This paper proposes a novel framework for coordinated free-space optical (FSO) relaying networks utilizing optical reconfigurable intelligent surfaces (ORISs), aiming to enhance overall communication performance. By intelligently controlling both reflection and refraction properties, the ORIS enables dynamic beam steering to effectively mitigate the multiple-access limitations inherent in traditional FSO networks. Exploiting this capability, we investigate coordinated transmission schemes that maximize network resource utilization and enhance signal quality, which have been largely underexplored in the context of FSO networks due to access constraints. Analytical derivations and simulation results demonstrate that the proposed ORIS-assisted coordinated relaying network significantly outperforms conventional time-division multiple-access-based FSO networks, especially under moderate to high optical transmission power, where the sum ergodic capacity can be dramatically increased, potentially even doubled. Building on these findings, this study provides foundational insights for the development of next-generation optical wireless communication networks, where ORIS not only enables multiple-access but also improves link capacity and energy efficiency in mixed optical networks.

The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we(i)provide an overview of TinyML and LargeML,(ii)analyze the motivations and requirements for unifying these paradigms within the 6G context,(iii)examine efficient bidirectional integration approaches,(iv)review state-of-the-art solutions and their applicability to emerging 6G services, and(v)identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet of Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.

This work proposes a novel semi-non-orthogonal multiple access (NOMA) and data transmission technique, called Semi-NOMA, to enhance the spectrum utilization of aerial simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided symbiotic networks without using successive interference cancellation approaches as classical NOMA. In particular, the proposed scheme is investigated with active and passive STAR-RIS models combined with infinite blocklength (IBL) and finite blocklength (FBL) regimes under discrete phase-shift alignments. For IBL scenarios, the ergodic capacity and outage probability are derived under both approximation and asymptotic frameworks. Besides, a joint optimization problem of the power allocation factor and energy splitting coefficient is also formulated to maximize the ergodic sum capacity (ESC), where closed-form solutions are derived for both active and passive STAR-RIS models. For FBL scenarios, not only the approximation and asymptotic frameworks are derived for the average achievable rate and block-error rate, but also an approximated convex form is derived for a non-convexity optimization problem of min-max blocklength. Numerical results corroborate the efficacy of the proposed Semi-NOMA over the baseline schemes, the developed mathematical frameworks, and the solutions of the ESC maximization and min-max blocklength.

With the broadening applications of deep learning, neural decoders have emerged as a key research focus, specifically aimed at improving the decoding performance of conventional decoding algorithms. In particular, error correction code transformer (ECCT), which utilizes the transformer architecture, has achieved state-of-the-art performance among neural network-based decoders. We present three technical contributions to significantly enhance the performance of ECCT. First, we propose a novel transformer architecture of ECCT, termed themultiple-masks ECCT (MM ECCT). We employ multiple masked self-attention blocks with different mask matrices in a parallel manner to learn diverse relationships among the codeword bits. Second, we discover that constructing mask matrices based on systematic parity check matrices (PCMs) can make the attention mapssparse, which not only enhances the decoding performance but also reduces computational complexity. Finally, we propose using complementary mask matrices derived from cyclic permutations of the systematic PCM. These complementary mask matrices are specifically designed to enhance the decoding of cyclic codes. Our extensive simulation results show that the proposed MM ECCT architecture with carefully designed mask matrices outperforms the original ECCT by a large margin, achieving state-of-the-art decoding performance among neural decoders. The source code is available at https://github.com/iil-postech/mm-ecct.

This paper proposes a novel binary code to correct one insertion and at most two consecutive deletion errors simultaneously occurring in a codeword. Our proposed code is intended first to isolate the possible error cases among all the error scenarios, and then correct the errors. Furthermore, we provide detailed descriptions of the code construction and also suggest a decoding strategy for the proposed code. According to the proposed code,$5\log _{2} n +O(1)$redundancy bits are required to correct one insertion and at most two consecutive deletion errors occurring in a codeword. In addition, a decoding procedure of the proposed code is comprehensively presented for all error scenarios including a single insertion error, an insertion and a deletion error, and an insertion and two consecutive deletion errors.

