Acquiring accurate channel state information (CSI) is critical for reliable and efficient wireless communication, but challenges such as high pilot overhead and channel aging hinder timely and accurate CSI acquisition. CSI prediction, which forecasts future CSI from historical observations, offers a promising solution. Recent deep learning approaches, including recurrent neural networks and Transformers, have achieved notable success but typically learn deterministic mappings, limiting their ability to capture the stochastic and multimodal nature of wireless channels. In this paper, we propose a novel CSI prediction scheme based on diffusion models. We decompose the CSI prediction task into two components: a temporal encoder, which extracts channel dynamics, and a diffusion-based generator, which produces future CSI samples autoregressively. Extensive simulations demonstrate that our diffusion-based models significantly outperform state-of-the-art baselines.

Acquiring accurate channel state information (CSI) is critical for reliable and efficient wireless communication, but challenges such as high pilot overhead and channel aging hinder timely and accurate CSI acquisition. CSI prediction, which forecasts future CSI from historical observations, offers a promising solution. Recent deep learning approaches, including recurrent neural networks and Transformers, have achieved notable success but typically learn deterministic mappings, limiting their ability to capture the stochastic and multimodal nature of wireless channels. In this paper, we introduce a novel probabilistic framework for CSI prediction based on diffusion models, offering a flexible design that supports integration of diverse prediction schemes. We decompose the CSI prediction task into two components: a temporal encoder, which extracts channel dynamics, and a diffusion-based generator, which produces future CSI samples. We investigate two inference schemes—autoregressive and sequence-to-sequence—and explore multiple diffusion backbones, including U-Net and Transformer-based architectures. Furthermore, we examine a diffusion-based approach without an explicit temporal encoder and utilize the DDIM scheduling to reduce model complexity. Extensive simulations demonstrate that our diffusion-based models significantly outperform state-of-the-art baselines.

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing an LO across APs can reduce costs in specific configurations but cause correlated PN between APs, leading to correlated interference that affects centralized combining. This can be improved by exploiting the PN correlation in channel estimation. This paper presents an uplink orthogonal frequency division multiplexing (OFDM) signal model for PN-impaired cell-free mMIMO, addressing gaps in single-carrier signal models. We evaluate mismatches from applying single-carrier methods to OFDM systems, showing how they underestimate the impact of PN and produce over-optimistic achievable SE predictions. Based on our OFDM signal model, we propose two PN-aware channel and common phase error estimators: a distributed estimator for uncorrelated PN with separate LOs and a centralized estimator with shared LOs. We introduce a deep learning-based channel estimator to enhance the performance and reduce the number of iterations of the centralized estimator. The simulation results show that the distributed estimator outperforms mismatched estimators with separate LOs, whereas the centralized estimator enhances distributed estimators with shared LOs.

Standard decoding approaches for convolutional codes, such as the Viterbi and BCJR algorithms, entail significant complexity when correcting synchronization errors. The situation worsens when multiple received sequences should be jointly decoded, as in DNA storage. Previous work has attempted to address this via separate-BCJR decoding, i.e., combining the results of decoding each received sequence separately. Another attempt to reduce complexity adapted sequential decoders for use over channels with insertion and deletion errors. However, these decoding alternatives remain prohibitively expensive for high-rate convolutional codes. To address this, we adapt sequential decoders to decode multiple received sequences jointly over the syndrome trellis. For the short blocklength regime, this decoding strategy can outperform separate-BCJR decoding under certain channel conditions, in addition to reducing decoding complexity. To mitigate the occurrence of a decoding timeout, formally called erasure, we also extend this approach to work bidirectionally, i.e., deploying two independent stack decoders that simultaneously operate in the forward and backward directions.

We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where inter-connections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or embeddings among clients. Instead, it leverages explicit global graph structure information to capture inter-node dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients.

In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the "masked" local models to be aggregated. This raises a problem when working with real-valued data, as there is no measure on the reals that is invariant under the masking operation, and hence information leakage is bound to occur. Shifting the data to a finite field circumvents this problem, but as a downside runs into an inherent accuracy–complexity tradeoff issue due to fixed-point modular arithmetic as opposed to floating-point numbers that can simultaneously handle numbers of varying magnitudes. In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field. This approach guarantees perfect privacy for each party’s data by utilizing the uniform distribution on the torus, while avoiding accuracy losses. Experimental results show that the new protocol performs similarly to the model without secure aggregation while maintaining perfect privacy. Compared to the finite field secure aggregation, the torus-based protocol can in some cases significantly outperform it in terms of model accuracy and cosine similarity, hence making it a safer choice.

We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the common scenario of interconnected subgraphs, where interconnections between clients significantly influence the learning process. Existing approaches suffer from critical limitations, either requiring the exchange of sensitive node embeddings, thereby posing privacy risks, or relying on computationally-intensive steps, which hinders scalability. To tackle these challenges, we propose FedLap, a novel framework that leverages global structure information via Laplacian smoothing in the spectral domain to effectively capture inter-node dependencies while ensuring privacy and scalability. We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy. Notably, FedLap is the first subgraph FL scheme with strong privacy guarantees. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive or superior utility compared to existing techniques.

We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sabrayolles et al., we derive the Bayes-optimal membership inference rule for node-level MIAs against graph neural networks, addressing key open questions about optimal query strategies in the graph setting. We introduce BASE and G-BASE, tractable approximations of the Bayes-optimal membership inference. G-BASE achieves superior performance compared to previously proposed classifier-based node-level MIA attacks. BASE, which is also applicable to non-graph data, matches or exceeds the performance of prior state-of-the-art MIAs, such as LiRA and RMIA, at a significantly lower computational cost. Finally, we show that BASE and RMIA are equivalent under a specific hyperparameter setting, providing a principled, Bayes-optimal justification for the RMIA attack.

We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian approach, we derive analytically the average AoI. We further provide an approximate analysis for accurate and easy-to-compute approximations of both the average AoI and the age-violation probability (AVP), i.e., the probability that the AoI exceeds a given threshold. We also analyze the average throughput. Via numerical results, we investigate two baseline strategies: transmit a new update whenever possible to exploit every opportunity to reduce the AoI, and transmit only when sufficient energy is available to increase the chance of successful decoding. The two strategies are beneficial for low and high update-generation rates, respectively. We show that an optimized policy that balances the two strategies outperforms them significantly in terms of both AoI metrics and throughput. Finally, we show the benefit of decoding multiple packets in a slot using successive interference cancellation and adapting the transmission probability based on both the current battery level and the time elapsed since the last transmission.

Federated learning (FL) has emerged as a promising approach for collaboratively training machine learning models while preserving data privacy. Due to its decentralized nature, FL is vulnerable to poisoning attacks, where malicious clients compromise the global model through altered data or updates. Identifying such malicious clients is crucial for ensuring the integrity of FL systems. This task becomes particularly challenging under privacy-enhancing protocols such as secure aggregation, creating a fundamental trade-off between privacy and security. In this work, we propose FedGT, a novel framework designed to identify malicious clients in FL with secure aggregation while preserving privacy. Drawing inspiration from group testing, FedGT leverages overlapping groups of clients to identify the presence of malicious clients via a decoding operation. The clients identified as malicious are then removed from the model training, which is performed over the remaining clients. By choosing the size, number, and overlap between groups, FedGT strikes a balance between privacy and security. Specifically, the server learns the aggregated model of the clients in each group—vanilla federated learning and secure aggregation correspond to the extreme cases of FedGT with group size equal to one and the total number of clients, respectively. The effectiveness of FedGT is demonstrated through extensive experiments on three datasets in a cross-silo setting under different data-poisoning attacks. These experiments showcase FedGT’s ability to identify malicious clients, resulting in high model utility. We further show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al. and the robust aggregation technique Multi-Krum in multiple settings.

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of power PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels and with antenna crosstalk. We propose a novel low-complexity FD convolutional neural network (CNN) DPD. We also propose a learning algorithm for any FD-DPDs with differentiable structure. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable. The proposed learning algorithm allows FD-DPDs to outperform TD-DPD optimized by indirect learning architecture under antenna crosstalk.

In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-based QLDPC decoders, the proposed GNN-based QLDPC decoder exploits the sparse graph structure of QLDPC codes and can be implemented as a message-passing decoding algorithm. We compare the proposed GNN-based decoding algorithm against selected classes of both conventional and neural–enhanced QLDPC decoding algorithms across several QLDPC code designs. The simulation results demonstrate excellent performance of GNN-based decoders along with their low complexity compared to competing methods.

We recently proposed a quantitative group testing (GT) scheme with low-complexity peeling decoding based on low-density parity-check (LDPC) codes. Based on finite length simulations and a density evolution analysis we were able to demonstrate that simple$(d_{\mathrm{v}},d_{\mathrm{c}})$-regular LDPC codes can be more efficient for GT than existing generalized LDPC (GLDPC) code constructions based on BCH component codes. Even larger gains were numerically observed in combination with spatial coupling. In this paper, we use vector admissible systems to prove threshold saturation and compute the corresponding potential thresholds.

