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This paper investigates the design and performance of delayed bit-interleaved coded modulation (DBICM) with low-density parity-check (LDPC) codes. For Gray labeled square M-ary quadrature amplitude modulation (QAM) constellations, we investigate the optimal delay scheme with the largest spectrum efficiency of DBICM for a fixed maximum number of delayed time slots and a given signal-to-noise ratio. When analyzing the capacity of DBICM, we find two important properties: the capacity improvement due to delayed coded bits being mapped to the real and imaginary parts of the transmitted symbols are independent of each other; a pair of delay schemes with delayed coded bits having identical bit-channel capacity lead to equivalent DBICM capacity. Using these two properties, we efficiently optimize the delay scheme for any uniform Gray-QAM systems. Furthermore, these two properties enable efficient LDPC code designs regarding unequal error protection via bit-channel type classifications. Moreover, we use protograph-based extrinsic information transfer charts to jointly optimize degree distributions and channel assignments of LDPC codes and propose a constrained progressive edge growth like algorithm to jointly construct LDPC codes and bit-interleavers for DBICM, taking distinctive bit-channel’s capacity into account. Simulation results demonstrate that the designed LDPC coded DBICM systems significantly outperform LDPC coded BICM systems. [ABSTRACT FROM AUTHOR]

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user’s characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user’s characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user’s characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user’s domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user’s features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec. [ABSTRACT FROM AUTHOR]

In most real-world settings, designs are often gradually adapted and improved over time. Consequently, there exists knowledge from distinct (but possibly related) design exercises, which have either been previously completed or are currently in-progress, that may be leveraged to enhance the optimization performance of a particular target optimization task of interest. Further, it is observed that modern day design cycles are typically distributed in nature, and consist of multiple teams working on associated ideas in tandem. In such environments, vast amounts of related information can become available at various stages of the search process corresponding to some ongoing target optimization exercise. Successfully exploiting this knowledge is expected to be of significant value in many practical settings, where solving an optimization problem from scratch may be exorbitantly costly or time consuming. Accordingly, in this paper, we propose an adaptive knowledge reuse framework for surrogate-assisted multiobjective optimization of computationally expensive problems, based on the novel idea of multiproblem surrogates. This idea provides the capability to acquire and spontaneously transfer learned models across problems, facilitating efficient global optimization. The efficacy of our proposition is demonstrated on a series of synthetic benchmark functions, as well as two practical case studies. [ABSTRACT FROM AUTHOR]

In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load u. To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying u. To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying u are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of u. Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method to design low-density parity-check Hadamard (LDPC-Hadamard) codes — a type of ultimate-Shannon-limit approaching channel codes. The technique is based on applying Hadamard constraints to the check nodes in a generalized protograph-based LDPC code, followed by lifting the generalized protograph. We name the codes formed protograph-based LDPC Hadamard (PLDPC-Hadamard) codes. We also propose a modified Protograph Extrinsic Information Transfer (PEXIT) algorithm for analyzing and optimizing PLDPC-Hadamard code designs. The proposed algorithm further allows the analysis of PLDPC-Hadamard codes with degree- 1 and/or punctured nodes. We find codes with decoding thresholds ranging from −1.53 dB to −1.42 dB. At a BER of 10−5, the gaps of our codes to the ultimate-Shannon-limit range from 0.40 dB (for rate = 0.0494) to 0.16 dB (for rate = 0.003). Moreover, the error performance of our codes is comparable to that of the traditional LDPC-Hadamard codes. Finally, the BER performances of our codes after puncturing are simulated and compared. [ABSTRACT FROM AUTHOR]

In the soft-input soft-output Viterbi algorithm (SOVA), the log-likelihood ratio (LLR) of each bit is determined by the minimum metric difference between the ML path and its competitive paths. This paper proposes to trim large metric differences in order to reduce the complexity of SOVA. By trimming the metric differences, only a small number of backtracking operations are carried out, while many LLRs may be omitted as the result of the lack of metric differences. By revealing the relationship among neighboring LLRs, the omitted LLRs are estimated from its neighoring LLRs as well as intrinsic information. The extrinsic information transfer chart analysis demonstrates that the proposed algorithm has similar convergence behavior as the Log-MAP algorithm, if the trimming factor $M$ is moderate. Other analyses verify that our approach provides good LLR quality with only at most $1/M$ backtracking operations of SOVA. Simulation results show that it outperforms SOVA and performs as well as its variants and the Log-MAP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence. [ABSTRACT FROM AUTHOR]

The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths. From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes. By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths. In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type. To reduce the complexity and overcome the vanishing gradient problem in training the proposed neural MS decoder, an iteration-by-iteration (i.e., layer-by-layer in neural networks) greedy training method is proposed. With this, the proposed neural MS decoder tends to be optimized with faster convergence, which is aligned with the early termination mechanism widely used in practice. To further enhance the generalization ability of the proposed neural MS decoder, a codelength/rate compatible training method is proposed, which randomly selects samples from a set of codes lifted from the same base code. As a theoretical performance evaluation tool, a trajectory-based extrinsic information transfer (T-EXIT) chart is developed for various decoders. Both T-EXIT and simulation results show that the optimized MS decoding can provide faster convergence and up to 1dB gain compared with the plain MS decoding and its variants with only slightly increased complexity. In addition, it can even outperform the sum-product algorithm for some short codes. [ABSTRACT FROM AUTHOR]

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. [ABSTRACT FROM AUTHOR]

A magnetic recording read channel has numerous parameters that must be carefully tuned for its best performance; these include not only the equalizer coefficients, but also any parameters inside the soft-output detector, some of which may be pattern-dependent, including signal levels, predictor coefficients, and residual noise variances. Conventional tuning strategies based on a minimum-mean-squared error criterion are not optimal in terms of frame-error rate and ultimately areal density. Here, we propose a strategy for optimizing the parameters with the aim of minimizing the frame-error rate after error-control decoding. The proposed strategy exploits the close connection between the frame-error rate and the gap between the two curves in an extrinsic information transfer (EXIT) chart. A stochastic gradient algorithm applied to a cost function that quantifies this gap leads to our proposed adaptive minimum-frame-error rate (AMFER) algorithm for adapting the equalizer and detector parameters. Numerical results based on a quasi-micromagnetic simulated channel show that the AMFER parameters can reduce the frame-error rate by more than two orders of magnitude, leading to a 7% gain in areal density over conventional minimum mean-square-error (MMSE) parameters. [ABSTRACT FROM AUTHOR]

Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this article develops a divide-and-conquer GP algorithm with knowledge transfer (KT) and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divide-and-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new KT method is proposed to improve GP learning performance. A new fitness function based on log loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-the-art algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and KT in the proposed approach. [ABSTRACT FROM AUTHOR]

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms. [ABSTRACT FROM AUTHOR]