The emerging quantum technology has highlighted the necessity for secure and efficient secret sharing in quantum networks. In this paper, we introduce a verifiable multiparty counterfactual quantum secret sharing (QSS) protocol, enhancing security and efficiency. This QSS protocol utilizes a low-depth quantum circuit to encrypt and decrypt information, which comprises a unitary operator constructed using a preshared secret key. To ensure the robustness and verifiability of the shared secret key, the protocol imposes constraints on the participants with the Chinese remainder theorem. The most significant advantage of our proposed QSS protocol is incorporating counterfactual communication, which considerably enhances the scheme’s security by enabling exchange-free information sharing among participants, thereby minimizing the risk of eavesdropping or intercept-and-resend attacks. Furthermore, we incorporate a weighted-threshold mechanism that provides flexibility, enabling diverse use cases to design security protocols for quantum networks. The security analysis of the counterfactual QSS protocol and its implementation on IBM Quantum computers reveals strong resilience to internal and external attacks, along with high efficiency and robustness, making it effective for quantum encryption in the noisy intermediate-scale quantum era.

This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP) and ergodic capacity (EC) frameworks, deriving exact expressions for both, along with asymptotic analysis in high signal-to-noise ratio (SNR) and low-rate regions, which serve as the foundation for deducing the approximate and maximal energy-reliability and energy-spectral formulas. To enhance system performance, we tackle the non-convex problems of jointly optimizing common and private power allocation (PA) coefficients to minimize the maximal OP performance. Moreover, we also delve into optimizing PA coefficients and rate-splitting factors concurrently to maximize the ergodic sum capacity (ESC). Addressing the influence of modulation schemes on user error rates, we introduce mathematical frameworks for symbol error rate (SER) considering four combined modulation schemes based on binary phase-shift keying (BPSK) and quadrature-phase shift keying (QPSK), the whole cases are quantified in terms of exact and asymptotic manners. Furthermore, we present a straightforward approach to optimize the PA coefficients to minimize the maximal SER performance. Finally, Monte-Carlo simulations are presented to validate our developed frameworks and optimization solutions.

In this article, we propose hybrid deep quantum-transformer networks (QTNs) to predict the optimal beam in integrated sensing and communication (ISAC) systems employing millimeter-wave (mmWave) band. In mobile applications, vehicle-to-infrastructure (V2I) communications at high frequency require large antenna arrays and narrow beams, which is associated with high-beam training overhead. In such a scenario, selecting an optimal beam to maximize the signal power at the receiver can be learned from the sensory data collected at the base station and guided by the position-based data provided by the user equipment. Such multimodal sensory data can be utilized by deep learning frameworks to create situational awareness for intelligently predicting optimal beams. We evaluate the proposed learning models in real-world V2I scenarios provided by the multimodal deepsense sixth generation data set and compare them with the existing works. The experimental results show a distance-based accuracy (DBA) score of 0.9124 for multimodal and 0.8832 for position-based data, respectively. Moreover, the hybrid QTN achieve the best DBA scores and the highest accuracy compared to other models on zero-shot testing. These QTN models exhibit low complexity and high performance, demonstrating their potential to address the challenges of beam management in mmWave ISAC systems.