Quantum low-density parity-check (QLDPC) codes have emerged as a promising technique for quantum error correction. A variety of decoders have been proposed for QLDPC codes and many utilize belief propagation (BP) decoding in some fashion. However, the use of BP decoding for degenerate QLDPC codes is known to have issues with convergence. These issues are typically attributed to short cycles in the Tanner graph and error patterns with the same syndrome due to code degeneracy. In this work, we propose a decoder for QLDPC codes based on BP guided decimation (BPGD), which has been previously studied for constraint satisfaction and lossy compression problems. This decimation process is applicable to both binary and quaternary BP and it involves sequentially freezing the value of the most reliable qubits to encourage BP convergence. We find that BPGD significantly reduces the BP failure rate due to non-convergence, achieving performance on par with BP with ordered statistics decoding and BP with stabilizer inactivation, without the need to solve systems of linear equations. To explore how and why BPGD improves performance, we discuss several interpretations of BPGD and their connection to BP syndrome decoding.

Local mutual-information privacy (LMIP) is a privacy notion that aims to quantify the reduction of uncertainty about the input data when the output of a privacy-preserving mechanism is revealed. We study the relation of LMIP with local differential privacy (LDP)-the de facto standard notion of privacy in context-independent scenarios-, and with local information privacy (LIP)-the state-of-the-art notion for context-dependent settings. We establish explicit conversion rules, i.e., bounds on the privacy parameters for a LMIP mechanism to also satisfy LDPILIP, and vice versa. We use our bounds to formally verify that LMIP is a weak privacy notion. We also show that uncorrelated Gaussian noise is the best-case noise in terms of context-independent LMIP if both the input data and the noise are subject to an average power constraint.

We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, we assume that a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.

We examine the age of information (AoI) of a status update system that incorporates energy harvesting and uses the slotted ALOHA protocol. We derive analytically the average AoI and the probability that the AoI exceeds a given threshold. Via numerical results, we investigate two strategies to minimize the age of information (AoI): transmitting a new update whenever possible to exploit every chance to reduce the AoI, and transmitting only when sufficient energy is available to increase the chance of successful delivery. The two strategies are beneficial for low and high update generation rates, respectively. However, an optimized approach that balances the two strategies outperforms them significantly in terms of both AoI and throughput.

Cell-Free massive MIMO networks provide huge power gains and resolve inter-cell interference by coherent processing over a massive number of distributed instead of colocated antennas in access points (APs). Cost-efficient hardware is preferred but imperfect local oscillators in both APs and users introduce multiplicative phase noise (PN), which affects the phase coherence between APs and users even with centralized processing. In this paper, we first formulate the system model of a PN- impaired uplink Cell-Free massive MIMO orthogonal frequency division multiplexing network, and then propose a PN-aware linear minimum mean square error channel estimator and derive a PN- impaired uplink spectral efficiency expression. Numerical results are used to quantify the spectral efficiency gain of the proposed channel estimator over alternative schemes for different receiving combiners.

We propose an irregular repetition slotted ALOHA (IRSA) based random-access protocol for the binary adder channel (BAC). The BAC captures important physical-layer concepts, such as packet generation, per-slot decoding, and information rate, which are neglected in the commonly considered collision channel model. We divide a frame into slots and let users generate a packet, to be transmitted over a slot, from a given codebook. In a state-of-the-art scheme proposed by Paolini et al. (2022), the codebook is constructed as the parity-check matrix of a BCH code. Here, we construct the codebook from independent and identically distributed binary symbols to obtain a random-coding achievability bound. Our per-slot decoder progressively discards incompatible codewords from a list of candidate codewords, and can be improved by shrinking this list across iterations. In a regime of practical interests, our scheme can resolve more colliding users in a slot and thus achieves a higher average sum rate than the scheme in Paolini et al. (2022).

Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios, the transmission process may be interrupted before receiving the complete codeword. This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received. Using particular dropout strategies applied during the training process, rateless AE codes allow to trade off between decoding delay and reliability, providing a graceful improvement of the latter with each additionally received codeword symbol. The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.

A non-adaptive quantitative group testing (GT) scheme based on sparse codes-on-graphs in combination with low-complexity peeling decoding was introduced and analyzed by Karimi et al.. In this work, we propose a variant of this scheme based on low-density parity-check codes where the BCH codes at the constraint nodes are replaced by simple single parity-check codes. Furthermore, we apply spatial coupling to both GT schemes, perform a density evolution analysis, and compare their performance with and without coupling. Our analysis shows that both schemes improve with increasing coupling memory, and for all considered cases, it is observed that the LDPC code-based scheme substantially outperforms the original scheme. Simulation results for finite block length confirm the asymptotic density evolution thresholds.

The errors occurring in DNA-based storage are correlated in nature, which is a direct consequence of the synthesis and sequencing processes. In this paper, we consider the memory-k nanopore channel model recently introduced by Hamoum et al., which models the inherent memory of the channel. We derive the maximum a posteriori (MAP) decoder for this channel model. The derived MAP decoder allows us to compute achievable information rates for the true DNA storage channel assuming a mismatched decoder matched to the memory-k nanopore channel model, and quantify the loss in performance assuming a small memory length—and hence limited decoding complexity. Furthermore, the derived MAP decoder can be used to design error-correcting codes tailored to the DNA storage channel. We show that a concatenated coding scheme with an outer low-density parity-check code and an inner convolutional code yields excellent performance.

We consider error-correcting coding for DNA-based storage. We model the DNA storage channel as a multi-draw IDS channel where the input data is chunked into M short DNA strands, which are copied a random number of times, and the channel outputs a random selection of N noisy DNA strands. The retrieved DNA strands are prone to insertion, deletion, and substitution (IDS) errors. We propose an index-based concatenated coding scheme consisting of the concatenation of an outer code, an index code, and an inner synchronization code, where the latter two tackle IDS errors. We further propose a mismatched joint index-synchronization code maximum a posteriori probability decoder with optional clustering to infer symbolwise a posteriori probabilities for the outer decoder. We compute achievable information rates for the outer code and present Monte-Carlo simulations for information-outage probabilities and frame error rates on synthetic and experimental data, respectively.

We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices’ data across the network. Compared to other schemes in the literature, which deal with stragglers or device dropouts by ignoring their contribution, the proposed schemes do not suffer from the client drift problem. The first scheme, CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy level of conventional FL. It combines one-time padding for user data privacy with gradient codes to yield straggler resiliency. The second scheme, CodedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir’s secret sharing. We apply CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with 120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash et al. without the shortcoming of additional leakage of private data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme LightSecAgg by a speed-up factor of 6.6–18.7 for the MNIST dataset for an accuracy of 95%.

We consider straggler-resilient learning. In many previous works, e.g., in the coded computing literature, straggling is modeled as random delays that are independent and identically distributed between workers. However, in many practical scenarios, a given worker may straggle over an extended period of time. We propose a latency model that captures this behavior and is substantiated by traces collected on Microsoft Azure, Amazon Web Services (AWS), and a small local cluster. Building on this model, we propose DSAG, a mixed synchronous-asynchronous iterative optimization method, based on the stochastic average gradient (SAG) method, that combines timely and stale results. We also propose a dynamic load-balancing strategy to further reduce the impact of straggling workers. We evaluate DSAG for principal component analysis, cast as a finite-sum optimization problem, of a large genomics dataset, and for logistic regression on a cluster composed of 100 workers on AWS, and find that DSAG is up to about 50% faster than SAG, and more than twice as fast as coded computing methods, for the particular scenario that we consider.

A product code with single parity-check component codes can be described via the tools of a multi-kernel polar code, where the rows of the generator matrix are chosen according to the constraints imposed by the product code construction. Following this observation, successive cancellation decoding of such codes is introduced. In particular, the error probability of single parity-check product codes over binary memoryless symmetric channels under successive cancellation decoding is characterized. A bridge with the analysis of product codes introduced by Elias is also established for the binary erasure channel. Successive cancellation list decoding of single parity-check product codes is then described. For the provided example, simulations over the binary input additive white Gaussian channel show that successive cancellation list decoding outperforms belief propagation decoding applied to the code graph. Finally, the performance of the concatenation of a product code with a high-rate outer code is investigated via distance spectrum analysis. Examples of concatenations performing within 0.7 dB from the random coding union bound are provided.

Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer nonbinary low-density parity-check code or a polar code and either an inner convolutional code or a time-varying block code. We propose two novel decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences and has a complexity that is linear with the number of sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence. In addition, we succeed in improving the performance of the aforementioned coding scheme by optimizing both the inner and outer codes.

To account for the massive uncoordinated random access scenario, which is relevant for the Internet of Things, Polyanskiy et al. (2017) proposed a novel formulation of the multiple-access problem, commonly referred to as unsourced multiple access, where all users employ a common codebook and the receiver decodes up to a permutation of the messages. In this paper, we extend this seminal work to the case where the number of active users is random and unknowna priori. We define a random-access code accounting for both misdetection (MD) and false alarm (FA), and derive a random-coding achievability bound for the Gaussian multiple access channel. Our bound captures the fundamental trade-off between MD and FA probabilities. It suggests that the lack of knowledge of the number of active users entails a small penalty in energy efficiency when the target MD and FA probabilities are high. However, as the target MD and FA probabilities decrease, the energy efficiency penalty becomes more significant. For example, in a typical IoT scenario with framelength 19200 complex channel uses and 25–300 active users in average, the required energy per bit to achieve both MD and FA probabilities below$10^{-1}$, predicted by our bound, is only 0.5–0.7 dB higher than that predicted by the bound in Polyanskiy et al. (2017) for a known number of active users. This gap increases to 3–4 dB when the target MD probability and/or FA probability is below$10^{-3}$. Taking both MD and FA into account, we use our bound to benchmark the energy efficiency of slotted-ALOHA with multi-packet reception, of a decoder that simply treats interference as noise, and of some recently proposed unsourced multiple access schemes. Numerical results suggest that, when the target MD and FA probabilities are high, it is effective to estimate the number of active users, then treat this estimate as the true value, and use a coding scheme that performs well for the case of known number of active users. However, this approach becomes energy inefficient when the requirements on MD and FA probabilities are stringent.

