Recent studies have shown that analog joint source-channel coding (AJSCC) is capable of approaching the optimum performance theoretically attainable (OPTA). However, it would be costly to transmit the continuous output of AJSCC. This paper proposes a two-dimensional (2D) quantization-based modulation scheme to map the discrete-time real-valued source encoder output into a finite set of 2D constellation points for modulated transmission. To minimize the resultant quantization distortion, the constellation is optimized by constructing a centroidal Voronoi diagram that can best match the distribution of the source encoder output under power transformation. Based on a comprehensive analysis of effects of encoder configuration and power transformation in connection with the channel condition, an algorithm is presented for jointly optimizing the source encoder and the constellation, with the objective of maximizing the system performance in terms of the end-to-end signal-to-distortion ratio (SDR) with respect to the channel condition in terms of signal-to-noise ratio (SNR). Simulation results show that under joint optimization, the modulated AJSCC system improves as the modulation order increases, and it tends to approach the benchmark performance of the unmodulated system that is close to the OPTA. More practically, the modulated AJSCC system is robust to tolerate imperfect channel estimation to some extent. This work provides a promising way of enabling a source-oriented AJSCC system to access digital communication networks.

Most current long-tailed classification approaches assume the cost-agnostic scenario, where the training distribution of classes is long-tailed while the testing distribution of classes is balanced. Meanwhile, the misclassification costs of all instances are the same. On the other hand, in many real-world applications, it is more proper to assume that the training and testing distributions of classes are the same, while the misclassification cost of tail-class instances is varied. In this work, we model such a scenario as cost-aware long-tailed classification, in which the identification of high-cost tail instances and focusing learning on them thereafter is essential. In consequence, we propose the learning strategy of augmenting new instances based on adaptive region partition in the feature space. We conduct theoretical analysis to show that under the assumption that the feature-space distance and the misclassification cost are correlated, the identification of high-cost tail instances can be realized by building region partitions with a low variance of risk within each region. The resulting AugARP approach could significantly outperform baseline approaches on both benchmark datasets and real-world product sales datasets.

Cyclic logic locking is a cutting-edge hardware security method developed to defend against SAT Attack. It introduces cycles into the original circuit, which can cause the circuit to either get trapped in an endless loop or generate incorrect outputs if an incorrect key is used. Recently, a new cyclic logic locking method called LOOPLock 2.0 was proposed. Its primary feature is that the circuit retains its cyclic structure regardless of whether the correct key vector is applied or not. However, LOOPLock 2.0 can still be successfully attacked using locking structure analysis in the state-of-the-art. As a result, this paper presents a more robust cyclic logic locking approach LOOPLock 3.0 to counteract state-of-the-art attacks. The experimental results validate the effectiveness of the proposed approach.

This paper investigates the possibilities of optimizing existing protograph low-density parity-check (PLDPC) codes and designing optimal PLDPC codes on both AWGN and Rayleigh channels under binary modulation with unequal power allocation (UPA) for nonuniform sources. While the binary UPA modulation achieves a power gain, it also results in unequal error protection on the coded bits in systematic codewords during transmission. For irregular codes, it is possible to best protect the information bits by optimizing the classification of variable nodes (VNs). Those VNs represent the coded bits in protograph that allows exploiting jointly the degree irregularity and the power differentiation. An algorithm for UPA-based protograph extrinsic information transfer (UPA-PEXIT) analysis on the Rayleigh channel is proposed with the concept of surrogate channel which circumvents the channel asymmetry problem with UPA. With the RJA code as example, it is shown that classification optimization does improve the decoding threshold when the source entropy is small. To design optimal codes, a UPA-based differential evolution (UPA-DE) algorithm is proposed in conjunction with the UPA-PEXIT analysis. A family of optimized codes are illustrated for three typical nonuniform source distributions and compared with the RJA and NND codes by simulations in error rate performance.

The prefixed Varshamov-Tenengolts (VT) codes, which are subsets of VT codes with predetermined prefixes, can be used for error correction over segmented edit channels. In this paper, we investigate the construction and analysis of this class of codes. First, we derive upper bounds on the size of zero-error codes for segmented edit channels with segment-by-segment decoding. Second, we establish a one-to-one correspondence between prefixed VT codes and Levenshtein codes. Based on this relation, we can obtain explicit formulas on the size of prefixed VT codes via the existing results on the size of Levenshtein codes. Third, we construct a new zero-error prefixed VT code and show that the size of the constructed code is strictly larger than that of the existing prefixed VT code for the segmented deletion channel. Finally, an efficient systematic encoding method of prefixed VT codes is proposed for the segmented edit channels.