This paper proposes a reconfigurable intelligent surface-aided two-way full-duplex communication with non-orthogonal multiple access transmission schemes to improve spectrum utilization. Besides, the joint impact of error phase-shift quantization, imperfect successive interference cancellation, and residual loop interference on the system performance have also been investigated. Under Nakagami-m fading channels, approximate closed-form expressions for the outage performance and the ergodic rate are derived. Through asymptotic analyses, some insights are achieved, including the diversity order, the coding gain, and the ergodic slope. Moreover, three adaptive power allocation optimization problems are formulated aiming to: 1) minimize outage probability, 2) achieve max-min rate fairness, and 3) maximize the user’s sum rate subject to the quality-of-service constraint. Simulation results not only validate the theoretical analyses and the optimal/sub-optimal solutions but also reveal three following observations. First, the considered system outperforms the orthogonal multiple access baseline. Second, increasing the number of control bits for the error phase-shift quantization and/or the number of RIS’s elements can significantly reduce the impact of imperfect successive interference cancellations. Third, employing one of three adaptive power allocation solutions improves the system’s performance significantly.

This paper investigates the performance of downlink rate-splitting multiple access (RSMA)-aided unmanned aerial vehicle (UAV) communication systems, wherein a multi-antenna UAV exploits RSMA to serve multiple ground users. Considering nonline-of-sight environments, double-shadowed scattering channel modeling is adopted to generically characterize the impacts of mobility and shadowing on UAV-to-ground communications, assuming imperfect successive interference cancellation (SIC). Besides, a unified precoder design is proposed to fully capture the benefits of multi-antenna paradigms. Closed-form expressions for the users' outage probability (OP) and ergodic capacity are derived. In addition, asymptotic analysis is carried out to get further insights into the system design, such as the diversity gain and ergodic slope. Numerical results are presented, and it is shown that: 1) the effects of double-shadowed scattering on the system outage performance can be significantly reduced by increasing the number of antennas installed at the UAV; 2) the imperfect SIC error can be minimized by properly optimizing the power allocation of the common stream; and 3) RSMA provides superior users' ergodic capacity compared to its orthogonal and non-orthogonal multiple access counterparts.

MOTIVATION: DNA-based data storage is one of the most attractive research areas for future archival storage. However, it faces the problems of high writing and reading costs for practical use. There have been many efforts to resolve this problem, but existing schemes are not fully suitable for DNA-based data storage, and more cost reduction is needed. RESULTS: We propose whole encoding and decoding procedures for DNA storage. The encoding procedure consists of a carefully designed single low-density parity-check code as an inter-oligo code, which corrects errors and dropouts efficiently. We apply new clustering and alignment methods that operate on variable-length reads to aid the decoding performance. We use edit distance and quality scores during the sequence analysis-aided decoding procedure, which can discard abnormal reads and utilize high-quality soft information. We store 548.83 KB of an image file in DNA oligos and achieve a writing cost reduction of 7.46% and a significant reading cost reduction of 26.57% and 19.41% compared with the two previous works. AVAILABILITY AND IMPLEMENTATION: Data and codes for all the algorithms proposed in this study are available at: https://github.com/sjpark0905/DNA-LDPC-codes.

This article studies short-packet communication (SPC) in multireconfigurable intelligent surface (RIS)-assisted multiantenna wireless systems. In this system, a sensor node communicates with another sensor node through the help of an access point (AP) and two sets of distributed RISs. Aiming to enhance system performance, we combine the best RIS selection strategies with maximum-ratio transmission (MRT) beamforming designs to improve the transmitted signal and selection combining (SC) or maximum-ratio combining (MRC) to increase the received signals. Closed-form expressions for the block error rate (BLER) throughput, latency, and reliability of the receivers over Rayleigh fading channels are derived to evaluate the system performance. Numerical results show that, in the first transmission phase, employing MRC with optimal phase shift (OPS) occurring at RIS can help AP achieve the best BLER performance, while SC with OPS provides better BLER performance than MRC and uncertain phase shift (UPS). In the second transmission phase, the reflective-path beamforming design shows better performance than the direct-path beamforming design, where OPS also attains outstanding performance when compared to UPS. Aiming for real-time system configurations with high reliability along with minimizing costs and resources, we propose a deep learning (DL) approach to optimize the number of reflective elements at each RIS or the number of RIS in a set of distributed RISs. Our work shows that the prediction results of the DL framework match the analytical derivations and the deep neural network (DNN) can help the systems save overhead and resources while satisfying the requirement of real-time communication.

