Joint Decoding Research Articles

Non-Linear and Non-Iterative Based Transceiver Design for SU-MIMO Systems

This paper considers the design of a low-complexity and high-performance precoder for multiple-input multiple-output (MIMO) systems. The precoder is designed by combining both nonlinear and non-iterative processing strategies. The proposed nonlinear precoding techniques employ a nonlinear constellation precoding technique based on maximum distance separable codes at the transmitter. We propose to reduce the computational complexity in iterative-based precoding algorithms by using less complex non-iterative singular value decomposition-based joint precoder and decoder pair design. The maximum likelihood detection for the linear MIMO channel is considered. The simulation results showed that the proposed nonlinear and non-iterative precoding schemes outperform the conventional linear MIMO precoder design, even when a reduced-complexity suboptimal strategy is adopted, considering the bit error rate performance.

Achievable Rate Region for Three User Discrete Broadcast Channel Based on Coset Codes

We consider the problem of developing coding techniques and deriving achievable rate regions for discrete memoryless broadcast channels with three receivers (3-DBC). We begin by identifying a novel vector additive 3-DBC for which we characterize an upper bound on the the largest achievable rate region based on unstructured codes, henceforth referred to as $\pmb {\mathscr {U}}\mathcal {M}$ -region. We propose a coding technique based on coset codes that yield an achievable rate triple not contained within $\pmb {\mathscr {U}}\mathcal {M}$ -region. We generalize the proposed coding technique using a new ensemble of codes— partitioned coset codes (PCC)—containing both empirical and algebraic properties, and evaluate its performance to derive an achievable rate region for the general 3-DBC. The new elements in this derivation are binning and joint typicality encoding and decoding of statistically correlated PCCs. We validate the utility of this technique by identifying non-additive instances of 3-DBC for which the proposed coding techniques based on PCC yield strictly larger rates.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

An Improved Variational Inference Approach to Iterative OFDM Receiver Design for Superimposed Training-Based AF Relay Networks

Using variational Bayesian approximation (VBA), we propose a general framework for joint channel estimation, demodulation, and decoding in an orthogonal frequency-division multiplexing (OFDM) amplify-and-forward (AF) relay network. AF relaying is adopted due to its simplicity and ease of implementation, and both cases of fixed- and variable-gain AF protocols are addressed. Unlike the classical approach where a number of subcarriers are solely dedicated to pilots, partial data-dependent superimposed training is considered here, which preserves the spectral efficiency. Assuming a bit-interleaved coded modulation OFDM system at the source, a modified formulation of VBA is proposed, which is, in fact, a Bayesian framework of turbo data detection of OFDM under channel estimation errors. More precisely, by an appropriate exploitation of the inherent “soft” information on channel and data available from the VBA formalism, we derive a modified iterative receiver, which reduces the impact of channel uncertainty by averaging the “soft data decision” over the posterior distribution of the unknown channel at each decoding iteration. The proposed modified VBA approach is contrasted to the conventional VBA and to the mismatched VBA approach, where, in the latter, the unknown channel is just replaced by its imperfect estimate. We show that conventional and mismatched VBA are suboptimal and can be viewed as a particular case of the proposed VBA method. In addition, we show that the VBA approach makes a nice connection between the classical techniques such as pilot-only channel estimation and the expectation–maximization algorithm, which can be viewed as a special case of VBA. By comparison with state-of-the-art VBA-based estimators through numerical analysis, we highlight the superiority of our modified VBA method and demonstrate a notable performance improvement in terms of the bit error rate.