We propose four finite-length scaling laws to predict the frame error rate (FER) performance in the waterfall region of spatially-coupled low-density parity-check code ensembles under full belief propagation (BP) decoding with a limit on the number of decoding iterations and a scaling law for sliding window decoding, also with limited iterations. The laws for full BP decoding provide a choice between accuracy and computational complexity; a good balance between them is achieved by the law that models the number of decoded bits after a certain number of BP iterations by a time-integrated Ornstein-Uhlenbeck process. This framework is developed further to model sliding window decoding as a race between the integrated Ornstein-Uhlenbeck process and an absorbing barrier that corresponds to the left boundary of the sliding window. The proposed scaling laws yield accurate FER predictions for the semi-structured code ensembles proposed by Olmos and Urbanke.

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (sPAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).

With the rapid expansion of the Internet of Things, the efficient sharing of the wireless medium by a large amount of simple transmitters is becoming essential. Scheduling-based solutions are inefficient for this setting, where small data units are broadcast sporadically by terminals that most of the time are idle. Modern random access has embraced the challenge and provides suitable slot-synchronous and asynchronous multiple access solutions based on replicating the packets and exploiting successive interference cancellation (SIC) at the receiver. In this work, we focus on asynchronous modern random access. Specifically, we derive an analytical approximation of the performance of irregular repetition ALOHA (IRA) in the so-called error floor region. Numerical results show the tightness of the derived approximation under various scenarios.

We present a novel coded federated learning (FL) scheme for linear regression that mitigates the effect of straggling devices while retaining the privacy level of conventional FL. The proposed scheme combines one-time padding to preserve privacy and gradient codes to yield resiliency against stragglers and consists of two phases. In the first phase, the devices share a one-time padded version of their local data with a subset of other devices. In the second phase, the devices and the central server collaboratively and iteratively train a global linear model using gradient codes on the one-time padded local data. To apply one-time padding to real data, our scheme exploits a fixed-point arithmetic representation of the data. Unlike the coded FL scheme recently introduced by Prakash et al., the proposed scheme maintains the same level of privacy as conventional FL while achieving a similar training time. Compared to conventional FL, we show that the proposed scheme achieves a training speed-up factor of 6.6 and 9.2 on the MNIST and Fashion-MNIST datasets for an accuracy of 95% and 85%, respectively.

We show that spatially coupled low-density parity-check (LDPC) codes yield robust performance over changing intersymbol interfere (ISI) channels with optimal and suboptimal detectors. We compare the performance with classical LDPC code design which involves optimizing the degree distribution for a given (known) channel. We demonstrate that these classical schemes, despite working very good when designed for a given channel, can perform poorly if the channel is exchanged. With spatially coupled LDPC codes, however, we get performances close to the symmetric information rates with just a single code, without the need to know the channel and adapt to it at the transmitter. We also investigate threshold saturation with the linear minimum mean square error (LMMSE) detector and show that with spatial coupling its performance can get remarkably close to that of an optimal detector for regular LDPC codes.

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio.

We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their data with the faster ones and do not participate in the learning process. The proposed scheme employs code-based cryptography to ensure computational privacy of the private data, i.e., no device with bounded computational power can obtain information about the other devices’ data in feasible time. For a scenario with 25 devices, the proposed scheme achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively, for an accuracy of 95% on the MNIST dataset compared with conventional mini-batch FL.

We consider a mobile edge computing scenario where a number of devices want to perform a linear inference${W}{x} $on some local data$ {x}$given a network-side matrix$ {W}$. The computation is performed at the network edge over a number of edge servers. We propose a coding scheme that provides information-theoretic privacy against$z$colluding (honest-but-curious) edge servers, while minimizing the overall latency—comprising upload, computation, download, and decoding latency—in the presence of straggling servers. The proposed scheme exploits Shamir’s secret sharing to yield data privacy and straggler mitigation, combined with replication to provide spatial diversity for the download. We also propose two variants of the scheme that further reduce latency. For a considered scenario with 9 edge servers, the proposed scheme reduces the latency by 8% compared to the nonprivate scheme recently introduced by Zhang and Simeone, while providing privacy against an honest-but-curious edge server.

neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity.

A new class of spatially-coupled turbo-like codes (SC-TCs), dubbed generalized spatially coupled parallel concatenated codes (GSC-PCCs), is introduced. These codes are constructed by applying spatial coupling on parallel concatenated codes (PCCs) with a fraction of information bits repeated$q$times. GSC-PCCs can be seen as a generalization of the original spatially-coupled parallel concatenated codes proposed by Moloudiet al., 2017. To characterize the asymptotic performance of GSC-PCCs, we derive the corresponding density evolution equations and compute their decoding thresholds. The threshold saturation effect is observed and proven. Most importantly, we rigorously prove that the rate-$R$GSC-PCC ensemble with 2-state convolutional component codes achieves at least a fraction$1-\frac {R}{R+q}$of the capacity of the binary erasure channel (BEC) for repetition factor$q\geq 2$and this multiplicative gap vanishes as$q$tends to infinity. To the best of our knowledge, this is the first class of SC-TCs that are proven to be capacity-achieving. Further, the connection between the strength of the component codes, the decoding thresholds of GSC-PCCs, and the repetition factor is established. The superiority of the proposed codes with finite blocklength is exemplified by comparing their error performance with that of existing SC-TCs via computer simulations.

Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file’s identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect privacy requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the perfect privacy constraint. To quantify the information leakage on the requested file’s identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes, denoted by Scheme A and Scheme B, based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. We also show that Scheme A achieves an improved download rate compared to the recently proposed scheme by Samyet al.under the so-called$\epsilon $-privacy metric. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound.

We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-check (LDPC) codes. In the first stage, we build a list by iterating between a conventional NBP decoder and guessing the least reliable bit. The second stage iterates between a conventional NBP decoder and learned decimation, where we use a neural network to decide the decimation value for each bit. For a (128,64) LDPC code, the proposed NBP with decimation outperforms NBP decoding by 0.75dB and performs within 1dB from maximum-likelihood decoding at a block error rate of 10−4.

We introduce generalized spatially coupled parallel concatenated codes (GSC-PCCs), a class of spatially coupled turbo-like codes obtained by coupling parallel concatenated codes (PCCs) with a fraction of information bits repeated before the PCC encoding. GSC-PCCs can be seen as a generalization of the original spatially coupled parallel concatenated convolutional codes (SC-PCCs) proposed by Moloudi et al. [1]. To characterize the asymptotic performance of GSC-PCCs, we derive the corresponding density evolution equations and compute their decoding thresholds. We show that the proposed codes have some nice properties such as threshold saturation and that their decoding thresholds improve with the repetition factor$q$. Most notably, our analysis suggests that the proposed codes asymptotically approach the capacity as$q$tends to infinity with any given constituent convolutional code.

We extend the seminal work by Polyanskiy (2017) on massive uncoordinated access to the case where the number of active users is random and unknown a priori. We define a random-access code accounting for both misdetection (MD) and false-alarm (FA), and derive a random-coding achievability bound for the Gaussian multiple access channel. Our bound captures the fundamental trade-off between MD and FA probabilities. It suggests that lack of knowledge of the number of active users entails a small penalty in power efficiency. For a typical scenario, to achieve both MD and FA probabilities below 0.1, the required energy per bit predicted by our bound is 0.5–0.7 dB higher than that predicted by the bound in Polyanskiy (2017) for a known number of active users. Taking both MD and FA into account, we use our bound to benchmark the energy efficiency of some recently proposed massive random access schemes.

In this paper, we derive the exact input/output transfer functions of the optimal a-posteriori probability channel detector for a general ISI channel with erasures. Considering three channel impulse responses of different memory as an example, we compute the BP and MAP thresholds for regular spatially coupled LDPC codes with joint iterative detection and decoding. When we compare the results with the thresholds of ISI channels with Gaussian noise we observe an apparent inconsistency, i.e., a channel which performs better with erasures performs worse with AWGN. We show that this anomaly can be resolved by looking at the thresholds from an entropy perspective. We finally show that with spatial coupling we can achieve the symmetric information rates of different ISI channels using the same code.

We consider distributed and dynamic caching of coded content at small base stations (SBSs) in an area served by a macro base station (MBS). Specifically, content is encoded using a maximum distance separable code and cached according to a time-to-live (TTL) cache eviction policy, which allows coded packets to be removed from the caches at periodic times. Mobile users requesting a particular content download coded packets from SBSs within communication range. If additional packets are required to decode the file, these are downloaded from the MBS. We formulate an optimization problem that is efficiently solved numerically, providing TTL caching policies minimizing the overall network load. We demonstrate that distributed coded caching using TTL caching policies can offer significant reductions in terms of network load when request arrivals are bursty. We show how the distributed coded caching problem utilizing TTL caching policies can be analyzed as a specific single cache, convex optimization problem. Our problem encompasses static caching and the single cache as special cases. We prove that, interestingly, static caching is optimal under a Poisson request process, and that for a single cache the optimization problem has a surprisingly simple solution.