In this paper, we propose a newly constructed binary code to correct one deletion and one insertion error that simultaneously occur at any position in a codeword. We first investigated all cases of one deletion and one insertion error. Three constraints in the proposed construction were used for determining the bit values and positions of all error cases. Furthermore, we propose the decoding procedure with two parallel decoding algorithms and explain the specific decoding procedure. To the best of the authors’ knowledge, our work is the first trial to provide an efficient code construction design to correct one deletion and one insertion error occurring in a codeword.

In this article, we propose a cooperative nonorthogonal multiple access (NOMA)-enabled simultaneous wireless information and power transfer (SWIPT) Internet of Things (IoT) networks, where one information source harvests energy from a multiantennas power beacon (PB) to serve two IoT users via the help of multiple energy-limited relay nodes. To improve the performance of far IoT user, we propose reactive and proactive relay selection protocols together with time-power energy harvesting mechanism under imperfect successive interference cancellation. Closed-form expressions for the outage probability (OP), throughput, and energy efficiency (EE) of the proposed system are obtained, from which the asymptotic analysis for the throughput is also carried out. To further enhance the system performance, we propose a low-complexity method to optimize the outage and throughput performance subject to power allocation, time, and power splitting parameters. Toward real-time configurations in IoT networks, we design a deep learning framework for the sum-throughput and EE predictions with low computation complexity and high accuracy. The influences of antennas setting at PB, time-switching ratio, power-splitting ratio, power allocation factor, and the number of relays on the system OP, throughput, and EE are evaluated and discussed along with numerical results.

In this article, we study novel wireless powered cognitive nonorthogonal multiple access (NOMA)-based Internet-of-Things (IoT) relay networks to improve the performance of a cell-edge user under perfect and imperfect successive interference cancelation (SIC). In the secondary networks, a source node communicates with a cell-center user via direct link and with a cell-edge user through the assistance of a master IoT node under cognitive radio constraint. Exact closed-form analytical expressions for the outage probability (OP) of NOMA users and the overall system throughput are derived. To provide further insights, a performance floor analysis is also carried out considering two power-setting scenarios: 1) the transmit powers at the power beacon goes to infinity and 2) the maximum allowable power constraint goes to infinity. Moreover, we develop two iterative algorithms for minimizing OP users and maximizing system throughput subject to time-switching and power-allocation factors in two-hop transmission. Direct derivation of the closed-form expression for the ergodic capacity (EC) becomes unfeasible due to the high complexity of the proposed system model. To overcome this issue, we design a deep neural network (DNN) framework for the EC prediction toward real-time configurations. Our results show that the predicted results based on this DNN framework perfectly align with the simulations, validating our design framework. In addition, the DNN approach exhibits the lowest root-mean-square error and low run-time predictions among other regression models.

In this article, we study wireless-powered cognitive nonorthogonal multiple access (NOMA) Internet of Things (IoT) networks with short-packet communications to improve spectrum utilization and sustainability, as well as reduce the latency under imperfect channel state information (CSI) and successive interference cancelation (SIC). For performance evaluation, closed-form expressions for the block error rate (BLER) of the NOMA users, goodput, energy efficiency, latency, and reliability are derived. To gain some further insights into the system design, two scenarios can be taken into account for the positions of the primary receivers: 1) they are located near the secondary network and 2) they are located far away from the secondary network. Moreover, we propose an effective algorithm to minimize the BLERs of the NOMA users by optimizing power allocation coefficients. In addition, a novel multi-output deep-learning (DL) framework is designed to simultaneously predict the BLERs and goodputs of users towards real-time configurations for IoT systems. Numerical results show the outstanding performance of the proposed system over the orthogonal multiple access (OMA) one in terms of the BLER and goodput. Moreover, the proposed system achieves a lower latency and higher reliability compared to the long packet communications under the same channel settings. Furthermore, our designed multioutput DL also exhibits the lowest error performance and a short run-time prediction compared to the other multioutput regression models, while the predicted results using the DL model are almost matched with the simulation ones.