Probabilistic amplitude shaping (PAS) has proved to be a promising way to achieve the shaping gain for an additive white Gaussian noise channel with a distribution matcher (DM). However, the DM schemes may suffer a rate loss and increase both complexity and latency, when they are not suited for the input bit stream with redundancy. In this paper, a novel PAS strategy is proposed for a joint source and channel coded modulation system, where the residual source redundancy after source coding can be exploited to obtain both shaping and coding gains. It is shown that the residual source redundancy can be controlled by choosing the row weight distribution for source code, thus making the probability distribution of the modulated symbols be optimized under an appropriate interleaver design. To guarantee the error-floor performance, the source code is constrained by predicting the probability distribution of source bits within target finite-length frames. By jointly designing source and channel codes, the residual source redundancy can also be exploited to achieve the coding gain. Compared with the state-of-the-art code pairs, the proposed code pairs have better error-floor performance, and achieve higher coding and shaping gains without suffering from the rate-loss and the latency caused by DM.

The rank-modulation scheme has been recently proposed to write and store data in flash memories efficiently. In this paper, a new construction of systematic error-correcting codes for permutations is presented under the Chebyshev distance. By constructing a subgroup code and using its coset codes to partition the set of information permutations, the proposed code construction can achieve much larger code cardinality and hence higher code rates. To facilitate the encoding and decoding of the constructed codes, we also investigate the concepts of ranking and unranking for permutations, and generalize them to M -ranking and M -unranking for multi-permutations. Examples are provided to demonstrate the relevant concepts and the encoding/decoding algorithms.

This study investigates the application of artificial fish swarm algorithm (AFSA) in the power allocation for multiple‐input and multiple‐output orthogonal frequency‐division multiplexing (MIMO‐OFDM) relay underwater acoustic (UWA) communication systems. First, by using the singular value decomposition technique, the two‐hop transmission links are converted into the virtual direct links in an single‐input and single‐output OFDM (SISO‐OFDM) system. Then, a power allocation optimisation problem, together with the assignment of subcarriers and relay nodes, are formulated for the virtual SISO‐OFDM system. Finally, the problem‐solving algorithms are proposed in two parts. Computer simulation results show that the proposed AFSA scheme improves in both power consumptions and diversity gains compared with two existing schemes for UWA communication systems.

The Euclidean projection involved in the decoding of low-density parity-check (LDPC) codes with the alternating direction method of multipliers (ADMM) can be simplified by jointly using uniform quantization and look-up tables (LUTs). However, the memory requirement for the original LUT-based ADMM decoding is comparatively large. In this letter, a nonuniform quantization method is proposed to save the memory cost by minimizing the mean square error of the outputs of Euclidean projections during quantization. Simulation results over two exemplified LDPC codes show that the proposed method can achieve similar error-rate performances when compared with the original LUT-based ADMM decoding by using significantly less memory units.

This paper investigates full-duplex (FD) secure transmissions with simultaneous wireless information and power transfer (SWIPT) on downlink (DL), where an FD base station, with multiple transmit antennas and multiple receive antennas, serves a group of single-antenna users on both DL and uplink (UL). An optimization problem with non-convex form is formulated for minimizing the system power under constraints on energy harvesting (EH) and security rates. In order to efficiently solve it, the original non-convex problem is first approximated to a convex one and then casted into a form of second-order cone programming (SOCP). Iterative solving algorithms are presented with the computational complexity analysis. Simulation results show that the proposed FD secure scheme is more power-efficient than conventional one, and the SOCP formulation is more computationally efficient than its semi-definite programming alternative. Compared with half-duplex schemes, the FD scheme is also advantageous in power performance especially under high DL and low UL security rate requirements.