In this paper, we study a class of spatially coupled turbo codes, namely partially information- and partially parity-coupled turbo codes. This class of codes enjoy several advantages such as flexible code rate adjustment by varying the coupling ratio and the encoding and decoding architectures of the underlying component codes can remain unchanged. For this work, we first provide the construction methods for partially coupled turbo codes with coupling memory m and study the corresponding graph models. We then derive the density evolution equations for the corresponding ensembles on the binary erasure channel to precisely compute their iterative decoding thresholds. Rate-compatible designs and their decoding thresholds are also provided, where the coupling and puncturing ratios are jointly optimized to achieve the largest decoding threshold for a given target code rate. Our results show that for a wide range of code rates, the proposed codes attain close-to-capacity performance and the decoding performance improves with increasing the coupling memory. In particular, the proposed partially parity-coupled turbo codes have thresholds within 0.0002 of the BEC capacity for rates ranging from 1/3 to 9/10, yielding an attractive way for constructing rate-compatible capacity-approaching channel codes.

We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge servers located at the edge of the network, but do not want the servers, or any other party with access to the wireless links, to gain any information about their data. We provide a scheme that guarantees information-theoretic user data privacy against an eavesdropper with access to a number of edge servers or their corresponding communication links. The novelty of the proposed scheme lies in the utilization of secret sharing and partial replication to provide privacy, mitigate the effect of straggling servers, and to allow for joint beamforming opportunities in the download phase, to minimize the overall latency, consisting of upload, computation, and download latencies.

Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging. To tackle this problem, we build a connection between residual learning and the PA nonlinearity, and propose a novel residual neural network structure, referred to as the residual real-valued time-delay neural network (R2TDNN). Instead of learning the whole behavior of the PA, the R2TDNN focuses on learning its nonlinear behavior by adding identity shortcut connections between the input and output layer. In particular, we apply the R2TDNN to digital predistortion and measure experimental results on a real PA. Compared with neural networks recently proposed by Liu et at. and Wang et at., the R2TDNN achieves the best linearization performance in terms of normalized mean square error and adjacent channel power ratio with less or similar computational complexity. Furthermore, the R2TDNN exhibits significantly faster training speed and lower training error.

We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this method significantly outperforms conventional BP decoding, the underlying parity-check matrix may still limit the overall performance. In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We consider the weights in the Tanner graph as an indication of the importance of the connected check nodes (CNs) to decoding and use them to prune unimportant CNs. As the pruning is not tied over iterations, the final decoder uses a different parity-check matrix in each iteration. For ReedMuller and short low-density parity-check codes, we achieve performance within 0.27dB and 1.5dB of the ML performance while reducing the complexity of the decoder.

Weakly-private information retrieval (WPIR) is a variant of the private information retrieval problem in which a user wants to efficiently retrieve a file stored across a set of servers while tolerating some information leakage on the identity of the requested file to the servers. In this paper, we consider WPIR from a single-server database where the information leakage is measured in terms of the mutual information (MI) or maximal leakage (MaxL) privacy metrics. In particular, we establish a connection between the WPIR problem and rate-distortion theory, and fully characterize the optimal tradeoff between the download cost and the allowed information leakage under the MI and MaxL metrics, settling the single-server WPIR capacity.

Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer low-density parity-check code and either an inner convolutional code or a block code. We propose two new decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence.

We analyze the finite-length performance of spatially coupled low-density parity-check (SC-LDPC) codes under window decoding over the binary erasure channel. In particular, we propose a refinement of the scaling law by Olmos and Urbanke for the frame error rate (FER) of terminated SC-LDPC ensembles under full belief propagation (BP) decoding. The refined scaling law models the decoding process as two independent Ornstein-Uhlenbeck processes, in correspondence to the two decoding waves that propagate toward the center of the coupled chain for terminated SC-LDPC codes. We then extend the proposed scaling law to predict the performance of (terminated) SC-LDPC code ensembles under the more practical sliding window decoding. Finally, we extend this framework to predict the bit error rate (BER) and block error rate (BLER) of SC-LDPC code ensembles. The proposed scaling law yields very accurate predictions of the FER, BLER, and BER for both full BP and window decoding.

We present a novel decoding algorithm for q-ary low-density parity- check codes, termed symbol message passing. The proposed algorithm can be seen as a generalization of Gallager B and the binary message passing algorithm by Lechner et al. to q-ary codes. We derive density evolution equations for the q-ary symmetric channel, compute thresholds for a number of regular low-density parity-check code ensembles, and verify those by Monte Carlo simulations of long channel codes. The proposed algorithm shows performance advantages with respect to an algorithm of comparable complexity from the literature.

We consider the problem of tracking the state of a process that evolves over time in a distributed setting, with multiple observers each observing parts of the state, which is a fundamental information processing problem with a wide range of applications. We propose a cloud-assisted scheme where the tracking is performed over the cloud. In particular, to provide timely and accurate updates, and alleviate the straggler problem of cloud computing, we propose a coded distributed computing approach where coded observations are distributed over multiple workers. The proposed scheme is based on a coded version of the Kalman filter that operates on data encoded with an erasure correcting code, such that the state can be estimated from partial updates computed by a subset of the workers. We apply the proposed scheme to the problem of tracking multiple vehicles. We show that replication achieves significantly higher accuracy than the corresponding uncoded scheme. The use of maximum distance separable (MDS) codes further improves accuracy for larger update intervals. In both cases, the proposed scheme approaches the accuracy of an ideal centralized scheme when the update interval is large enough. Finally, we observe a trade-off between age-of- information and estimation accuracy for MDS codes.

Private information retrieval (PIR) protocols make it possible to retrieve a file from a database without disclosing any information about the identity of the file being retrieved. These protocols have been rigorously explored from an information-theoretic perspective in recent years. While existing protocols strictly impose that no information is leaked on the file's identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the requirement of perfect privacy. In case the user is willing to leak some information on the identity of the retrieved file, we study how the PIR rate, as well as the upload cost and access complexity, can be improved. For the particular case of replicated servers, we propose two weakly-private information retrieval schemes based on two recent PIR protocols and a family of schemes based on partitioning. Lastly, we compare the performance of the proposed schemes.

We consider private information retrieval (PIR) for distributed storage systems with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. Recently, it was shown that when data is stored using certain non-MDS codes, the MDS-PIR capacity can be achieved, and is indeed the capacity of the system. In this paper, for storage codes not belonging to this class, we present a heuristic algorithm for their decomposition into punctured subcodes and a PIR protocol based on these punctured subcodes. The code decomposition is guided by the generalized Hamming weights of the storage code. We show that the proposed PIR protocol can achieve a larger PIR rate than that of all existing PIR protocols.

We propose a refined scaling law to predict the finite-length performance in the waterfall region of spatially coupled low-density parity-check codes over the binary erasure channel. In particular, we introduce some improvements to the scaling law proposed by Olmos and Urbanke that result in a better agreement between the predicted and simulated frame error rate. We also show how the scaling law can be extended to predict the bit error rate performance.

We consider the problem of downloading content from a cellular network that is cached at the wireless edge while achieving privacy. In particular, we consider private information retrieval (PIR) of content from a library of files, i.e., the user wishes to download a file and does not want the network to learn any information about which file she is interested in. To reduce the backhaul usage, content is cached at the wireless edge in a number of small-cell base stations (SBSs) using maximum distance separable codes. We propose a PIR scheme based on generalized Reed-Solomon codes for this scenario that achieves privacy against a number of spy SBSs that collaborate. The proposed PIR scheme is an extension of a scheme by Kumar et al. to the case of multiple code rates, suitable for the scenario where files have different popularities. We derive the backhaul rate and optimize the content placement to minimize it. We prove that uniform content placement is optimal, i.e., all files that are cached should be stored using the same code rate. This is in contrast to the case where no PIR is required. Furthermore, we show numerically that popular content placement is optimal for some scenarios.

Spatially coupled turbo-like codes (SC-TCs) have been shown to have excellent decoding thresholds due to the threshold saturation effect. Furthermore, even for moderate block lengths, the simulation results demonstrate a very good bit error rate performance in the waterfall region. In this paper, we discuss the effect of spatial coupling on the performance of TCs in the finite block-length regime. We investigate the effect of coupling on the error floor performance of SC-TCs by establishing conditions under which the spatial coupling either preserves or improves the minimum distance of TCs. This allows us to investigate the error floor performance of SC-TCs by performing a weight enumerator function analysis of the corresponding uncoupled ensembles. Our results demonstrate that the spatial coupling changes the design trade-off between the waterfall and error floor performance. Instead of optimizing the belief propagation (BP) threshold of uncoupled TCs, which in turn leads to a higher error floor, we can take advantage of the threshold saturation property of the SC-TCs. Choosing strong ensembles, characterized by good maximum-a-posteriori (MAP) thresholds and low error floors, the corresponding SC-TCs are then able to simultaneously approach capacity and achieve very low error floor.