Performance Analysis of Lossy Decode-and-Forward for Non-Orthogonal MARCs

Non-orthogonal transmission is considered to be one of the promising techniques for improving the throughput of the existing and future wireless communication networks. We concentrate on the transmission of both independent and correlated binary sources over a non-orthogonal multiple access relay channel (MARC), which consists of two sources, one relay, and one destination. The lossy decode-and-forward (DF), developed from the conventional DF, is adopted at the relay. Two time slots are required with non-orthogonal transmission over such network setup, while three time slots are required with the conventional orthogonal transmission. We analyze the outage probability of transmission of independent binary sources over the non-orthogonal MARC based on the theorem of multiple access channel (MAC) with a helper, which combines the Slepian-Wolf rate region and the MAC capacity region. For the performance verification, we implement a practical coding-decoding chain, which is applicable to the transmission of both independent and correlated binary sources. Exclusive-OR based multi-user complete decoding is introduced at the relay node, and iterative joint decoding is utilized at the destination by taking into consideration the estimated intra-link error probability and correlation information between the sources. The practical simulation results are well matched with the theoretical analyses.

Frequency-Domain Joint Channel Estimation and Decoding for Faster-Than-Nyquist Signaling

Faster-than-Nyquist (FTN) signaling has attracted a lot of attentions for the fifth-generation (5G) cellular communication systems. However, low-complexity receiver design for FTN signaling becomes challenging. In this paper, we develop frequency-domain joint channel estimation and decoding methods for FTN signaling transmitting systems over frequency-selective fading channels. To deal with the colored noise inherent in FTN signaling, we propose to approximate the corresponding autocorrelation matrix by a circulant matrix, the special eigenvalue decomposition of which facilitates an efficient fast Fourier transform operation and decoupling the noise in frequency domain. Through a specific partition of the received symbols, many independent estimates are obtained and combined to further improve the accuracy of the channel estimation and data detection. Moreover, instead of assuming the data symbols to be Gaussian random variables, a generalized approximated message passing-based equalization is developed and embedded in the turbo iterations between the channel estimation and the soft-in soft-out decoder. Simulation results show that the proposed algorithm outperforms the cyclic prefix-based and overlap-based frequency-domain equalization methods. With the proposed algorithms, FTN signaling reaches up to 67% higher transmission rate compared to the Nyquist counterpart without substantially consuming more transmitter energy per bit, and the overall complexities grow logarithmically with the length of the observations.

Raptor Codes for Higher-Order Modulation Using a Multi-Edge Framework

In this letter, we represent Raptor codes using a multi-edge framework, which gives a general framework to design them for higher-order modulation using multi-edge type density evolution. We then propose an efficient Raptor code design method for higher-order modulation, where we design distinct degree distributions for distinct bit levels. We consider a joint decoding scheme based on belief propagation for Raptor codes and also derive an exact expression for the stability condition. In several examples, we demonstrate that the higher-order modulated Raptor codes designed using the multi-edge framework outperform previously reported higher-order modulation codes in literature.

A Novel Image Compression-Encryption Scheme Based on Chaos and Compression Sensing

In this paper, a novel image compression–encryption hybrid algorithm is proposed. First, a Gauss random matrix and a random scrambling matrix are generated by using Chebyshev mapping and Logistic mapping, respectively. Then, based on the principle that a scrambling Gauss matrix is still a Gauss matrix, a compression scheme for ciphertext images is designed. It is dependent on the Gauss random matrix and the random scrambling matrix, which mainly consists of three parts: the permutation-based encryption using random scrambling matrix by Alice, the encoding with Gauss random matrix by Charlie, and the joint decryption and decoding by Bob. It has a special application scenario, that is, Alice requires semi integrity Charlie to transmit images to Bob through a channel. Experimental results show that the scheme has strong robustness against noise and chosen-plaintext attack, and further the peak signal-to-noise ratio (PSNR) and subjective visual quality of reconstructed images can be improved by comparing with the similar methods.

Both the single user and multi user MIMO system design with individual transmit power constraint and improper constellation

This work address the problem of designing joint optimum transceiver for both single-user (SU) and multi-user (MU) MIMO system. Most of the previous works on both SU and MU-MIMO system, the joint precoder and decoder designs are based on the total transmit power constraint (TTPC) with proper modulation techniques. On the other hand, in practice, individual transmit power constraint (ITPC) is more realistic as the power at each antenna at the transmitter is restricted independently by the linearity of the power amplifier. In this work, a minimum total mean squared error (TMSE) design is formulated as a non-convex optimisation problem. This formulation is under equal power allocation (EPA) and the power constraint that jointly meets both EPA and TTPC (i.e., ITPC). The closed-form optimum linear precoder and decoder for SU-MIMO systems with an improper modulation are determined by solving this non-convex optimisation problem. It considers both the perfect and imperfect channel state information (CSI) is available at both the transmitter and receiver. The simulation results show the performance improvement of the proposed work over conventional work in terms of bit error rate (BER).

