Joint Decoding Research Articles

Multi-Pattern Viterbi Algorithm for joint decoding of multiple speech patterns

Joint decoding of multiple speech patterns so as to improve speech recognition performance is important, especially in the presence of noise. In this paper, we propose a Multi-Pattern Viterbi algorithm (MPVA) to jointly decode and recognize multiple speech patterns for automatic speech recognition (ASR). The MPVA is a generalization of the Viterbi Algorithm to jointly decode multiple patterns given a Hidden Markov Model (HMM). Unlike the previously proposed two stage Constrained Multi-Pattern Viterbi Algorithm (CMPVA), the MPVA is a single stage algorithm. MPVA has the advantage that it can be extended to connected word recognition (CWR) and continuous speech recognition (CSR) problems. MPVA is shown to provide better speech recognition performance than the earlier techniques: using only two repetitions of noisy speech patterns (−5 dB SNR, 10% burst noise), the word error rate using MPVA decreased by 28.5%, when compared to using individual decoding.

Joint Decoding of Unequally Protected JPEG2000 Bitstreams and Reed-Solomon Codes

In this paper we present joint decoding of JPEG2000 bitstreams and Reed-Solomon codes in the context of unequal loss protection. Using error resilience features of JPEG2000 bitstreams, the joint decoder helps to restore the erased symbols when the Reed-Solomon decoder fails to retrieve them on its own. However, the joint decoding process might become time-consuming due to a search through the set of possible erased symbols. We propose the use of smaller codeblocks and transmission of a relatively small amount of side information with high reliability as two approaches to accelerate the joint decoding process. The accelerated joint decoder can deliver essentially the same quality enhancement as the nonaccelerated one, while operating several times faster.

Information theoretic capacity of cellular multiple access channel with shadow fading

In this paper, we extend the well-known model for the Gaussian Cellular Multiple Access Channel originally presented by Wyner. The first extension to the model incorporates the distance-dependent path loss (maintaining a close relevance to path loss values in real world cellular systems) experienced by the users distributed in a planar cellular array. The density of base stations and hence the cell sizes are variable. In the context of a Hyper-receiver joint decoder, an expression for the information theoretic capacity is obtained assuming a large number of users in each cell. The model is further extended to incorporate the log-normal shadow fading variations, ensuring that the shadowing models are fairly comparable to the free space model. Using these fair models the effect of the shadow fading standard deviation on the information theoretic capacity of the cellular system is quantified. It is observed that a higher standard deviation results in lower capacity if the mean path loss is appropriately adjusted in order to model the mean loss due to the physical obstacles causing the shadow fading. The results validate that larger cell sizes and a higher standard deviation of shadowing (with appropriately adjusted mean path loss) results in lower spectral efficiency.

Error-Resilient and Low-Complexity Onboard Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding

In this paper, we propose a lossless compression algorithm for hyperspectral images inspired by the distributed-source-coding (DSC) principle. DSC refers to separate compression and joint decoding of correlated sources, which are taken as adjacent bands of a hyperspectral image. This concept is used to design a compression scheme that provides error resilience, very low complexity, and good compression performance. These features are obtained employing scalar coset codes to encode the current band at a rate that depends on its correlation with the previous band, without encoding the prediction error. Iterative decoding employs the decoded version of the previous band as side information and uses a cyclic redundancy code to verify correct reconstruction. We develop three algorithms based on this paradigm, which provide different tradeoffs between compression performance, error resilience, and complexity. Their performance is evaluated on raw and calibrated AVIRIS images and compared with several existing algorithms. Preliminary results of a field-programmable gate array implementation are also provided, which show that the proposed algorithms can sustain an extremely high throughput.

Phase ambiguity diminishing space-time trellis codes

Phase ambiguities affect any joint decoding and channel estimation algorithm over fading channels, making codes that mitigate this problem of considerable interest. This work presents a design that transforms a Space-Time Trellis Code (STTC), prone to carrier phase ambiguities, into a Phase Ambiguity Diminishing STTC (PAD-STTC) that is robust to such impairment. A PAD-STTC can be considered as a Multiple STTC (MSTTC) with special constellation mappings. The design criteria for these mappings are developed in this work by trading among the PAD property, diversity, and coding gain. Two PAD-STTC structures termed PAD1-STTC and PAD2-STTC are proposed, where PAD2-STTC increases the number of states over the constituent STTC. Computer simulation results indicate that PAD-STTCs can solve the phase ambiguity problem and provide a larger diversity gain than the constituent STTC over high Doppler fading channels. Moreover, with the same constituent STTC, the PAD2-STTC outperforms the PAD1-STTC at the expense of increased complexity.