This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users’ constraints. Our analysis assumes imperfect channel state information and successive interference cancellation under scenarios with and without line-of-sight (LoS) link between source and users. We derive exact closed-form expressions for the outage probability, throughput, and an upper bound for the ergodic capacity (EC). To provide further insights, an asymptotic analysis is carried out by considering two power settings at the source. It is also determined the optimal data rate factors of all users that maximize the system throughput. In addition, a deep learning framework (DLF) for EC prediction is designed. Numerical results show that: i) compared to the system without LoS link, the performance of the proposed system with LoS link can significantly improve when the number of reflecting elements at the RIS increases, and ii) the proposed system has superior performance compared to its orthogonal multiple access counterpart. Furthermore, our proposed DLF exhibits the lowest root-mean-square error and low execution-time among other approaches, verifying the effectiveness of this method for future analysis.

MOTIVATION: In DNA storage systems, there are tradeoffs between writing and reading costs. Increasing the code rate of error-correcting codes may save writing cost, but it will need more sequence reads for data retrieval. There is potentially a way to improve sequencing and decoding processes in such a way that the reading cost induced by this tradeoff is reduced without increasing the writing cost. In past researches, clustering, alignment and decoding processes were considered as separate stages but we believe that using the information from all these processes together may improve decoding performance. Actual experiments of DNA synthesis and sequencing should be performed because simulations cannot be relied on to cover all error possibilities in practical circumstances. RESULTS: For DNA storage systems using fountain code and Reed-Solomon (RS) code, we introduce several techniques to improve the decoding performance. We designed the decoding process focusing on the cooperation of key components: Hamming-distance based clustering, discarding of abnormal sequence reads, RS error correction as well as detection and quality score-based ordering of sequences. We synthesized 513.6 KB data into DNA oligo pools and sequenced this data successfully with Illumina MiSeq instrument. Compared to Erlich's research, the proposed decoding method additionally incorporates sequence reads with minor errors which had been discarded before, and thus was able to make use of 10.6-11.9% more sequence reads from the same sequencing environment, this resulted in 6.5-8.9% reduction in the reading cost. Channel characteristics including sequence coverage and read-length distributions are provided as well. AVAILABILITY AND IMPLEMENTATION: The raw data files and the source codes of our experiments are available at: https://github.com/jhjeong0702/dna-storage.

In this article, we investigate power beacon (PB)-assisted wireless-powered nonorthogonal multiple access (NOMA) Internet-of-Things (IoT)-based systems, where all transmitters harvest energy from a PB to transmit their signals to a destination by employing a time-splitting mechanism. In order to utilize spectral efficiency and enhance the quality of service of an edge user (EU), a cellular base station can communicate with the EU thank to the help of one IoT node in a cellular network by employing the NOMA protocol. To characterize the performance of the proposed systems, we derive the exact closed-form expression for the outage probability, throughput, energy efficiency, and the approximate closed-form expression for the ergodic capacity. To further improving the system performance, we present two algorithms that are to minimize the outage performance of users by optimizing the time-splitting factor and to maximize sum throughput via jointly optimal power allocation and time-spitting factor. Our numerical results show that the proposed system has outstanding performance in comparison with simultaneous wireless information and power transfer NOMA systems under time switching mechanism based on the IoT relay.