Design and Optimization of Joint Iterative Detection and Decoding Receiver for Uplink Polar Coded SCMA System

SCMA and polar coding are possible candidates for 5G systems. In this paper, we firstly propose the joint iterative detection and decoding (JIDD) receiver for the uplink polar coded sparse code multiple access (PC-SCMA) system. Then, the EXIT chart is used to investigate the performance of the JIDD receiver. Additionally, we optimize the system design and polar code construction based on the EXIT chart analysis. The proposed receiver integrates the factor graph of SCMA detector and polar soft-output decoder into a joint factor graph, which enables the exchange of messages between SCMA detector and polar decoder iteratively. Simulation results demonstrate that the JIDD receiver has better BER performance and lower complexity than the separate scheme. Specifically, when polar code length $N=256$ and code rate $R = 1/2$ , JIDD outperforms the separate scheme 4.8 and 6 dB over AWGN channel and Rayleigh fading channel, respectively. It also shows that, under 150% system loading, the JIDD receiver only has 0.3 dB performance loss compared to the single user uplink PC-SCMA over AWGN channel and 0.6 dB performance loss over Rayleigh fading channel.

Quantum Turbo Decoding for Quantum Channels Exhibiting Memory

Inspired by the success of classical turbo codes, quantum turbo codes (QTCs) have also been conceived for near-hashing-bound transmission of quantum information over memoryless quantum channels. However, in real physical situations, the memoryless channel assumption may not be well justified, since the channel often exhibits memory of previous error events. Here, we investigate the performance of QTCs over depolarizing channels exhibiting memory and we show that they suffer from a performance degradation at low depolarizing probability values. In order to circumvent the performance degradation issue, we conceive a new coding scheme termed quantum turbo coding scheme exploiting error-correlation (QTC-EEC) that is capable of utilizing the error-correlation while performing the iterative decoding at the receiver. The proposed QTC-EEC can achieve convergence threshold at a higher depolarizing probability for channels with a higher value of correlation parameter and achieve performance near to the capacity. Finally, we propose a joint decoding and estimation scheme for our QTC-EEC relying on the correlation estimation (QTC-EEC-E) designed for more realistic quantum systems with unknown correlation parameter. Simulation results reveal that the proposed QTC-EEC-E can achieve the same performance as that of the ideal system of known correlation parameter and hence demonstrate the accurate estimation of the proposed QTC-EEC-E.

Efficient scheduling and power allocation for multiuser decoding receivers in OFDMA networks with minimum rate requirements

In recent years, techniques for exploiting interference on the receiver side to improve the performance of future cellular networks have been of interest. Due to the dominance of data in network traffic, user rate demands are becoming crucial for resource allocation of cells in the network.We propose an efficient transmission scheme for the downlink of an OFDMA multicell multiuser system with multiuser decoding (successive interference cancellation (SIC), joint decoding (JD), single decoding (SD)) capable receivers. We define a marginal rate maximization problem taking into account minimum rate demands of users and develop a practical scheduling (subchannel assignment) and power allocation algorithm using Lagrangian dual decomposition and gradient (ellipsoid and projected subgradient) methods. In the subchannel assignment, we propose the ratio of direct-to-cross channel strengths and multiuser rate regions different than the studies in the literature. Through Lagrangian dual decomposition, we derive the optimal power allocation rule using multiuser decoding modes for a given subchannel assignment.We observe that the method proposed here has low computational complexity and can be easily integrated to next generation networks as well as achieving high performance in practical scenarios.

