Results 1  10
of
87
PerSurvivor Processing: A General Approach to MLSE in Uncertain Environments
 IEEE Trans. Commun
, 1995
"... PerSurvivor 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 dataaided est ..."
Abstract

Cited by 131 (12 self)
 Add to MetaCart
PerSurvivor 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 dataaided estimation of unknown parameters may be embedded into the structure of the Viterbi algorithm itself. Among the numerous possible applications, we concentrate here 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 andunknown channel parameters are presented and compared with "conventional" techniques based on the use of tentative decisions. Results for uncoded modu...
Multichannel Blind Identification: From Subspace to Maximum Likelihood Methods
 Proc. IEEE
, 1998
"... this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified ..."
Abstract

Cited by 79 (2 self)
 Add to MetaCart
this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified into the momentbased and the maximum likelihood (ML) methods. We further divide these algorithms based on the modeling of the input signal. If input is assumed to be random with prescribed statistics (or distributions), the corresponding blind channel estimation schemes are considered to be statistical. On the other hand, if the source does not have a statistical description, or although the source is random but the statistical properties of the source are not exploited, the corresponding estimation algorithms are classified as deterministic. Fig. 2 shows a map for different classes of algorithms and the organization of the paper.
Noncoherent sequence detection
 IEEE TRANS. COMMUN
, 1999
"... In this paper, 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 rando ..."
Abstract

Cited by 46 (18 self)
 Add to MetaCart
In this paper, 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, 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 timevarying phase models. The proposed schemes compare favorably with other solutions previously proposed in the technical literature.
Equalization Concepts for EDGE
 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
, 1999
"... In this paper, an equalization concept for the novel radio access scheme EDGE (Enhanced Data Rates for GSM Evolution) is proposed, by which high performance can be obtained at moderate computational complexity. Because highlevel modulation is employed in EDGE, optimum equalization as usually perfor ..."
Abstract

Cited by 25 (9 self)
 Add to MetaCart
In this paper, an equalization concept for the novel radio access scheme EDGE (Enhanced Data Rates for GSM Evolution) is proposed, by which high performance can be obtained at moderate computational complexity. Because highlevel modulation is employed in EDGE, optimum equalization as usually performed in GSM (Global System for Mobile Communications) receivers is too complex, and suboptimum schemes have to be considered. It is shown that delayed decisionfeedback sequence estimation (DDFSE) and reducedstate sequence estimation (RSSE) are promising candidates. For various channel profiles, approximations for the bit error rate of these suboptimum equalization techniques are given and compared with simulation results for DDFSE. It turns out that a discretetime prefilter creating a minimumphase overall impulse response is indispensible for a favourable tradeoff between performance and complexity. Additionally, the influence of channel estimation and of the receiver input filter is investigated, and the reasons for performance degradation compared to the additive white Gaussian noise channel are indicated. Finally, the overall system performance attainable with the proposed equalization concept is determined for transmission with channel coding.
Reducedstate BCJRtype algorithms
 in Proc. IEEE Int. Conf. Communications
, 2000
"... Abstract—In this paper, we propose a technique to reduce the number of trellis states in BCJRtype algorithms, i.e., algorithms with a structure similar to that of the wellknown algorithm by Bahl, Cocke, Jelinek, and Raviv (BCJR). This work is inspired by reducedstate sequence detection (RSSD). Th ..."
Abstract

Cited by 25 (9 self)
 Add to MetaCart
Abstract—In this paper, we propose a technique to reduce the number of trellis states in BCJRtype algorithms, i.e., algorithms with a structure similar to that of the wellknown algorithm by Bahl, Cocke, Jelinek, and Raviv (BCJR). This work is inspired by reducedstate sequence detection (RSSD). The key idea is the construction, during one of the recursions in the reducedstate 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 recently proposed by the authors; and 3) detection based on linear prediction for Rayleigh fading channels. As in classical RSSD, the proposed algorithm allows significant statecomplexity reduction with limited performance degradation. Index Terms—Error correcting codes, iterative decoding, softinput softoutput, turbo codes. I.
The Viterbi algorithm and Markov noise memory
 IEEE Trans. Inform. Theory
, 2000
"... Abstract—This work designs sequence detectors for channels with intersymbol interference (ISI) and correlated (and/or signaldependent) noise. We describe three major contributions. i) First, by modeling the noise as a finiteorder Markov process, we derive the optimal maximumlikelihood sequence de ..."
Abstract

Cited by 20 (4 self)
 Add to MetaCart
Abstract—This work designs sequence detectors for channels with intersymbol interference (ISI) and correlated (and/or signaldependent) noise. We describe three major contributions. i) First, by modeling the noise as a finiteorder Markov process, we derive the optimal maximumlikelihood sequence detector (MLSD) and the optimal maximum a posteriori (MAP) sequence detector, extending to the correlated noise case the Viterbi algorithm. We show that, when the signaldependent noise is conditionally Gauss–Markov, the branch metrics in the MLSD are computed from the conditional secondorder noise statistics. We evaluate the branch metrics using a bank of finite impulse response (FIR) filters. ii) Second, we characterize the error performance of the MLSD and MAP sequence detector. The error analysis of these detectors is complicated by the correlation asymmetry of the channel noise. We derive upper and lower bounds and computationally efficient approximations to these bounds based on the banded structure of the inverses of Gauss–Markov covariance matrices. An experimental study shows the tightness of these bounds. iii) Finally, we derive several classes of suboptimal sequence detectors, and demonstrate how these and others available in the literature relate to the MLSD. We compare their error rate performance and their relative computational complexity, and show how the structure of the MLSD and the performance evaluation guide us in choosing a best compromise between several types of suboptimal sequence detectors. Index Terms—Correlated noise, Gauss–Markov processes, intersymbol
On the application of factor graphs and the sumproduct algorithm to ISI channels
 IEEE Trans. Commun
, 2005
"... Abstract—In this paper, based on the application of the sum–product (SP) algorithm to factor graphs (FGs) representing the joint a posteriori probability (APP) of the transmitted symbols, we propose new iterative softinput softoutput (SISO) detection schemes for intersymbol interference (ISI) chan ..."
Abstract