This paper investigates secure cooperative transmissions in a dual-hop MIMO relay system using a combined transmit antenna selection (TAS) and maximal-ratio combining (MRC) scheme over the Nakagami-m fading channels, where an adaptive decode-and-forward relaying protocol and an multi-antenna eavesdropper are considered. Due to the feedback delay, channel state information (CSI) for TAS might be outdated at both the source and the relay. To evaluate the secrecy performance of the TAS/MRC scheme and the impacts of outdated CSI, the closed-form expressions for the metrics of exact ergodic secrecy rate and exact secrecy outage probability are derived under both perfect and outdated CSI conditions in a channel feedback error model. In order to explicitly reveal the behaviors of the secrecy performance in high signal-to-noise ratio regime, asymptotic expressions for both the metrics are further derived. As validated by simulation results, analytically numerical results demonstrate that outdated the CSI always results in a loss in the secrecy performance, but the loss can be recovered by increasing the number of antennas at legitimate receivers. Furthermore, the outdated CSI yields a reduced secrecy diversity order, whereas only the perfect CSI leads to the full secrecy diversity order.

This paper addresses robust designs of secure simultaneous wireless information and power transfer for multiple-input single-output (MISO) multiuser systems. By using a bounded uncertainty model for the channel state information (CSI) from transmitters to legitimate receivers and under the assumption of only available statistic knowledge of the CSI to eavesdroppers, we consider the robust designs with both worst-case and chance-constrained optimizations. To jointly optimize transmit beamforming vectors and power splitting factors, we formulate three problems aiming at minimizing the system power, maximizing the sum secrecy rate, and maximizing the minimal secrecy rate, respectively, subject to both energy harvesting and secrecy outage constraints. These problems are intractable in their original forms due to the CSI models and their non-convexity. To reach computationally efficient solutions, we first rewrite the original problems by semi-definite relaxation, then transform the CSI uncertainty related constraints by S-procedure, and finally approximate the non-convex constraints by the first-order Taylor expansion. By the reformulations, we present iterative successive convex approximation algorithm for approaching the optimized solutions. Simulation results under various setups of system parameters are provided to examine the system performance and validate the effectiveness of the proposed schemes.

Linear programming decoding with the alternating direction method of multipliers (ADMM) is a promising decoding technique for low-density parity-check (LDPC) codes, where the computational complexity of Euclidean projections onto check polytopes becomes a prominent problem. In this paper, the problem is circumvented by building lookup tables (LUTs) and quantizing the inputs to approach approximate Euclidean projections at low computational complexities. To challenge the huge memory cost of LUTs, we first propose two commutative compositions of Euclidean projection and self-map, and show the existence of a small quantization range which does not alter the Euclidean projection. Then, we investigate the design and simplification of the LUTs by exploiting the commutative compositions and check node decomposition techniques. An efficient algorithm for the LUT-based projection is demonstrated by using one simplification method. Simulation results show that for both the regular and irregular LDPC codes, the ADMM decoding using LUT-based projection can substantially reduce the decoding time while maintaining the error rate performance at a comparatively large memory cost.

A class of orthogonal convolutional codes featuring self-doubly-orthogonal properties is analyzed under iterative decoding techniques. The self-doubly-orthogonal properties of these codes allow them to approach the asymptotic error performance using a low complexity iterative threshold decoding algorithm. It can be shown that convolutional self-doubly-orthogonal codes are also suited for iterative belief propagation (BP) decoding algorithm with typically five iterations to approach the asymptotic error performance. At a substantially reduced complexity, iterative threshold decoding requires the same number of iterations as iterative BP decoding to achieve practically the asymptotic error performance at moderate signal-to-noise ratios.

An algebraic construction for a class of low-density parity-check (LDPC) convolutional codes is presented on the basis of difference families associated with the code generator matrix. It can be shown that these codes have girth of at least 10 on the Tanner graph which is independent of either the size of the code generator matrix or the minimum Hamming distance of the codes. The code construction guarantees the independence of the messages exchanged in the belief propagation decoding process during two successive decoding iterations. Computer simulations show that over the additive white Gaussian noise channel, the best error performance of these codes at moderate signal-to- noise ratio values is practically obtained using only three to five iterations.

The complexities of iterative belief propagation decoding techniques for randomly constructed low-density parity-check (LDPC) block codes and nonrecursive convolutional self-doubly-orthogonal codes (CSO2Cs) are analyzed and compared on the basis of their decoding latencies expressed in the number of code symbols. Although, in principle, they have the same computational complexities, the pipeline decoder structure for CSO2Cs has a much smaller implementation complexity than the block decoder structure for LDPC block codes, but at a cost of small loss in the error performance of decoding.