Channel Modeling and Signal Processing for Probe Storage Channels

Probe-storage devices employ large arrays of probes to write/read data in parallel in some storage medium, and combine ultra-high density, low access times, and low power consumption. A particular probe-storage technique utilizes thermomechanical means to store and retrieve information in thin polymer films. In this paper, a system-level channel model for the thermomechanical probe-storage channel is presented. Each of the components of the proposed model is derived by extensive characterization of experimentally obtained readback signals from probe recording tests. Moreover, detection techniques that are actually utilized in a probe-storage prototype implementation are described, followed by coding techniques for added reliability in the presence of particles or other impurities of the storage medium. In addition to low-complexity coding constructs, a concatenated coding scheme with an outer LDPC and inner modulation code is considered, in order to establish a benchmark for overall system performance. A novel methodology for joint decoding of outer LDPC and inner (d,k) modulation codes is developed. Furthermore, an optimal soft decoder for the modulation code is proposed, based on a modification of the decoder metrics to accurately account for the probe storage channel output statistics. Experimental results are used throughout the paper to validate the channel model and identify its relevant parameters, as well as to verify the system performance obtained by simulations.

Analogue transmission over a two-hop gaussian cascade network

We investigate lossy transmission of two intercorrelated sources over a two-hop cascade network. A low delay analogue based joint source-channel coding scheme is proposed for the case of jointly Gaussian sources over channels with additive white Gaussian noise (AWGN), subject to a quadratic distortion constraint. For the first hop, uncoded transmission is used, with an optimal minimum mean-squared error (MMSE) decoder at the relay node that utilizes the other source as side information. For the second hop, we first perform decorrelation using Karhunen-Loeve transform, followed by dimension reduction mappings. Simulation results show that relative to the optimal performance theoretically attainable (OPTA), uncoded transmission with the optimal MMSE joint decoder for the first hop is able perform close to OPTA when the inter-correlation is relatively weak. For the second hop, result of the proposed scheme is able to come within 1.5 dB from OPTA.

A message-passing approach for joint channel estimation, interference mitigation, and decoding

Channel uncertainty and co-channel interference are two major challenges in the design of wireless systems such as future generation cellular networks. This paper studies receiver design for a wireless channel model with both time-varying Rayleigh fading and strong co-channel interference of similar form as the desired signal. It is assumed that the channel coefficients of the desired signal can be estimated through the use of pilots, whereas no pilot for the interference signal is available, as is the case in many practical wireless systems. Because the interference process is non-Gaussian, treating it as Gaussian noise generally often leads to unacceptable performance. In order to exploit the statistics of the interference and correlated fading in time, an iterative message-passing architecture is proposed for joint channel estimation, interference mitigation and decoding. Each message takes the form of a mixture of Gaussian densities where the number of components is limited so that the overall complexity of the receiver is constant per symbol regardless of the frame and code lengths. Simulation of both coded and uncoded systems shows that the receiver performs significantly better than conventional receivers with linear channel estimation, and is robust with respect to mismatch in the assumed fading model.

Approach to Chinese Word Segmentation Based on Character-Word Joint Decoding

近年来基于字的方法极大地提高了中文分词的性能,借助于优秀的学习算法,由字构词逐渐成为中文分词的主要技术路线.然而,基于字的方法虽然在发现未登录词方面有其优势,却往往在针对表内词的切分效果方面不及基于词的方法,而且还损失了一些词与词之间的信息以及词本身的信息.在此基础上,提出了一种结合基于字的条件随机场模型与基于词的Bi-gram语言模型的切分策略,实现了字词联合解码的中文分词方法,较好地发挥了两个模型的长处,能够有效地改善单一模型的性能,并在SIGHAN Bakeoff3的评测集上得到了验证,充分说明了合理的字词结合方法将有效地提高分词系统的性能,可以更好地应用于中文信息处理的各个方面.;The performance of Chinese word segmentation has been greatly improved by character-based approaches in recent years. With the help of powerful machine learning strategies, the words extraction via combination of characters becomes the focus in Chinese word segmentation researches. In spite of the outstanding capability of discovering out-of-vocabulary words, the character-based approaches are not as good as word-based approaches in in-vocabulary words segmentation with some internal and external information of the words lost. In this paper we propose a joint decoding strategy that combines the character-based conditional random field model and word-based Bi-gram language model, for segmenting Chinese character sequences. The experimental results demonstrate the good performance of our approach, and prove that two sub models are well integrated as the joint model of character and word could more effectively enhance the performance of Chinese word segmentation systems than any of the single model, thus is fit for many applications in Chinese information processing.