Cited by 16 (4 self)
 Add to MetaCart
Abstract—In this paper, based on the application of the sum–product (SP) algorithm to factor graphs (FGs) representing the joint a posteriori probability (APP) of the transmitted symbols, we propose new iterative softinput softoutput (SISO) detection schemes for intersymbol interference (ISI) channels. We have verified by computer simulations that the SP algorithm converges to a good approximation of the exact marginal APPs of the transmitted symbols if the FG has girth at least 6. For ISI channels whose corresponding FG has girth 4, the application of a stretching technique allows us to obtain an equivalent girth6 graph. For sparse ISI channels, the proposed algorithms have advantages in terms of complexity over optimal detection schemes based on the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm. They also allow a parallel implementation of the receiver and the possibility of a more efficient complexity reduction. The application to joint detection and decoding of lowdensity paritycheck (LDPC) codes is also considered and results are shown for some partialresponse magnetic channels. Also in these cases, we show that the proposed algorithms have a limited performance loss with respect to that can be obtained when the optimal “serial ” BCJR algorithm is used for detection. Therefore, for their parallel implementation, they represent a favorable alternative to the modified “parallel ” BCJR algorithm proposed in the literature for the application to magnetic channels. Index Terms—Factor graphs, intersymbol interference (ISI) channels, iterative detection, lowdensity paritycheck (LDPC) codes, partialresponse channels, sum–product (SP) algorithm. I.
Capacity, mutual information, and coding for finitestate Markov channels
 IEEE Trans. Inform. Theory
, 1996
"... Abstract The FiniteState Markov Channel (FSMC) is a discretetime varying channel whose variation is determined by a finitestate Markov process. These channels have memory due to the Markov channel variation. We obtain the FSMC capacity as a function of the conditional channel state probability. W ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
Abstract The FiniteState Markov Channel (FSMC) is a discretetime varying channel whose variation is determined by a finitestate Markov process. These channels have memory due to the Markov channel variation. We obtain the FSMC capacity as a function of the conditional channel state probability. We also show that for i.i.d. channel inputs, this conditional probability converges weakly, and the channel's mutual information is then a closedform continuous function of the input distribution. We next consider coding for FSMCs. In general, the complexity of maximumlikelihood decoding grows exponentially with the channel memory length. Therefore, in practice, interleaving and memoryless channel codes are used. This technique results in some performance loss relative to the inherent capacity of channels with memory. We propose a maximumlikelihood decisionfeedback decoder with complexity that is independent of the channel memory. We calculate the capacity and cutoff rate of our technique, and show that it preserves the capacity of certain FSMCs. We also compare the performance of the decisionfeedback decoder with that of interleaving and memoryless channel coding on a fading channel with 4PSK modulation.
Channel Estimation with Superimposed Pilot Sequence
 in Proc. Advanced Signal Processing for Communications Symposium in conjunction with IEEE GLOBECOM '99, Rio de Janeiro
, 1999
"... For the purpose of various synchronization tasks (including carrier phase, time, frequency, and frame synchronization) , one may add a known pilot sequence, typically a pseudonoise sequence, to the unknown data sequence. This approach is known as a spreadspectrum pilot technique or as a superimpos ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
For the purpose of various synchronization tasks (including carrier phase, time, frequency, and frame synchronization) , one may add a known pilot sequence, typically a pseudonoise sequence, to the unknown data sequence. This approach is known as a spreadspectrum pilot technique or as a superimposed pilot sequence technique.
The structure and design of realizable decision feedback equalizers for IIR channels with coloured noise
, 1990
"... A simple algorithm for optimizing decision feedback equalizers by minimizing the mean square error (MSE) is presented. A complex baseband channel and correct past decisions are assumed. The dispersive channel may have infinite impulse response and the noise may be coloured. We consider optimal reali ..."
Abstract

Cited by 14 (10 self)
 Add to MetaCart
A simple algorithm for optimizing decision feedback equalizers by minimizing the mean square error (MSE) is presented. A complex baseband channel and correct past decisions are assumed. The dispersive channel may have infinite impulse response and the noise may be coloured. We consider optimal realizable (stable and finitelag smoothing) forward and feedback filters in discrete time. They are parametrized as recursive filters. In the special case of transmission channels with finite impulse response and autoregressive noise, the minimum MSE can be attained with transversal feedback and forward filters. In general, the forward part should include a noisewhitening filter (the inverse noise model). The finite realizations of the filters are calculated using a polynomial equation approach to the linear quadratic optimization problem. The equalizer is optimized essentially by solving a system of linear equations Ax = B, where A contains transfer function coefficients from the channel and ...