In this paper, we investigate an underlay cognitive non-orthogonal multiple access (NOMA)-based coordinated direct and relay transmission network with imperfect successive interference cancellation, imperfect channel state information, and co-channel interference caused by a multi-antenna primary transmitter. In the secondary network, a source communicates with a near user via direct link and with a far user through the assistance of multiple relays subject to transmit power constraints. Four relay selection schemes are proposed to enhance the performance of NOMA users and the overall system throughput. In our analysis, exact closed-form expressions for the outage probability (OP) of NOMA users and for the overall system throughput are derived. To provide further insights, a performance floor analysis is carried out considering two power-setting scenarios: (i) the transmit powers at the secondary source and relays go to infinity and (ii) the peak interference constraint goes to infinity. Towards real-time configurations, we also design a deep learning (DL) framework for the OP and system throughput prediction. Our results show that the deep neural network exhibits the lowest run-time prediction and root-mean-square error among the proposed DL models. Furthermore, the predicted results based on DL framework match with those of the analysis and simulation.

In this paper, we investigate pilot power allocation in multi-cell multi-user massive MIMO systems for enhancing channel estimation quality. it is known that, with good channel estimation quality, we can detect signals more accurately. To improve the throughput, instead of focusing directly on spectral efficiency (SE) of the system, we focus on improving the channel estimation quality by creating a closed-form formula for the normalized mean squared error (NMSE) between true and estimated channel. We consider well-known minimum mean squared error (MMSE) channel estimation method. After that, we propose a minimization problem to minimize the summation of estimation error in the system with constrain of a maximum pilot power per user. The optimization problem for is nondeterministic polynomial-time hard and it is very hard to find the global solution, so that we propose an algorithm to find the local optimum solution with polynomial time. Numerical results show the advantages of our proposed approach in channel estimation quality, sum SE of the system, and minimum SE in the system.

In this study, the authors investigate the issue of pilot power allocation in multi‐cell multi‐user massive multiple‐inputmultiple‐output (MIMO) systems to maximize the sum rate. In contrast to conventional scheme that assigns equal pilot power for all users in the system, they assume that different users can be assigned different pilot powers while the total pilot power per cell is fixed. They show that when the number of BS antennas goes to infinity, the signal‐to‐interference‐and‐noise ratio of a user only depends on the large‐scale fading and pilot power of users who have the same pilot sequence. From that they derive an optimization problem to maximize the total uplink achievable rate of a target cell and prove that this problem is convex, which can be solved by the well‐known Lagrange multiplier method. They also propose an extended optimization problem that solves the issue of zero‐pilot power in the original problem. Eventually, two algorithms corresponding to the original and extended optimization problems are proposed to obtain the optimized pilot power set for all users in the systems. Numerical results show that the proposed algorithms outperform the existing methods relating to pilot power allocation problem as well as conventional scheme.

Relay selection in cooperative communication is a crucial task for achieving the spatial diversity since the improper relay selection can decrease the overall capacity of the network. In this study, the authors use a reinforcement learning technique, called as Q ‐learning (QL), to solve the relay selection problem. They propose a ‘QL‐based relay selection algorithm’ (QL‐RSA) for wireless cooperative networks that maximises the total capacity of the network. QL‐RSA receives the reward (feedback) in terms of the capacity by learning a multi‐node amplify‐and‐forward cooperative environment with time‐varying Rayleigh fading channels. The advantages of QL‐RSA are that it is less complex, requires less channel feedback information and it is distributed in a multiple‐sources environment as it provides each source a self‐learning capability to find the optimal relay without exchanging information with other source nodes.

In this study, a concatenated coding scheme based on Reed–Muller (RM) codes and bit‐extension codes is proposed for equivocation of a wiretap channel. RM codes and their cosets are adopted for message encoding, and bit‐extension codes are used to enhance the equivocation capability for a wiretapper's channel. The average equivocation is discussed when only RM codes are used in the system, and the probability causing imperfect secrecy is also determined. Analytical results show that the proposed code can be used for the equivocation capability of wiretap channels and suggest a proper management over a wiretap channel.