A simplified MMSE-based iterative receiver for MIMO systems

A simplified minimum mean square error (MMSE) detector is proposed for joint detection and decoding of multiple-input multiple-output (MIMO) systems. The matrix inversion lemma and the singular value decomposition (SVD) of the channel matrix are used to simplify the computation of the coefficient of the MMSE filter. Compared to the original MMSE detector, the proposed detector has a much lower computational complexity with only a marginal performance loss. The proposed detector can also be applied to MIMO systems with high order modulations.

Securing OFDM over Wireless Time-Varying Channels Using Subcarrier Overloading with Joint Signal Constellations

A method of overloading subcarriers by multiple transmitters to secure OFDM in wireless time-varying channels is proposed and analyzed. The method is based on reverse piloting, superposition modulation, and joint decoding. It makes use of channel randomness, reciprocity, and fast decorrelation in space to secure OFDM with low overheads on encryption, decryption, and key distribution. These properties make it a good alternative to traditional software-based information security algorithms in systems where the costs associated with such algorithms are an implementation obstacle. A necessary and sufficient condition for achieving information theoretic security in accordance with channel and system parameters is derived. Security by complexity is assessed for cases where the condition for information theoretic security is not satisfied. In addition, practical means for implementing the method are derived including generating robust joint constellations, decoding data with low complexity, and mitigating the effects of imperfections due to mobility, power control errors, and synchronization errors.

A compressed sensing receiver for UWB impulse radio in bursty applications like wireless sensor networks

We propose a novel receiver for Ultra-Wide-band Impulse-Radio communication in Wireless Sensor Networks, which are characterized by bursty traffic and severe power constraints. The receiver is based on the principle of Compressed Sensing, and exploits the sparsity of the transmitted signal to achieve reliable demodulation from a relatively small number of projections. The projections are implemented in an analog front-end as correlations with tractable test-functions, and a joint decoding of the time of arrival and the data bits is done by a DSP back-end using an efficient quadratic program. The proposed receiver differs from extant schemes in the following respects: (i) It needs neither a high-rate analog-to-digital converter nor wide-band analog delay lines, and can operate in a significantly under-sampled regime. (ii) It is robust to large timing uncertainty and hence the transmitter need not waster power on explicit training headers for timing synchronization. (iii) It can operate in a regime of heavy inter-symbol interference (ISI), and therefore allows a very high baud rate (close to the Nyquist rate). (iv) It has a built-in capability to blindly acquire and track the channel response irrespective of line-of-sight/non-line-of-sight conditions. We demonstrate that the receiver’s performance remains close to the maximum likelihood receiver under every scenario of under-sampling, timing uncertainty, ISI, and channel delay spread.

A low-complexity iterative channel estimation and detection technique for doubly selective channels

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

Real-valued maximum likelihood decoder for quasi-orthogonal space-time block codes

In this letter, we propose a low complexity Maximum Likelihood (ML) decoding algorithm for quasi-orthogonal space-time block codes (QOSTBCs) based on the real-valued lattice representation and QR decomposition. We show that for a system with rate r = ns/T, where ns is the number of transmitted symbols per T time slots; the proposed algorithm decomposes the original complex-valued system into a parallel system with ns 2 times 2 real-valued components, thus allowing for a simple joint decoding of two real symbols. For a square QAM constellation with L points (L-QAM), this algorithm achieves full diversity by properly incorporating two-dimensional rotation using the optimal rotation angle and the same rotating matrix for any number of transmit antennas (N ges 4). We show that the complexity gain becomes greater when N or L becomes larger. The complexity of the proposed algorithm is shown to be linear with the number of transmitted symbols ns.

Jointly Decoded Raptor Codes: Analysis and Design for the BIAWGN Channel

We are interested in the analysis and optimization of Raptor codes under a joint decoding framework, that is, when the precode and the fountain code exchange soft information iteratively. We develop an analytical asymptotic convergence analysis of the joint decoder, derive an optimization method for the design of efficient output degree distributions, and show that the new optimized distributions outperform the existing ones, both at long and moderate lengths. We also show that jointly decoded Raptor codes are robust to channel variation: they perform reasonably well over a wide range of channel capacities. This robustness property was already known for the erasure channel but not for the Gaussian channel. Finally, we discuss some finite length code design issues. Contrary to what is commonly believed, we show by simulations that using a relatively low rate for the precode, we can improve greatly the error floor performance of the Raptor code.

Multiterminal source coding for multiview images under wireless fading channels

This paper addresses the problem of wireless transmission of a captured scene from multiple cameras, which do not communicate among each other, to a joint decoder. Correlation among different camera views calls for distributed source coding for efficient multiview image compression. The fact that cameras are placed within a short range of each other results in a high level of interference, multipath fading, and noise effects during communications. We develop a novel two-camera system, that employs multiterminal source coding and complete complementary data spreading, so that while the former technique exploits the statistical correlation between camera views, and performs joint compression to reduce transmission rates, the spreading technique will protect transmitted data by mitigating the effects of wireless fading channels. Our results indicate that the proposed system is competitive when compared to two independently JPEG encoded streams at low to medium transmission rates.