We investigate the use of maximum distance separable (MDS) codes to cache popular content to reduce the download delay of wireless content delivery. In particular, we consider a cellular system, where popular files are cached in a distributed fashion in a limited number of the mobile devices using an MDS code and can be downloaded from them using device-to-device (D2D) communication. The base station controls the D2D communication and assists the requests that cannot be fully satisfied by the distributed caching (DC) network by providing the missing data. We consider a network model, where the cell is divided into clusters, where D2D links can be activated. We derive an analytical expression for the delay incurred in downloading content from the wireless network assuming that devices roam in and out of clusters according to a Poisson random process. Our analysis allows to identify the parameters of the wireless network that mostly affect the performance and to compare different caching strategies in terms of delay. We show that DC using MDS codes can dramatically reduce the download delay with respect to the scenario where content is always downloaded from the base station and to the case of uncoded DC.

We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors. The first scheme is based on partitioning the matrix into submatrices and applying maximum distance separable (MDS) codes to each submatrix. For this scheme, we prove that up to a given number of partitions the communication load and the computational delay (not including the encoding and decoding delay) are identical to those of the scheme recently proposed by Li et al., based on a single, long MDS code. However, due to the use of shorter MDS codes, our scheme yields a significantly lower overall computational delay when the delay incurred by encoding and decoding is also considered. We further propose a second coded scheme based on Luby transform (LT) codes under inactivation decoding. Interestingly, LT codes may reduce the delay over the partitioned scheme at the expense of an increased communication load. We also consider distributed computing under a deadline and show numerically that the proposed schemes outperform other schemes in the literature, with the LT code-based scheme yielding the best performance for the scenarios considered.

We propose a novel binary message passing decoding algorithm for product-like codes based on bounded distance decoding (BDD) of the component codes. The algorithm, dubbed iterative BDD with scaled reliability (iBDD-SR), exploits the channel reliabilities and is therefore soft in nature. However, the messages exchanged by the component decoders are binary (hard) messages, which significantly reduces the decoder data flow. The exchanged binary messages are obtained by combining the channel reliability with the BDD decoder output reliabilities, properly conveyed by a scaling factor applied to the BDD decisions. We perform a density evolution analysis for generalized low-density parity-check (GLDPC) code ensembles and spatially coupled GLDPC code ensembles, from which the scaling factors of the iBDD-SR for product and staircase codes, respectively, can be obtained. For the white additive Gaussian noise channel, we show performance gains up to 0.29 dB and 0.31 dB for product and staircase codes compared to conventional iterative BDD (iBDD) with the same decoder data flow. Furthermore, we show that iBDD-SR approaches the performance of ideal iBDD that prevents miscorrections.

We propose three private information retrieval (PIR) protocols for distributed storage systems (DSSs), where data is stored using an arbitrary linear code. The first two protocols, named Protocol 1 and Protocol 2, achieve privacy for the scenario with noncolluding nodes. Protocol 1 requires a file size that is exponential in the number of files in the system, while Protocol 2 requires a file size that is independent of the number of files and is hence simpler. We prove that, for certain linear codes, Protocol 1 achieves the maximum distance separable (MDS) PIR capacity, i.e., the maximum PIR rate (the ratio of the amount of retrieved stored data per unit of downloaded data) for a DSS that uses an MDS code to store any given (finite and infinite) number of files, and Protocol 2 achieves the asymptotic MDS-PIR capacity (with infinitely large number of files in the DSS). In particular, we provide a necessary and a sufficient condition for a code to achieve the MDS-PIR capacity with Protocols 1 and 2 and prove that cyclic codes, Reed-Muller (RM) codes, and a class of distance-optimal local reconstruction codes achieve both the finite MDS-PIR capacity (i.e., with any given number of files) and the asymptotic MDS-PIR capacity with Protocols 1 and 2, respectively. Furthermore, we present a third protocol, Protocol 3, for the scenario with multiple colluding nodes, which can be seen as an improvement of a protocol recently introduced by Freij-Hollanti et al.. Similar to the noncolluding case, we provide a necessary and a sufficient condition to achieve the maximum possible PIR rate of Protocol 3. Moreover, we provide a particular class of codes that is suitable for this protocol and show that RM codes achieve the maximum possible PIR rate for the protocol. For all three protocols, we present an algorithm to optimize their PIR rates.

We consider the caching of content in the mobile devices in a dense wireless network using maximum distance separable (MDS) codes. We focus on an area, served by a base station (BS), where mobile devices move around according to a random mobility model. Users requesting a particular file download the coded packets from caching devices within a communication range using device-to-device communication. If additional packets are required to decode the file, these are downloaded from the BS. We analyze the device mobility and derive a good approximation of the distribution of caching devices within the communication range of mobile devices at any given time. We then optimize the MDS codes to minimize the network load under a cache size constraint and show that using optimized MDS codes results in significantly lower network load compared to when caching the most popular files. We further show, numerically, that caching coded packets of each file on all mobile devices, i.e., maximal spreading, is optimal.

We propose a private information retrieval (PIR) protocol for distributed storage systems with noncolluding nodes where data is stored using an arbitrary linear code. An expression for the PIR rate, i.e., the ratio of the amount of retrieved data per unit of downloaded data, is derived, and a necessary and a sufficient condition for codes to achieve the maximum distance separable (MDS) PIR capacity are given. The necessary condition is based on the generalized Hamming weights of the storage code, while the sufficient condition is based on code automorphisms. We show that cyclic codes and Reed-Muller codes satisfy the sufficient condition and are thus MDS-PIR capacity-achieving.

We prove that a class of distance-optimal local reconstruction codes (LRCs), an important family of repair-efficient codes for distributed storage systems, achieve the maximum distance separable private information retrieval capacity for the case of noncolluding nodes. This particular class of codes includes Pyramid codes and other LRCs proposed in the literature.

We consider private information retrieval (PIR) for distributed storage systems (DSSs) with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. It was recently shown that if data is stored using a particular class of non-MDS linear codes, the MDS-PIR capacity, i.e., the maximum possible PIR rate for MDS-coded DSSs, can be achieved. For this class of codes, we prove that the PIR capacity is indeed equal to the MDS-PIR capacity, giving the first family of non-MDS codes for which the PIR capacity is known. For other codes, we provide asymmetric PIR protocols that achieve a strictly larger PIR rate compared to existing symmetric PIR protocols.

We present the construction of a family of erasure correcting codes for distributed storage which achieve low repair bandwidth and complexity at the expense of a lower fault tolerance. The construction is based on two classes of codes, where the primary goal of the first class of codes is to provide fault tolerance, while the second class aims at reducing the repair bandwidth and repair complexity. The repair procedure is a two-step procedure where parts of the failed node are repaired in the first step using the first code. The downloaded symbols during the first step are cached in the memory and used to repair the remaining erased data symbols at minimal additional read cost during the second step. The first class of codes is based on maximum distance separable (MDS) codes modified using piggybacks, while the second class is designed to reduce the number of additional symbols that need to be downloaded to repair the remaining erased symbols. We numerically show that the proposed codes achieve better repair bandwidth compared to MDS codes, codes constructed using piggybacks, and local reconstruction/Pyramid codes, while a better repair complexity is achieved when compared to MDS, Zigzag, Pyramid codes, and codes constructed using piggybacks.

A counter braid (CB) is a novel counter architecture introduced by Lu et al. in 2007 for per-flow measurements on high-speed links which can be decoded with low complexity using message passing (MP). CBs achieve an asymptotic compression rate (under optimal decoding) that matches the entropy lower bound of the flow size distribution. In this paper, we apply the concept of spatial coupling to CBs to improve the performance of the original CBs and analyze the performance of the resulting spatially-coupled CBs (SC-CBs). We introduce an equivalent bipartite graph representation of CBs with identical iteration-by-iteration finite-length and asymptotic performance. Based on this equivalent representation, we then analyze the asymptotic performance of single-layer CBs and SC-CBs under the MP decoding algorithm proposed by Lu et al.. In particular, we derive the potential threshold of the uncoupled system and show that it is equal to the area threshold. We also derive the Maxwell decoder for CBs and prove that the potential threshold is an upper bound on the Maxwell decoding threshold, which, in turn, is a lower bound on the maximum a posteriori (MAP) decoding threshold. We then show that the area under the extended MP extrinsic information transfer curve (defined for the equivalent graph), computed for the expected residual CB graph when a peeling decoder equivalent to the MP decoder stops, is equal to zero precisely at the area threshold. This, combined with the analysis of the Maxwell decoder and simulation results, leads us to the conjecture that the potential threshold is, in fact, equal to the Maxwell decoding threshold and hence a lower bound on the MAP decoding threshold. Interestingly, SC-CBs do not show the well-known phenomenon of threshold saturation of the MP decoding threshold to the potential threshold characteristic of spatially-coupled low-density parity-check codes and other coupled systems. However, SC-CBs yield better MP decoding thresholds than their uncoupled counterparts. Finally, we also consider SC-CBs as a compressed sensing scheme and show that low undersampling factors can be achieved.