In this study, the authors propose an equivocation scheme for wiretap channels, which is composed of bit‐extension mapping, coset coding and hybrid automatic repeat request (HARQ). The inner bit‐extension code and outer coset code are used for equivocation of a wiretapper channel, whereas the HARQ scheme is to mitigate noisy errors in a main legitimate channel. These concatenated codes and HARQ are effective and practical for various channel conditions. The average equivocation and the probability of causing imperfect secrecy are analysed for finite codeword lengths. As a function of channel conditions, they investigate the block error rate at the legitimate receiver and the information leakage to the wiretapper. From simulation results, they further determine the minimum requirements of code design for some target values of the ‘residual’ block error rate and information leakage at maximum retransmission.

This paper studies dirty-paper coding in a Gaussian broadcast channel with two receivers. It finds that an approximate version of dirty-paper coding using low-density lattices can be implemented with a complexity that is polynomial-time on average in the block length. The main difference between this paper and prior work is that a non-binary LDPC-based lattice codebook is used for each user, and one codebook is aligned with the other. The low-density nature enables tractable encoding and decoding algorithms, and the alignment gives structure to the overall signal transmitted and it enables us to perform the encoding and decoding efficiently.1

In this paper, we analyze the sequential message- passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results indicate that as the number of partitioned subsets of check nodes increases, the BER performance is improved. We also derive the recursive equations for mean values of messages at check and variable nodes by using density evolution with a Gaussian approximation. From these equations, the mean values are obtained at each iteration of the sequential decoding algorithm and the corresponding BER values are calculated. They show that the sequential decoding algorithm converges faster than the conventional one. Finally, the analytical results are confirmed by the simulation results.

A quasi-cyclic (QC) low-density parity-check (LDPC) code can be viewed as the protograph code with circulant permutation matrices. In this paper, we find all the subgraph patterns of protographs of QC LDPC codes having inevitable cycles of length 2i,i= 6,7,8,9,10, i.e., the cycles existing regardless of the shift values of circulants. It is also derived that if the girth of the protograph is 2g,gges 2, its protograph code cannot have the inevitable cycles of length smaller than 6g. Based on these subgraph patterns, we propose new combinatorial construction methods of the protographs, whose protograph codes can have girth larger than or equal to 14.

A quasi-cyclic (QC) low-density parity-check (LDPC) code can be viewed as the protograph code with circulant permutation matrices (or circulants). In this correspondence, we find all the subgraph patterns of protographs of QC LDPC codes having inevitable cycles of length 2i, i = 6, 7, 8, 9,10, i.e., the cycles that always exist regardless of the shift values of circulants. It is also derived that if the girth of the protograph is 2g, g > 2, its protograph code cannot have the inevitable cycles of length smaller than 6g. Based on these subgraph patterns, we propose new combinatorial construction methods of the protographs, whose protograph codes can have girth larger than or equal to 14 or 18. We also propose a couple of shift value assigning rules for circulants of a QC LDPC code guaranteeing the girth 14.

In this correspondence, the cycles of Tanner (3,5) quasi-cyclic (QC) low-density parity-check (LDPC) codes are analyzed and their girth values are derived. The conditions for the existence of cycles of lengths 4,6,8, and 10 in Tanner (3,5) QC LDPC codes of length 5p are expressed in terms of polynomial equations in a 15th root of unity of the prime field F/sub p/. By checking the existence of solutions for these equations over F/sub p/, the girths of Tanner (3,5) QC LDPC codes are derived.

In this paper, the cycles of Tanner's (3,5) quasicyclic (QC) low-density parity-check (LDPC) codes are analyzed and their girth values are derived. The conditions for the existence of cycles of lengths 4, 6, 8, and 10 in Tanner's (3,5) QC LDPC codes of length 5p are expressed in terms of polynomial equations in a 15-th root of unity of the prime field Fp. By checking the existence of solutions for these equations over Fp, the girths of Tanner's (3,5) QC LDPC codes are derived