Performance Of Maximum-likelihood Sequence Estimation Research Articles

Improving performance of MLSE in RSOA-based WDM-PON by partial response signaling

High data-rate upstream transmission in wavelength division multiplexed passive optical network (WDM-PON) based on reflective semiconductor optical amplifier (RSOA) is limited by severe inter-symbol interference (ISI), owing to low-bandwidth transmitter and fiber dispersion. To overcome the limitations in RSOA-based WDM-PON, we propose a novel receiver based on partial response maximum likelihood (PRML) equalization which combines the use of partial response (PR) signaling with maximum likelihood sequence estimation (MLSE). MLSE has been long considered as the optimal reception technique to overcome various types of impairment in optical transmission such as dispersions. It is demonstrated in this paper that PRML surpasses standard MLSE in ISI and reflection suppression with reduced complexity for RSOA-based WDM-PON system. 150 km unidirectional distance is demonstrated for 10 Gb/s uplink, while bidirectional uplinks up to 50 km and 20 km are achieved for data rate of 10 Gb/s and 20 Gb/s respectively, in WDM-PON using PRML. Furthermore, the impacts of discrete reflections on various equalization techniques are investigated, where PRML also shows superiority over MLSE.

Performance of an MLSE-EDC Receiver With SOA-Induced Nonlinear Impairments

We experimentally investigate the performance of maximum-likelihood sequence estimation (MLSE) electronic dispersion compensation for signals impaired by nonlinear optical effects induced in a semiconductor optical amplifier (SOA). Single- and multichannel effects are investigated. We study 10.7-Gb/s nonreturn-to-zero, low-pass filter duobinary, and differential phase-shift-keying signals detected as optical duobinary, each with different amounts of uncompensated dispersion before amplification by the SOA and detection. The results show performance variability by the MLSE receiver with increasing nonlinearity, from small penalties back-to-back compared to a standard receiver for most formats to advantages of several decibels with uncompensated dispersion.

Practical reduced-complexity equaliser for EDGE

EDGE uses the 8PSK modulation scheme. It is difficult to employ optimum equalisation due to the high computation complexity required. Consequently, a sub-optimum technique offering an approximation to maximum likelihood sequence estimation performance is required. A simple, reduced complexity fractional spaced equaliser structure involving the delayed decision feedback sequence estimation is therefore presented. The fractional spaced approach does not require additional pre-processing creating a minimum phase overall channel impulse. It is suitable for unknown channels and has near optimum performance.

Performance bounds for cochannel interference cancellation within the current GSM standard

The performance of maximum-likelihood sequence estimation (MLSE) is bounded (and often approximated well) by the matched filter bound (MFB). In this paper, we first show how the linearized GMSK modulation of GSM can be reformulated to have a real symbol constellation, leading to a two-channel aspect due to the in-phase (I) and in-quadrature (Q) components of the received signal. More channels can be added by oversampling and/or the use of multiple sensors (antennas, polarizations). Given this model, we present the MFB for MLSE single-user detection in the presence of interferers that are modeled as colored Gaussian noise. The MLSE is assumed to employ noise correlation information that in general may differ from the true one (e.g. by simplifying spatio-temporal correlations to spatial correlations only). One of the conclusions is that properly taking into account the spatio-temporal correlation of the interference leads to much improved performance (MFB) compared to purely spatial approaches. Another conclusion is that the excess bandwidth in GMSK provides some useful channel diversity.

Iterative equalization with adaptive soft feedback

In this letter, a novel equalization algorithm applying soft-decision feedback and designed for binary transmission is introduced. In contrast to conventional decision-feedback equalization (DFE), iterations are necessary, because a simple matched filter serves as feedforward filter, which collects signal energy, but creates noncausal intersymbol interference. The rule for generating soft decisions is adapted continuously to the current state of the algorithm. In most cases, standard DFE methods are clearly outperformed. For a class of certain channel impulse responses, performance of maximum-likelihood sequence estimation is attained, in principle. The high performance of the scheme is explained using results from neural network theory.

Adaptive MLSE performance on the D-AMPS 1900 channel

Maximum-likelihood sequence-estimation (MLSE) performance on the D-AMPS 1900 channel is evaluated through simulations. A channel-tracking algorithm of least-mean-squares (LMS) complexity is used in conjunction with the MLSE. It is shown that the receiver specification points for D-AMPS 1900 are met and that performance can be further enhanced with fractionally spaced equalization and/or receiver-antenna diversity combining.

A neural network for detection of signals in communication

An architecture of densely connected compact neural networks is presented for the maximum-likelihood sequence estimation (MLSE) of signals in digital communications. The combinatorial minimization of the detection cost Is performed through the optimization of a concave Lyapunov function associated with the network, and truly paralleled operations can be achieved via the collective computational behaviors. In addition, the MLSE performance can be improved by a paralleled annealing technique which has been developed for obtaining optimal or near-optimal solutions in high-speed, real-time applications. Given a sequence of length n, the network of complexity and throughput rate are O(L) and n/T/sub c/, respectively, where L is the number of symbols the inference spans and T/sub c/ is the convergence time. The hardware architecture as well as network models, neuron models, and methods of feeding the input to the network are addressed in terms of the probability of error. Through the simulations, it is demonstrated that the proposed compact neural network approach is an efficient method of realizing the MLSE receiver.

Reduced-state sequence estimation for coded modulation of intersymbol interference channels

The authors investigated the detection of trellis codes designed for channels that are intersymbol interference free when they operate in the presence of intersymbol interference. A well-structured reduced-state sequence estimation (RSSE) algorithm is described which can achieve the performance of maximum-likelihood sequence estimation (MLSE) with drastically reduced complexity. Well-defined reduced-state trellises are first constructed by merging the states of the ML supertrellis using set partitioning principles. Then the Viterbi algorithm is used to search these trellises. A special case of RSSE, called parallel decision-feedback decoding, uses the encoder trellis, yet on channels with large attenuation distortion it can provide a significantly better performance than linear equalization. The performance of RSSE is examined analytically and through simulation, and then compared to that of MLSE and ideal decision-feedback equalization. It is noted that the performance advantage of RSSE can be obtained without significantly increasing the decoding delay or complicating an adaptive implementation. >

Reduced-state sequence estimation with set partitioning and decision feedback

A reduced-state sequence estimator for linear intersymbol interference channels is described. The estimator uses a conventional Viterbi algorithm with decision feedback to search a reduced-state subset trellis that is constructed using set-partitioning principles. The complexity of maximum-likelihood sequence estimation (MLSE) due to the length of the channel memory and the size of the signal set is systematically reduced. An error probability analysis shows that a good performance/complexity tradeoff can be obtained. In particular, the results indicate that the required complexity to achieve the performance of MLSE is independent of the size of the signal set for large enough signal sets. Simulation results are provided for two partial-response systems. A simple technique for quadrature partial-response signaling (QPRS) is described that eliminates the quasicatastrophic nature of the ML trellis.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>