We propose graph-defined IRSA (G-IRSA), a new approach to design irregular repetition slotted ALOHA (IRSA) uncoordinated multiple access schemes for a controlled-size population of users that become active sporadically. The proposed scheme considers a joint design of the distribution according to which users select their repetition factors and the distribution determining how many packet replicas are transmitted per slot, as well as the connectivity of the underlying graph, i.e., to which slots users transmit. This is in sharp contrast to standard IRSA, where only the users degree distribution is optimized, while active users place their packet replicas uniformly at random and thus there is no control on how many replicas are transmitted per slot and in which slots users transmit. The key idea is to establish a link between the IRSA for the considered scenario and low-density parity-check (LDPC) codes for transmission over the binary erasure channel (BEC). Using this parallelism, the design of a G-IRSA scheme can be cast as the design of a high-rate LDPC code over the BEC. We show that the proposed scheme achieves significantly lower error floors than the original IRSA and very good decoding thresholds.

We introduce successive cancellation (SC) decoding of product codes (PCs) with single parity-check (SPC) component codes. Recursive formulas are derived, which resemble the SC decoding algorithm of polar codes. We analyze the error probability of SPC-PCs over the binary erasure channel under SC decoding. A bridge with the analysis of PCs introduced by Elias in 1954 is also established. Furthermore, bounds on the block error probability under SC decoding are provided, and compared to the bounds under the original decoding algorithm proposed by Elias. It is shown that SC decoding of SPC-PCs achieves a lower block error probability than Elias' decoding.

We propose an information-theoretic private information retrieval (PIR) scheme for distributed storage systems where data is stored using a linear systematic code of rate R> 1/2. The proposed scheme generalizes the PIR scheme for data stored using maximum distance separable codes recently proposed by Tajeddine and El Rouayheb for the scenario of a single spy node. We further propose an algorithm to optimize the communication price of privacy (cPoP) using the structure of the underlying linear code. As an example, we apply the proposed algorithm to several distributed storage codes, showing that the cPoP can be significantly reduced by exploiting the structure of the distributed storage code.

We introduce a unified ensemble for turbo-like codes (TCs) that contains the four main classes of TCs: parallel concatenated codes, serially concatenated codes, hybrid concatenated codes, and braided convolutional codes. We show that for each of the original classes of TCs, it is possible to find an equivalent ensemble by proper selection of the design parameters in the unified ensemble. We also derive the density evolution (DE) equations for this ensemble over the binary erasure channel. The thresholds obtained from the DE indicate that the TC ensembles from the unified ensemble have similar asymptotic behavior to the original TC ensembles.

We consider the distributed computing problem of multiplying a set of vectors with a matrix. For this scenario, Li et al. recently presented a unified coding framework and showed a fundamental tradeoff between computational delay and communication load. This coding framework is based on maximum distance separable (MDS) codes of code length proportional to the number of rows of the matrix, which can be very large. We propose a block-diagonal coding scheme consisting of partitioning the matrix into submatrices and encoding each submatrix using a shorter MDS code. We show that the assignment of coded matrix rows to servers to minimize the communication load can be formulated as an integer program with a nonlinear cost function, and propose an algorithm to solve it. We further prove that, up to a level of partitioning, the proposed scheme does not incur any loss in terms of computational delay (as defined by Li et al.) and communication load compared to the scheme by Li et al. We also show numerically that, when the decoding time is also taken into account, the proposed scheme significantly lowers the overall computational delay with respect to the scheme by Li et al. For heavy partitioning, this is achieved at the expense of a slight increase in communication load.

We propose an uncoordinated medium access control (MAC) protocol, called all-to-all broadcast coded slotted ALOHA (B-CSA) for reliable all-to-all broadcast with strict latency constraints. In B-CSA, each user acts as both transmitter and receiver in a half-duplex mode. The half-duplex mode gives rise to a double unequal error protection (DUEP) phenomenon: the more a user repeats its packet, the higher the probability that this packet is decoded by other users, but the lower the probability for this user to decode packets from others. We analyze the performance of B-CSA over the packet erasure channel for a finite frame length. In particular, we provide a general analysis of stopping sets for B-CSA and derive an analytical approximation of the performance in the error floor (EF) region, which captures the DUEP feature of B-CSA. Simulation results reveal that the proposed approximation predicts very well the performance of B-CSA in the EF region. Finally, we consider the application of B-CSA to vehicular communications and compare its performance with that of carrier sense multiple access (CSMA), the current MAC protocol in vehicular networks. The results show that B-CSA is able to support a much larger number of users than CSMA with the same reliability.

We consider a frame asynchronous coded slotted ALOHA (FA-CSA) system for uncoordinated multiple access, where users join the system on a slot-by-slot basis according to a Poisson random process, and in contrast to standard frame synchronous CSA (FS-CSA), users are not frame-synchronized. We analyze the performance of FA-CSA in terms of packet loss rate and delay. In particular, we derive the (approximate) density evolution that characterizes the asymptotic performance of FA-CSA when the frame length goes to infinity. We show that, if the receiver can monitor the system before anyone starts transmitting, a boundary effect similar to that of spatially coupled codes occurs, which greatly improves the iterative decoding threshold. Furthermore, we derive tight approximations of the error floor (EF) for the finite frame length regime, based on the probability of occurrence of the most frequent stopping sets. We show that, in general, FA-CSA provides better performance in both the EF and waterfall regions as compared to FS-CSA. Moreover, FA-CSA exhibits better delay properties than FS-CSA.

Generalized product codes (GPCs) are extensions of product codes (PCs), where code symbols are protected by two component codes but not necessarily arranged in a rectangular array. We consider a deterministic construction of GPCs (as opposed to randomized code ensembles) and analyze the asymptotic performance over the binary erasure channel under iterative decoding. Our code construction encompasses several classes of GPCs previously proposed in the literature, such as irregular PCs, blockwise braided codes, and staircase codes. It is assumed that the component codes can correct a fixed number of erasures and that the length of each component code tends to infinity. We show that this setup is equivalent to studying the behavior of a peeling algorithm applied to a sparse inhomogeneous random graph. Using a convergence result for these graphs, we derive the density evolution equations that characterize the asymptotic decoding performance. As an application, we discuss the design of irregular GPCs, employing a mixture of component codes with different erasure-correcting capabilities.

In this paper, we introduce the concept of spatially coupled turbo-like codes (SC-TCs) as the spatial coupling of a number of turbo-like code ensembles. In particular, we consider the spatial coupling of parallel concatenated codes, introduced by Berrou et al., and that of serially concatenated codes (SCCs), introduced by Benedetto et al. Furthermore, we propose two extensions of braided convolutional codes (BCCs), and a class of turbo-like codes which have an inherent spatially coupled structure, to higher coupling memories, and show that these yield improved belief propagation (BP) thresholds as compared with the original BCC ensemble. We derive the exact density evolution (DE) equations for SC-TCs and analyze their asymptotic behavior on the binary erasure channel. We also consider the construction of families of rate-compatible SC-TC ensembles. Our numerical results show that the threshold saturation of the BP decoding threshold to the maximum a posteriori threshold of the underlying uncoupled ensembles occurs for large enough coupling memory. The improvement of the BP threshold is especially significant for SCCs and BCCs, whose uncoupled ensembles suffer from a poor BP threshold. For a wide range of code rates, SC-TCs show close-to-capacity performance as the coupling memory increases. We further give a proof of threshold saturation for SC-TC ensembles with identical component encoders. In particular, we show that the DE of SC-TC ensembles with identical component encoders can be properly rewritten as a scalar recursion. This allows us to define potential functions and prove threshold saturation using the proof technique recently introduced by Yedla et al.

Several authors have proposed spatially-coupled (or convolutional-like) variants of product codes (PCs). In this paper, we focus on a parametrized family of generalized PCs that recovers some of these codes (e.g., staircase and block-wise braided codes) as special cases and study the iterative decoding performance over the binary erasure channel. Even though our code construction is deterministic (and not based on a randomized ensemble), we show that it is still possible to rigorously derive the density evolution (DE) equations that govern the asymptotic performance. The obtained DE equations are then compared to those for a related spatially-coupled PC ensemble. In particular, we show that there exists a family of (deterministic) braided codes that follows the same DE equation as the ensemble, for any spatial length and coupling width.

We consider the use of distributed storage (DS) to reduce the communication cost of content delivery in wireless networks. Content is stored (cached) in a number of mobile devices using an erasure correcting code. Users retrieve content from other devices using device-to-device communication or from the base station (BS), at the expense of higher communication cost. We address the repair problem when a device storing data leaves the cell. We introduce a repair scheduling where repair is performed periodically and derive analytical expressions for the overall communication cost of content download and data repair as a function of the repair interval. The derived expressions are then used to evaluate the communication cost entailed by DS using several erasure correcting codes. Our results show that DS can reduce the communication cost with respect to the case where content is downloaded only from the BS, provided that the repairs are performed frequently enough. If devices storing content arrive to the cell, the communication cost using DS is further reduced and, for a large enough arrival rate, it is always beneficial. Interestingly, we show that maximum distance separable codes, which do not perform well for classical DS, can yield a low overall communication cost in wireless DS.

We analyze the asymptotic performance of nonbinary spatially coupled low-density parity-check (SC-LDPC) code ensembles defined over the general linear group on the binary erasure channel. In particular, we prove the threshold saturation of belief propagation decoding to the so-called potential threshold, using the proof technique based on potential functions introduced by Yedla et al., assuming that the potential function exists. We rewrite the density evolution of nonbinary SC-LDPC codes in an equivalent vector recursion form which is suited for the use of the potential function. We then discuss the existence of the potential function for the general case of vector recursions defined by multivariate polynomials, and give a method to construct it. We define a potential function in a slightly more general form than the one by Yedla et al., in order to make the technique based on potential functions applicable to the case of nonbinary LDPC codes. We show that the potential function exists if a solution to a carefully designed system of linear equations exists. Furthermore, we numerically show the existence of a solution to the system of linear equations for a large number of nonbinary LDPC code ensembles, which allows us to define their potential function and thus prove threshold saturation.

