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In this correspondence, we propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum–Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additional...
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... methods can be developed, but as long as a burst- oriented TDMA access is considered in the GSM system (Fig. 5), a burst-oriented detection is more suitable. Additionally, at the chosen bit rate (270.8 kb/s), multipath propagation as well as Doppler lead to deep fades, which might lead adaptive algorithms to lose channel tracking ...
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... has been evaluated for the GSM system and has been found to be close to that achieved by nonblind schemes. Moreover, a blind receiver based on such algorithms would require no training sequences, and that would imply a 26/156 = 17% increase in the capacity of voice bursts (see Fig. 5). On the other hand, the most important drawback of the algorithms is their high computational burden, although the nonblind reference receiver [14] is rather sophisticated as well. Three trends can be outlined for future work. First, we have a detailed analysis of the computational complexity and convergence rate for the developed ...
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In this paper, we present an algorithm based on the Hidden Markov Models (HMM) theory to solve the problem of blind channel estimation and sequence detection in mobile digital communications. The environment in which the algorithm is tested is the Paneuropean Mobile Radio System, also known as GSM. In this system, a large part in each burst is devo...
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... If we estimate the nominal conditional channel outputs given the current and past inputs v(x i , x i−1 , . . . , x i−L ) [40] [41] instead of the channel response u, we can reduce the ambiguity to a single common phase factor. In our approach, this leads to the factorization shown in Fig. 6 ...
... Clearly, other canonical distributions for µ V k →Ti are possible and might achieve better performance. It is also possible to further constrain the v to be valid outputs for the channel model as mentioned by [40] in the context of Baum-Welch algorithmbased estimation. Because the standard updates for µ Ti→Hi+1 have complexity 2 L+1 for all the structures in Fig. 5, we might consider reduced-complexity updates for the nodes T i in Fig. 5a that lead to algorithms that are like decision-feedback equalization. ...
Iterative algorithms are an attractive approach to approximating optimal, but high-complexity, joint channel estimation and decoding receivers for communication systems. We present a unified approach based on factor graphs for deriving iterative message-passing receiver algorithms for channel estimation and decoding. For many common channels, it is easy to find simple graphical models that lead directly to implementable algorithms. Canonical distributions provide a new, general framework for handling continuous variables. Example receiver designs for Rayleigh fading channels with block or Markov memory, and multipath fading channels with fixed unknown coe#cients illustrate the e#ectiveness of our approach. Keywords--- channel estimation; fading channels; iterative decoding; low-density parity check (LDPC) codes I. Introduction For many communication systems it is well-known that joint demodulation and decoding is required for optimum performance. Typically, this processing is too co...
... The E step is carried out in dcv_fb using the forward-backward recursions of Baum et al [13], [12]. The use of EM for this particular model is also detailed in many papers dealing with telecommunications applications such as [8] or [1]. Note that dcv_em (and all the iterative estimation procedures) returns the sequence of estimated parameters rather than just the parameters estimated during the last iteration. ...
In this paper, we address the problem of blind identification of sparse channels. For this purpose, the ExpectationMaximization is modified to accommodate channel sparsity. The resulting algorithm is applicable for linear and nonlinear channels. Computer simulations on various channel set ups illustrate that the proposed algorithm achieves performance close to the genie-aided estimator.
Many existing multiuser detection algorithms assume that the user sequences are independent and identically distributed (i.i.d.). These algorithms, however, may not be efficient when the user sequences sent to a multiuser system are time correlated due to signal processing procedures such as channel coding. In this paper, we assume that the user sequences are time correlated and can be modeled as first-order, finite-state Markov chains. The proposed algorithm applies the decision feedback framework in which a linear filter based on the maximum target likelihood (MTL) criterion is derived to remove the interferences. A hidden Markov model (HMM) estimator is applied to the output of the MTL filter to estimate the user data, noise variance, and state transition probabilities. The estimated user data in turn are applied to update the parameters of the MTL filter. By exploiting the transmission of training symbols, the proposed algorithm requires neither knowledge of the user codes nor the timing information. Simulation results show the performance improvement of the proposed algorithm by exploiting the time-correlated redundancy of the Markov sources.
In a recent paper, we proposed a new estimation method for the blind deconvolution of a linear system with discrete random input, when the observations may be noise perturbed. We give here asymptotic properties of the estimators in the parametric situation. With nonnoisy observations, the speed of convergence is governed by the $l_1$-tail of the inverse filter, which may have an exponential decrease. With noisy observations, the estimator satisfies a limit theorem with known distribution, which allows for the construction of confidence regions. To our knowledge, this is the first precise asymptotic result in the noisy blind deconvolution problem with an unknown level of noise. We also extend results concerning Hankel’s estimation to Toeplitz’s estimation and prove a formula to compute Toeplitz forms that may have interest in itself.
Modulation classification is to identify the modulation type of a received signal automatically. It is a promising technique that can increase the throughput of adaptive modulation systems such as IEEE 802.11 and HIPERLAN systems. While a variety of approaches have been proposed for modulation classification, most of them rely on the unrealistic assumption that the channel is additive white Gaussian noise channel. This paper presents an algorithm using hybrid likelihood ratio test for BPSK and QPSK classification in multipath fading environment. The unknown channel coefficients are first estimated blindly using the EM algorithm. The estimates are then used in likelihood ratio test for classification. Simulations show that the proposed classifier has high accuracy and is superior to the classifier that does not take the channel distortion into account. An added advantage of the proposed algorithm is that the maximum likelihood estimate of the channel coefficients can be used in equalization and data demodulation
We propose an iterative solution to the problem of blindly and
jointly identifying the channel response and transmitted symbols in a
digital communications system. The proposed algorithm iterates between a
symbol estimator, which uses tentative channel estimates to provide soft
symbol estimates, and a channel estimator, which uses the symbol
estimates to improve the channel estimates. The proposed algorithm
shares some similarities with the expectation-maximization (EM)
algorithm but with lower complexity and better convergence properties.
Specifically, the complexity of the proposed scheme is linear in the
memory of the equalizer, and it avoids most of the local maxima that
trap the EM algorithm
In this paper, a blind algorithm for detecting active users in a
DS-CDMA system is presented. This probabilistic algorithm relies on the
theory of hidden Markov models (HMM) and is completely blind in the
sense that no knowledge of the signature sequences, channel state
information or training sequences is required for any user.
Additionally, observation through an array of sensors is also
considered. Performance is verified via computer simulations, showing
the near-far resistance of the analyzed procedure
We present a semi-blind approach to combined detection and turbo decoding for unknown ISI channels. The proposed method utilizes very few training symbols and minimizes the probability of ill-convergence characteristic of full-blind approaches, even for long ISI channels. Furthermore, the semi-blind approach leads to an improved bit error rate performance and a decrease in the overall computational burden when compared with full blind schemes. The reduction in computational complexity increases with the ISI channel length.
The theory of hidden Markov models (HMM) is applied to the problem
of blind (without training sequences) channel estimation and data
detection. Within a HMM framework, the Baum-Welch (1970) identification
algorithm is frequently used to find out maximum-likelihood (ML)
estimates of the corresponding model. However, such a procedure assumes
the model (i.e., the channel response) to be static throughout the
observation sequence. By means of introducing a parametric model for
time-varying channel responses, a version of the algorithm, which is
more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)]
is derived. Aiming to compare algorithm behavior, a set of computer
simulations for a GSM scenario is provided. Results indicate that, in
comparison to other Baum-Welch (BW) versions of the algorithm, the TDBW
approach attains a remarkable enhancement in performance. For that
purpose, only a moderate increase in computational complexity is needed
