numerous possible applications, the authors concentrate on (a) adaptive MLSE, (b) simultaneous trellis coded modulation (TCM) decoding and phase synchronization, (c) adaptive reduced state sequence estimation (RSSE). As a matter of fact, PSP is interpretable as a generalization of decision feedback techniques of RSSE to decoding in the presence of unknown parameters. A number of algorithms for the simultaneous estimation of data sequence and unknown channel parameters are presented and compared with "conventional" techniques based on the use of tentative decisions. Results for uncoded modulations over interSymbol interference (ISI) fading channels and joint TCM decoding and carrier synchronization are presented. In all cases, it is found that PSP algorithms are clearly more robust than conventional techniques both in tracking a time-varying channel and acquiring its characteristics without training.>

We propose a fully parametric, time-discrete, symbol-spaced simulation model of frequency-selective mobile radio channels. The model encapsulates transmission and receiver filters and a physical channel accounting for both Rayleigh and Rice fading. The simulator allows one to accurately trade between complexity and quality of representation of the propagation channel, making use of a digital filter of variable order to implement the Doppler spectrum of the fading processes. Numerical results assessing the performance of the model in terms of the simulation time and the power spectrum of the fading processes are provided. A communication system with an adaptive receiver is employed to test the reliability of the propagation model. Increasing the degree of accuracy in the approximation of the Doppler spectrum, the performance of the considered receiver, expressed in terms of symbol error rate, rapidly converges to a specific function of the signal to noise ratio.