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Low-density lattice codes (LDLCs) achieve near-capacity performance on additive white Gaussian noise (AWGN) channels. The $M$ -Gaussian decoder is the state-of-the-art message passing decoder for LDLCs in terms of the error performance. However, this decoder has complexity $O(M^{d-1})$ with messages represented by Gaussian mixtures, where $d$ is the degree of an LDLC and $M$ is the number of Gaussian functions for approximating each check node message. In this paper, we establish the correspondence between Gaussian functions for approximating a variable node message and points of a certain lattice. Based on this lattice viewpoint, the problem of approximating a variable node message is formulated as a lattice point enumeration (LPE) problem. Then, an LPE decoder with linear complexity $O(d)$ is proposed. Our simulation results validate that the LPE decoder achieves almost the same error performance as the $M$ -Gaussian decoder.

Index assignment (IA) is a low-complexity joint source-channel coding technique that has the potential for use in low-latency and low-power applications, such as wireless sensor networks (WSNs). Though binary IA has been extensively studied for assigning binary indices to quantized codewords (or symbols) under the assumption of binary symmetric channels (BSCs), real-world scenarios often use $M$ -ary modulations. Directly applying binary IAs designed for BSCs to $M$ -ary modulations results in suboptimal performance. In this paper, we investigate the $M$ -ary IA, which assigns $M$ -ary labels to quantized codewords (or symbols), assuming the use of a equiprobable lattice quantizer. For such a system, we derive a tight performance bound and propose a near-optimal IA scheme based on a two-step design. In addition, we propose explicit IA constructions for practical modulation schemes, including PAM, QAM, and PSK. Our proposed IA design is rigorously proven to be optimal for 3-PSK and QPSK, whereas for larger modulation orders, the proposed IA constructions approach the bounds within small gaps. Our simulations show that the constructed IA scheme can achieve significant energy savings compared to the conventional binary IA scheme. Specifically, in some WSN scenarios, the proposed IA for 16-QAM is shown to achieve significant reductions in energy consumption relative to the conventional binary counterpart.

In this paper, we re-examine the classical problem of efficiently evaluating the block and bit error rate performance of linear block codes over binary symmetric channels (BSCs). In communication systems, the maximum likelihood decoding (MLD) bounds are powerful tools to predict the error performance of the coded systems, especially in the asymptotic regime of low error probability (or high signal-to-noise ratio). Contrary to the conventional wisdom, we prove that for BSCs, all bounds based on Gallager’s first bounding technique, including the famous union bound, are not asymptotically tight for all possible choices of the Gallager region. By proposing the so-called input demodulated-output weight enumerating function (IDWEF) of a code, asymptotically tight MLD upper and lower bounds for BSCs are then derived. In many practical scenarios where performance bounds are not applicable (e.g., due to the unavailability of the relevant coding parameters under a given decoder), the Monte Carlo simulation is commonly used despite its inefficiency, especially in the low error probability regime. We propose an efficient importance sampling (IS) estimator by deriving the optimal IS distribution of the Hamming weight of the error vector. In addition, the asymptotic relative saving on the required sample size of the proposed IS estimator over the state-of-the-art counterpart in the recent literature is characterized. Its accuracy in predicting the efficiency of the proposed IS estimator is verified by extensive computer simulation.

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary. Compressed sensing, sparse approximation, and dictionary learning are topics similar to sparse coding. Matrix completion is the process of recovering a data matrix from a subset of its entries, and it extends the principles of compressed sensing and sparse approximation. The nonnegative matrix factorization is a low-rank matrix factorization technique for nonnegative data. All of these low-rank matrix factorization techniques are unsupervised learning techniques, and can be used for data analysis tasks, such as dimension reduction, feature extraction, blind source separation, data compression, and knowledge discovery. In this paper, we survey a few emerging matrix factorization techniques that are receiving wide attention in machine learning, signal processing, and statistics. The treated topics are compressed sensing, dictionary learning, sparse representation, matrix completion and matrix recovery, nonnegative matrix factorization, the Nyström method, and CUR matrix decomposition in the machine learning framework. Some related topics, such as matrix factorization using metaheuristics or neurodynamics, are also introduced. A few topics are suggested for future investigation in this article.

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it is incapable of classifying linearly inseparable patterns. A new era of neural network research started in 1986, when the backpropagation (BP) algorithm was rediscovered for training the multilayer perceptron (MLP) model. An MLP with a large number of hidden nodes can function as a universal approximator. To date, the MLP model is the most fundamental and important neural network model. It is also the most investigated neural network model. Even in this AI or deep learning era, the MLP is still among the few most investigated and used neural network models. Numerous new results have been obtained in the past three decades. This survey paper gives a comprehensive and state-of-the-art introduction to the perceptron model, with emphasis on learning, generalization, model selection and fault tolerance. The role of the perceptron model in the deep learning era is also described. This paper provides a concluding survey of perceptron learning, and it covers all the major achievements in the past seven decades. It also serves a tutorial for perceptron learning.

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators' network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.

Camera-based wireless video sensor networks present opportunities to use large numbers of low-cost, low-resolution wireless camera sensors for various applications such as outdoor remote surveillance and ubiquitous data transmission in line-of-sight systems. Owing to the continuously increasing popularity of displays and cameras, communication in sensor networks with display–camera pairs has become a promising area in both computer vision and wireless communication communities. However, measurement/sampling procedures for sensing data in wireless video sensor networks have not been established well yet. Consistency in channel measurements is an open issue, as precise calibration of the experimental settings has not been fully studied in the literature. This article focuses on establishing a scheme for the precise calibration of the display–camera channel performance. To guarantee high consistency in the experiment, we propose an accurate measurement scheme for the geometric parameters and identify some unstable channel factors such as the Moire effect, rolling-shutter effect, blocking artifact, inconsistencies in autofocus, trembling, and vibrations. In the experiment, we first define the consistency criteria based on the error-prone regions in the bit error rate plots of the channel measurements. It is demonstrated that the consistency of the experimental results can be improved by the proposed precise calibration scheme.