Analysis and Design of Raptor Codes Using a Multi-Edge Framework

The focus of this paper is on the analysis and design of Raptor codes using a multi-edge framework. In this regard, we first represent Raptor codes as multi-edge type (MET) low-density parity-check codes. This MET representation gives a general framework to analyze and design Raptor codes over a binary input additive white Gaussian noise channel using MET density evolution (MET-DE). We then consider a joint decoding scheme based on the belief propagation (BP) decoding for Raptor codes in the multi-edge framework, and analyze the convergence behavior of the BP decoder using MET-DE. In joint decoding of Raptor codes, the component codes corresponding to the inner code and the precode are decoded in parallel and provide information to each other. We also derive an exact expression for the stability of Raptor codes with joint decoding. We then propose an efficient Raptor code design method using the multi-edge framework, where we simultaneously optimize the inner code and the precode. Through density evolution analysis we show that the designed Raptor codes using the multi-edge framework outperform the existing Raptor codes in literature in terms of realized rates.

Hybrid CTC/Attention Architecture for End-to-End Speech Recognition

Conventional automatic speech recognition (ASR) based on a hidden Markov model (HMM)/deep neural network (DNN) is a very complicated system consisting of various modules such as acoustic, lexicon, and language models. It also requires linguistic resources, such as a pronunciation dictionary, tokenization, and phonetic context-dependency trees. On the other hand, end-to-end ASR has become a popular alternative to greatly simplify the model-building process of conventional ASR systems by representing complicated modules with a single deep network architecture, and by replacing the use of linguistic resources with a data-driven learning method. There are two major types of end-to-end architectures for ASR; attention-based methods use an attention mechanism to perform alignment between acoustic frames and recognized symbols, and connectionist temporal classification (CTC) uses Markov assumptions to efficiently solve sequential problems by dynamic programming. This paper proposes hybrid CTC/attention end-to-end ASR, which effectively utilizes the advantages of both architectures in training and decoding. During training, we employ the multiobjective learning framework to improve robustness and achieve fast convergence. During decoding, we perform joint decoding by combining both attention-based and CTC scores in a one-pass beam search algorithm to further eliminate irregular alignments. Experiments with English (WSJ and CHiME-4) tasks demonstrate the effectiveness of the proposed multiobjective learning over both the CTC and attention-based encoder–decoder baselines. Moreover, the proposed method is applied to two large-scale ASR benchmarks (spontaneous Japanese and Mandarin Chinese), and exhibits performance that is comparable to conventional DNN/HMM ASR systems based on the advantages of both multiobjective learning and joint decoding without linguistic resources.

A Speaker-Dependent Approach to Single-Channel Joint Speech Separation and Acoustic Modeling Based on Deep Neural Networks for Robust Recognition of Multi-Talker Speech

We propose a novel speaker-dependent (SD) multi-condition (MC) training approach to joint learning of deep neural networks (DNNs) of acoustic models and an explicit speech separation structure for recognition of multi-talker mixed speech in a single-channel setting. First, an MC acoustic modeling framework is established to train a SD-DNN model in multi-talker scenarios. Such a recognizer significantly reduces the decoding complexity and improves the recognition accuracy over those using speaker-independent DNN models with a complicated joint decoding structure assuming the speaker identities in mixed speech are known. In addition, a SD regression DNN for mapping the acoustic features of mixed speech to the speech features of a target speaker is jointly trained with the SD-DNN based acoustic models. Experimental results on Speech Separation Challenge (SSC) small-vocabulary recognition show that the proposed approach under multi-condition training achieves an average word error rate (WER) of 3.8%, yielding a relative WER reduction of 65.1% from a top performance, DNN-based pre-processing only approach we proposed earlier under clean-condition training (Tu et al. 2016). Furthermore, the proposed joint training DNN framework generates a relative WER reduction of 13.2% from state-of-the-art systems under multi-condition training. Finally, the effectiveness of the proposed approach is also verified on the Wall Street Journal (WSJ0) task with medium-vocabulary continuous speech recognition in a simulated multi-talker setting.