A novel approach for the algebraic construction of low-density parity-check (LDPC) convolutional codes is presented. It is based on the orthogonality structures of the codes. The proposed code construction leads to a girth of at least 10 in the Tanner graph. The error performance of these codes compares favorably with the usual LDPC convolutional codes, especially at low signal-to-noise ratio range.

The complexities and latencies of iterative belief propagation (BP) and threshold decoding techniques for a new class of convolutional self-orthogonal codes are analysed using forward-only pipeline architectures. Computer simulation results show that over the additive white Gaussian noise channel at moderate signal-to-noise ratios, BP decoding yields one half the decoding delay when compared with threshold decoding at essentially the same error performances. However, the reduction in the decoding delay using BP is obtained at the cost of an increase of the implementation complexity.

The forward-only iterative decoding techniques for convolutional self-doubly-orthogonal codes are systematically presented based on one-step belief propagation (BP) decoding and one-step threshold decoding. A feedback mechanism and a weighing technique are examined in order to improve both the convergence speed and error performance. Computer simulation results show that compared with the iterative threshold decoding over an additive white Gaussian noise channel, the iterative BP decoding for these codes achieves essentially the same error performance while requiring only about half the number of iterations. Therefore, these two iterative decoding techniques can provide a tradeoff between the latency and the complexity of decoding and allow for the applications of these codes in very high speed wireless communications.

A procedure for the forward-only iterative belief propagation decoding of orthogonal convolutional codes is presented. It can be dramatically simplified to perform the iterative threshold decoding of convolutional self-doubly-orthogonal codes without interleaving. These procedures can help implement iterative decoders efficiently using a serial concatenation of one-step BP decoders or one-step threshold decoders, respectively. Simulations have shown that the error performance of orthogonal convolutional codes can be improved by iterative decoding whether based on belief propagation decoding or threshold decoding. For convolutional self-doubly-orthogonal codes, iterative threshold decoding can achieve the same error performance as iterative belief propagation decoding, but with greatly reduced decoding complexity, allowing an advantageous tradeoff between implementation complexity and latency.

A variant of convolutional self doubly orthogonal codes that can be decoded using an iterative threshold decoding algorithm is presented. These new codes are called degenerate convolutional self-doubly orthogonal codes since not all the double orthogonality conditions required to obtained convolutional self doubly orthogonal codes defined in the wide sense are satisfied. The memory lengths or spans of the degenerate convolutional self-doubly orthogonal codes are substantially shorter than those of the usual convolutional self doubly orthogonal codes defined in the wide sense, at the cost of only a slight degradation of the error performances. As a consequence, very low complexity implementations are possible with these error correcting schemes. Several new degenerate convolutional self doubly orthogonal codes have been determined and their error performances evaluated using computer simulations.

An efficient parallel processing mode is proposed for the implementation of fast forward-only iterative belief propagation decoding of orthogonal convolutional codes based on one-step decoding. In the proposed parallel processing mode, the iterative decoding of orthogonal convolutional codes makes use of the complete set of orthogonal parity-check equations on each information symbol, thus avoiding the error performance loss. The proposed parallel processing allows the speed of iterative decoding to approach that of one-step decoding. Therefore, an advantageous tradeoff can be available between data rate and latency.

The structures of convolutional self-orthogonal codes and convolutional self-doubly-orthogonal codes for both belief propagation and threshold iterative decoding algorithms are analyzed on the basis of difference sets and computation tree. It is shown that the double orthogonality property of convolutional self-doubly-orthogonal codes improves the code structure by maximizing the number of independent observations over two successive decoding iterations while minimizing the number of cycles of lengths 6 and 8 on the code graphs. Thus, the double orthogonality may improve the iterative decoding in both convergence speed and error performance. In addition, the double orthogonality makes the computation tree rigorously balanced. This allows the determination of the best weighing technique, so that the error performance of the iterative threshold decoding algorithm approaches that of the iterative belief propagation decoding algorithm, but at a substantial reduction of the implementation complexity.

A class of forward-only iterative belief propagation algorithms for decoding convolutional self-orthogonal codes is presented, which perform successively a number of one-step decoding and thus have only an initial decoding delay. The one-step belief propagation decoders can be realized in a similar way to one-step threshold decoders. The error performance of the algorithms is easily improved by using a weighing technique. These iterative algorithms allow good tradeoffs between complexity, latency, and error performance of the coding scheme.