We present a comprehensive study of the information rate loss of the max-log approximation for M-ary pulse-amplitude modulation (PAM) in a bit-interleaved coded modulation (BICM) system. It is widely assumed that the calculation of L-values using the max-log approximation leads to an information loss. We prove that this assumption is correct for all M-PAM constellations and labelings with the exception of a symmetric 4-PAM constellation labeled with a Gray code. We also show that for max-log L-values, the BICM generalized mutual information (GMI), which is an achievable rate for a standard BICM decoder, is too pessimistic. In particular, it is proved that the so-called harmonized GMI, which can be seen as the sum of bit-level GMIs, is achievable without any modifications to the decoder. We then study how bit-level channel symmetrization and mixing affect the MI and the GMI for max-log L-values. Our results show that these operations, which are often used when analyzing BICM systems, preserve the GMI. However, this is not necessarily the case when the MI is considered. Necessary and sufficient conditions under which these operations preserve the MI are provided.

We present the construction of a new family of erasure correcting codes for distributed storage that yield low repair bandwidth and low repair complexity. The construction is based on two classes of parity symbols. The primary goal of the first class of symbols is to provide good fault tolerance, while the second class facilitates node repair, reducing the repair bandwidth and the repair complexity. We compare the proposed codes with other codes proposed in the literature.

We study two approaches to distributed compressed sensing for in-network data compression and signal reconstruction at a sink. Communication to the sink is considered to be bandwidth-constrained due to the large number of devices. By using distributed compressed sensing for compression of the data in the network, the communication cost (bandwidth usage) to the sink can be decreased at the expense of delay induced by the local communication. We investigate the relation between cost and delay given a certain reconstruction performance requirement when using basis pursuit denoising for reconstruction. Moreover, we analyze and compare the performance degradation due to erased packets sent to the sink.

In many practical communication systems, one binary encoder/decoder pair is used to communicate over a set of parallel channels. Examples of this setup include multi-carrier transmission, rate-compatible puncturing of turbo-like codes, and bit-interleaved coded modulation (BICM). A bit mapper is commonly employed to determine how the coded bits are allocated to the channels. In this paper, we study spatially coupled low-density parity check codes over parallel channels and optimize the bit mapper using BICM as the driving example. For simplicity, the parallel bit channels that arise in BICM are replaced by independent binary erasure channels (BECs). For two parallel BECs modeled according to a 4-PAM constellation labeled by the binary reflected Gray code, the optimization results show that the decoding threshold can be improved over a uniform random bit mapper, or, alternatively, the spatial chain length of the code can be reduced for a given gap to capacity. It is also shown that for rate-loss free, circular (tail-biting) ensembles, a decoding wave effect can be initiated using only an optimized bit mapper.

The problem of joint oscillator phase noise (PHN) estimation and data detection for multi‐input multi‐output (MIMO) systems using bit‐interleaved‐coded modulation is analysed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation‐maximisation (EM) framework, is proposed. It is shown that at high signal‐to‐noise ratios, a maximum a posteriori (MAP) estimator can be used to carry out the maximisation step of the EM algorithm. Moreover, to reduce the computational complexity of the proposed EM algorithm, a soft decision‐directed extended Kalman filter‐smoother (EKFS) is applied instead of the MAP estimator to track the PHN parameters. The numerical results show that by combining the proposed EKFS‐based approach with an iterative detector that employs low‐density parity check codes, PHN can be accurately tracked. The simulations also demonstrate that compared to the existing algorithms, the proposed iterative receiver can significantly enhance the performance of MIMO systems in the presence of PHN.

In the framework of write-once memory (WOM) codes, it is important to distinguish between codes that can be decoded directly and those that require the decoder to know the current generation so as to successfully decode the state of the memory. A widely used approach to constructing WOM codes is to design first nondecodable codes that approach the boundaries of the capacity region and then make them decodable by appending additional cells that store the current generation, at an expense of rate loss. In this paper, we propose an alternative method to making nondecodable WOM codes decodable by appending cells that also store some additional data. The key idea is to append to the original (nondecodable) code a short synchronous WOM code and write generations of the original code and the synchronous code simultaneously. We consider both the binary and the nonbinary case. Furthermore, we propose a construction of synchronous WOM codes, which are then used to make nondecodable codes decodable. For short-to-moderate block lengths, the proposed method significantly reduces the rate loss as compared to the standard method.

In this paper, we consider pseudocodewords of (relaxed) linear programming (LP) decoding of 3-dimensional turbo codes (3D-TCs). We present a relaxed LP decoder for 3D-TCs, adapting the relaxed LP decoder for conventional turbo codes proposed by Feldman in his thesis. We show that the 3D-TC polytope is proper and C-symmetric and make a connection to finite graph covers of the 3D-TC factor graph. This connection is used to show that the support set of any pseudocodeword is a stopping set of iterative decoding of 3D-TCs using maximum a posteriori constituent decoders on the binary erasure channel. Furthermore, we compute ensemble-average pseudoweight enumerators of 3D-TCs and perform a finite-length minimum pseudoweight analysis for small cover degrees. Moreover, an explicit description of the fundamental cone of the 3D-TC polytope is given. Finally, we present an extensive numerical study of small-to-medium block length 3D-TCs, which shows that 1) typically (i.e., in most cases), when the minimum distance dminand/or the stopping distance hminis high, the minimum pseudoweight (on the additive white Gaussian noise channel) is strictly smaller than both dminand hminand that 2) the minimum pseudoweight grows with the block length, at least for small-to-medium block lengths.

We present a decode-and-forward transmission scheme based on spatially-coupled low-density parity-check (SC-LDPC) codes for a network consisting of two (possibly correlated) sources, one relay, and one destination. The links between the nodes are modeled as binary erasure channels. Joint source-channel coding with joint channel decoding is used to exploit the correlation. The relay performs network coding. We derive analytical bounds on the achievable rates for the binary erasure time-division multiple-access relay channel with correlated sources. We then design bilayer SC-LDPC codes and analyze their asymptotic performance for this scenario. We prove analytically that the proposed coding scheme achieves the theoretical limit for symmetric channel conditions and uncorrelated sources. Using density evolution, we furthermore demonstrate that our scheme approaches the theoretical limit also for non-symmetric channel conditions and when the sources are correlated, and we observe the threshold saturation effect that is typical for spatially-coupled systems. Finally, we give simulation results for large block lengths, which validate the DE analysis.

We analyze the asymptotic performance of non-binary spatially-coupled low-density parity-check (SC-LDPC) codes built on the general linear group, when the transmission takes place over the binary erasure channel. We propose an efficient method to derive an upper bound to the maximum a posteriori probability (MAP) threshold for nonbinary LDPC codes, and observe that the MAP performance of regular LDPC codes improves with the alphabet size. We then consider nonbinary SC-LDPC codes. We show that the same threshold saturation effect experienced by binary SC-LDPC codes occurs for the nonbinary codes, hence we conjecture that the BP threshold for large termination length approaches the MAP threshold of the underlying regular ensemble.

An asymptotically optimal trellis-coded modulation (TCM) encoder requires the joint design of the encoder and the binary labeling of the constellation. Since analytical approaches are unknown, the only available solution is to perform an exhaustive search over the encoder and the labeling. For large constellation sizes and/or many encoder states, however, an exhaustive search is unfeasible. Traditional TCM designs overcome this problem by using a labeling that follows the set-partitioning principle and by performing an exhaustive search over the encoders. In this paper we study binary labelings for TCM and show how they can be grouped into classes, which considerably reduces the search space in a joint design. For 8-ary constellations, the number of different binary labelings that must be tested is reduced from 8!=40320 to 240. For the particular case of an 8-ary pulse amplitude modulation constellation, this number is further reduced to 120 and for 8-ary phase shift keying to only 30. An algorithm to generate one labeling in each class is also introduced. Asymptotically optimal TCM encoders are tabulated which are up to 0.3 dB better than the previously best known encoders.

We study the design of amplitude phase-shift keying (APSK) constellations for a coherent fiber-optical communication system where nonlinear phase noise (NLPN) is the main system impairment. APSK constellations can be regarded as a union of phase-shift keying (PSK) signal sets with different amplitude levels. A practical two-stage (TS) detection scheme is analyzed, which performs close to optimal detection for high enough input power. We optimize APSK constellations with 4, 8, and 16 points in terms of symbol error probability (SEP) under TS detection for several combinations of input power and fiber length. For 16 points, performance gains of 3.2 dB can be achieved at a SEP of 10^{-2} compared to 16-QAM by choosing an optimized APSK constellation. We also demonstrate that in the presence of severe nonlinear distortions, it may become beneficial to sacrifice a constellation point or an entire constellation ring to reduce the average SEP. Finally, we discuss the problem of selecting a good binary labeling for the found constellations.