Physical-Layer Security With Full-Duplex Transceivers and Multiuser Receiver at Eve

Full-duplex communication enables simultaneous transmission from both ends of a communication link, thereby promising significant performance gains. Generally, it has been shown that the throughput and delay gains of full-duplex communication are somewhat limited in realistic network settings, leading researchers to study other possible applications that can accord higher gains. The potential of full-duplex communication in improving the physical-layer security of a communication link is investigated in this contribution. We specifically present a thorough analysis of the achievable ergodic secrecy rate and the secrecy degrees of freedom with full-duplex communication in the presence of a half-duplex eavesdropper node, with both single-user decoding and multi-user decoding capabilities. For the latter case, an eavesdropper with successive interference cancellation and joint decoding capabilities is assumed. Irrespective of the eavesdropper capabilities and channel strengths, the ergodic secrecy rate with full-duplex communication is found to grow linearly with the log of the direct channel signal-to-noise-ratio (SNR) as opposed to the flattened out secrecy rate with conventional half-duplex communication. Consequently, the secrecy degrees of freedom with full-duplex is shown to be two as opposed to that of zero in half-duplex mode.

On the Achievable Rate of Bistatic Modulated Rescatter Systems

In ambient rescatter communications, devices convey information by modulating and rescattering the radio frequency signals impinging on their antennas. In this correspondence, we consider a system consisting of a legacy modulated continuous carrier multiple-input-multiple-output link and a multiantenna modulated rescatter (MRS) node, where the MRS node modulates and rescatters the signal generated by the legacy transmitter. The receiver seeks to decode both the original message and the information added by the MRS. We show that the achievable sum rate of this system exceeds that which the legacy system could achieve alone. We further consider the impact of channel estimation errors under the least squares channel estimation and study the achievable rate of the legacy and MRS systems, where a linear minimum mean square error receiver with successive interference cancellation is utilized for joint decoding.

Correlation Reduction Precoding for Joint Decoding in Overloaded MIMO-OFDM System on Rician Channel

This paper presents a correlation reduction precoding scheme for joint decoding in an overloaded multiple input multiple output (MIMO)-orthogonal frequency division multiplexing system. In the overloaded MIMO system, the number of receive antenna elements is less than that of transmit antenna elements. Through joint maximum likelihood (ML) decoding a frequency diversity suppresses the BER degradation owing to the spatial multiplexing of signal streams. On a Rician channel, the correlation among coded symbols deteriorates the performance of the joint ML decoding. Thus, this paper proposes the precoding scheme that reduces the correlation on the Rician channel. Numerical results obtained through computer simulation show that the proposed scheme improves the BER by a factor of 1/10 on the Rician channel with a Rician factor of 10.

Joint Blind Equalization and Decoding in OFDM Systems Using Particle Filtering

In this paper a sequential algorithm is proposed for joint blind channel equalization and decoding for orthogonal frequency-division multiplexing (OFDM) in frequency selective channels. This algorithm offers a recursive method to sequentially calculate the posterior probability for maximum a posteriori detection. Recursive calculations are done along the indexes in each OFDM symbol using a particle filter. By defining an appropriate importance function, and a proper prior probability distribution function for the channel tap coefficients (and marginalizing it), an efficient method is presented for joint equalization and channel decoding in OFDM based systems. Performance of the proposed detector is evaluated using computer simulations and its bit error rate is compared with the trained turbo equalizer and a conventional particle filter-based method. The results show that the proposed method outperforms the previously presented particle filter-based method without a need for training data.

Iterative MMSE‐based soft MIMO detection with parallel interference cancellation

Minimum mean square error (MMSE)-based techniques are often used for the joint iterative detection and decoding that is for coded multi-input-multi-output (MIMO) system due to a sound complexity and the performance trade-off. This study proposes an enhanced MMSE-based soft MIMO-detection scheme by using three main ideas. The first idea is an efficient complexity-reduced soft-bit estimation technique, the second one is a performance improvement method utilised inside the MMSE detection process, and the third one is a complexity-reduced soft-symbol estimation method for quadrature amplitude modulation. The proposed ideas enable the interference-cancellation processes to be activated in parallel on each symbol layer, thereby reducing the processing time. The simulation results show that the proposed method efficiently contributes to the improvement of the performance in addition to its reduction of the linear-order complexity.