On the multicell processing capacity of the cellular MIMO uplink channel in correlated rayleigh fading environment

In the context of cellular systems, it has been shown that multicell processing can eliminate inter-cell interference and provide high spectral efficiencies with respect to traditional interference-limited implementations. Moreover, it has been proved that the multiplexing sum-rate capacity gain of multicell processing systems is proportional to the number of base station (BS) antennas. These results have been also established for cellular systems, where BSs and user terminals (UTs) are equipped with multiple antennas. Nevertheless, a common simplifying assumption in the literature is the uncorrelated nature of the Rayleigh fading coefficients within the BSUT MIMO links. In this direction, this paper investigates the ergodic multicell-processing sum-rate capacity of the Gaussian MIMO cellular multiple-access channel in a correlated fading environment. More specifically, the multiple antennas of both BSs and UTs are assumed to be correlated according to the Kronecker product model. Furthermore, the current system model considers Rayleigh fading, uniformly distributed UTs over a planar coverage area and power-law path loss. Based on free probabilistic arguments, the empirical eigenvalue distribution of the channel covariance matrix is derived and it is used to calculate both optimal joint decoding and minimum mean square error (MMSE) filtering capacity. In addition, numerical results are presented, where the per-cell sum-rate capacity is evaluated while varying the cell density of the system, as well as the level of fading correlation. In this context, it is shown that the capacity performance is greatly compromised by BS-side correlation, whereas UT-side correlation has a negligible effect on the system's performance. Furthermore, MMSE performance is shown to be greatly suboptimal but more resilient to fading correlation in comparison to optimal decoding.

Distributed Arithmetic Coding for the Slepian–Wolf Problem

Distributed source coding schemes are typically based on the use of channels codes as source codes. In this paper we propose a new paradigm, named ldquodistributed arithmetic coding,rdquo which extends arithmetic codes to the distributed case employing sequential decoding aided by the side information. In particular, we introduce a distributed binary arithmetic coder for the Slepian-Wolf coding problem, along with a joint decoder. The proposed scheme can be applied to two sources in both the asymmetric mode, wherein one source acts as side information, and the symmetric mode, wherein both sources are coded with ambiguity, at any combination of achievable rates. Distributed arithmetic coding provides several advantages over existing Slepian-Wolf coders, especially good performance at small block lengths, and the ability to incorporate arbitrary source models in the encoding process, e.g., context-based statistical models, in much the same way as a classical arithmetic coder. We have compared the performance of distributed arithmetic coding with turbo codes and low-density parity-check codes, and found that the proposed approach is very competitive.

Iterative Joint Detection, Decoding, and Channel Estimation in Turbo-Coded MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of a list detector, a turbo decoder, and a channel estimator that is based on the space-alternating generalized expectation-maximization (SAGE) algorithm. This paper proposes a way to improve the convergence of the iterative detection and decoding by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. A new list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal losses of performance. Furthermore, the organization of spectrally efficient decision-directed (DD) SAGE channel estimation under a constrained number of detector-decoder iterations is optimized by computer simulations, and the SAGE algorithm itself is modified for nonconstant envelope constellations. The list recalculation is shown to improve convergence. It is also shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. Despite the low preamble density and fast-fading channel, the proposed iterative receiver shows robust performance.

A Variational Inference Framework for Soft-In Soft-Out Detection in Multiple-Access Channels

We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed framework may be used in multiple-access interference (MAI), intersymbol interference (ISI), and multiple-input multiple-output (MIMO) channels. Without loss of generality, we will focus our attention on turbo multiuser detection, to facilitate a more concrete discussion. It is shown that, with some loss of optimality, variational inference avoids the exponential complexity of a posteriori probability (APP) detection by optimizing a closely related, but much more manageable, objective function called variational free energy. In addition to its systematic appeal, there are several other advantages to this viewpoint. First of all, it provides unified and rigorous justifications for numerous detectors that were proposed on radically different grounds, and facilitates convenient joint detection and decoding (utilizing the turbo principle) when error-control codes are incorporated. Second, efficient joint parameter estimation and data detection is possible via the variational expectation maximization (EM) algorithm, such that the detrimental effect of inaccurate channel knowledge at the receiver may be dealt with systematically. We are also able to extend BPSK-based SISO detection schemes to arbitrary square QAM constellations in a rigorous manner using a variational argument.