In this paper, we address the problem of optimizing signal constellations for strong phase noise. The problem is investigated by considering three optimization formulations, which provide an analytical framework for constellation design. In the first formulation, we seek to design constellations that minimize the symbol error probability (SEP) for an approximate ML detector in the presence of phase noise. In the second formulation, we optimize constellations in terms of mutual information (MI) for the effective discrete channel consisting of phase noise, additive white Gaussian noise, and the approximate ML detector. To this end, we derive the MI of this discrete channel. Finally, we optimize constellations in terms of the MI for the phase noise channel. We give two analytical characterizations of the MI of this channel, which are shown to be accurate for a wide range of signal-to-noise ratios and phase noise variances. For each formulation, we present a detailed analysis of the optimal constellations and their performance in the presence of strong phase noise. We show that the optimal constellations significantly outperform conventional constellations and those proposed in the literature in terms of SEP, error floors, and MI.

We consider the design of amplitude phase-shift keying (APSK) constellations, targeting their application to coherent fiber-optical communications. Phase compensation is used at the receiver to combat nonlinear phase noise caused by the Kerreffect. We derive the probability density function of the post-compensated observation for multilevel constellations. Optimal APSK constellations in terms of symbol error probability (SEP) are found assuming a two-stage detector. Performance gains of 3:2 dB can be achieved compared to 16-QAM at a SEP of 10-2. We optimize the number of rings, the number of points per ring, as well as the radius distribution of the constellation. For low to moderate nonlinearities, radius optimization only yields minor improvements over an equidistant spacing of rings. In the highly nonlinear regime, however, a smaller SEP can be achieved by “sacrificing” the outer ring of the constellation, in favor of achieving good SEP in the remaining rings.

Optimal trellis-coded modulation (TCM) schemes are obtained by jointly designing the convolutional encoder and the binary labeling of the constellation. Unfortunately this approach is infeasible for large encoder memories or constellation sizes. Traditional TCM designs circumvent this problem by using a labeling that follows the set-partitioning principle and by performing an exhaustive search over the encoders. Therefore, traditional TCM schemes are not necessarily optimal. In this paper, we study binary labelings for TCM and show how they can be grouped into classes, which considerably reduces the search space in a joint design. For the particular case of 8-ary modulation the search space for the labelings is reduced from 8! to 240. Using this classification, we formally prove that for any channel it is always possible to design a TCM system based on the binary-reflected Gray code with identical performance to the one proposed by Ungerboeck in 1982. Moreover, the classification is used to tabulate asymptotically optimal TCM schemes.

While some write once memory (WOM) codes are inherently decodable, others require the added knowledge of the current generation in order to successfully decode the state of the memory. If there is no limit on the code length, n, a binary non-decodable t-write WOM code can be made decodable at an insignificant cost in terms of code rate by adding t − 1 cells to store the current generation after replicating the code enough times for the t − 1 cells to be of negligible weight. This justifies the research on non-decodable WOM codes. However, if n is bounded, the t − 1 additional cells may introduce a significant loss in terms of code rate. In this paper, we propose a new method to make non-decodable WOM codes decodable at a lower price when n is bounded. The main idea is to add cells that do not only store the current generation, but also additional data, by using a synchronous (t − 1)-write WOM code of length t − 1 or slightly above which does not contain the all-zero codeword. A bound on the rate of a simple family of synchronous WOM codes with n = t is given, as well as very short codes from this family. Better codes are then obtained by local manipulations of these codes. Finally, a construction of synchronous WOM codes with good properties is proposed to reach higher values of t.

Hybrid automatic repeat request (HARQ) is a well-known technique for improving system throughput and link performance of wireless communication systems, including cooperative communication systems. In this paper, we exploit the limited feedback applied to the two-source turbo coded cooperation scheme to define a particular cooperative HARQ protocol, called two-level HARQ, where the decision on retransmission at each node is conditioned by two levels: first by the feedback from the destination and second by the feedback from the partner node. To evaluate the performance improvement of this cooperative HARQ system over the original turbo coded cooperation system in terms of frame error probability, we define the system retransmission gain. This gain serves as a decision parameter to determine the conditions under which the cooperative HARQ protocol is useful. Finally, optimal time resource allocation is explored, offering sizable performance improvements.

This paper addresses the problem of optimal power allocation for hybrid automatic repeat request (HARQ) feedback over slowly-fading channels. We mainly focus on the repetition time diversity HARQ scheme where the results are obtained for both continuous and bursting communication models. Moreover, the effect of an outage probability constraint on the system data transmission efficiency is studied under different transmission power constraints. Simulation results show that 1) for Nakagami fading channels, the optimal HARQ-based (re)transmission powers maximizing the system throughput should be decreasing in every (re)transmission round, 2) higher rates are achieved in the continuous communication, when compared with the bursting model, and 3) HARQ feedback leads to considerable performance improvement even in outage-limited conditions.

It has been widely observed that there exists a fundamental tradeoff between the minimum (Hamming) distance properties and the iterative decoding convergence behavior of turbo-like codes. While capacity-achieving code ensembles typically are asymptotically bad in the sense that their minimum distance does not grow linearly with block length, and they therefore exhibit an error floor at moderate-to-high signal-to-noise ratios, asymptotically good codes usually converge further away from channel capacity. In this paper, we introduce the concept of tuned turbo codes, a family of asymptotically good hybrid concatenated code ensembles, where asymptotic minimum distance growth rates, convergence thresholds, and code rates can be tradedoff using two tuning parameters:$\lambda $and$\mu $. By decreasing$\lambda $, the asymptotic minimum distance growth rate is reduced in exchange for improved iterative decoding convergence behavior, while increasing$\lambda $raises the asymptotic minimum distance growth rate at the expense of worse convergence behavior, and thus, the code performance can be tuned to fit the desired application. By decreasing$\mu $, a similar tuning behavior can be achieved for higher rate code ensembles.

We derive bounds on the error probability of optimal and sub- optimal detectors in an uncoded decode-and- forward relay system with two correlated information sources. This setup is relevant to wireless sensor networks where nearby sensors collect spatially correlated data. We show that taking into account the source correlation at the relay and at the destination leads to significant performance gains. Simulation results corroborate the tightness of our analytical bounds.

In this work, we consider pseudocodewords of (relaxed) linear programming (LP) decoding of 3-dimensional turbo codes (3D-TCs), recently introduced by Berrou et al.. Here, we consider binary 3D-TCs while the original work of Berrou et al. considered double-binary codes. We present a relaxed LP decoder for 3D-TCs, which is an adaptation of the relaxed LP decoder for conventional turbo codes proposed by Feldman in his thesis. The vertices of this relaxed polytope are the pseudocodewords. We show that the support set of any pseudocodeword is a stopping set of iterative decoding of 3D-TCs using maximum a posteriori constituent decoders on the binary erasure channel. Furthermore, we present a numerical study of small block length 3D-TCs, which shows that typically the minimum pseudoweight (on the additive white Gaussian noise (AWGN) channel) is smaller than both the minimum distance and the stopping distance. In particular, we performed an exhaustive search over all interleaver pairs in the 3D-TC (with input block length K = 128) based on quadratic permutation polynomials over integer rings with a quadratic inverse. The search shows that the best minimum AWGN pseudoweight is strictly smaller than the best minimum/stopping distance.

An improved concatenated code structure, which generalizes parallel and serially concatenated convolutional codes is presented and investigated. The structure is ideal for designing low-complexity rate-compatible code families with good performance in both the waterfall and error floor regions. As an additional feature, the structure provides a unified analysis and design framework, which includes both parallel and serially concatenated codes as particular cases. We derive design criteria for the generalized class of concatenated convolutional codes based on union bounds for the error probability and extrinsic information transfer (EXIT) charts for the decoding threshold.

In this work, we consider the minimum distance properties and convergence thresholds of 3-D turbo codes (3D-TCs), recently introduced by BerrouHere, we consider binary 3D-TCs while the original work of Berrouconsidered double-binary codes. In the first part of the paper, the minimum distance properties are analyzed from an ensemble perspective, both in the finite-length regime and in the asymptotic case of large block lengths. In particular, we analyze the asymptotic weight distribution of 3D-TCs and show numerically that their typical minimum distance$d_{\min}$may, depending on the specific parameters, asymptotically grow linearly with the block length, i.e., the 3D-TC ensemble is asymptotically good for some parameters. In the second part of the paper, we derive some useful upper bounds on the$d_{\min}$when using quadratic permutation polynomial (QPP) interleavers with a quadratic inverse. Furthermore, we give examples of interleaver lengths where an upper bound appears to be tight. The best codes (in terms of estimated$d_{\min}$) obtained by randomly searching for good pairs of QPPs for use in the 3D-TC are compared to a probabilistic lower bound on the$d_{\min}$when selecting codes from the 3D-TC ensemble uniformly at random. This comparison shows that the use of designed QPP interleavers can improve the$d_{\min}$significantly. For instance, we have found a (6144,2040) 3D-TC with an estimated$d_{\min}$of 147, while the probabilistic lower bound is 69. Higher rates are obtained by puncturing nonsystematic bits, and optimized periodic puncturing patterns for rates$1/2,$$2/3$, and$4/5$are found by computer search. Finally, we give iterative decoding thresholds, computed from an extrinsic information transfer chart analysis, and present simulation results on the additive white Gaussian noise channel to compare the error rate performance to that of conventional turbo codes.

In this paper, we propose a distributed turbo-like coding scheme for a multi-source relay scenario where multiple sources communicate with a destination with the help of a common relay, which uses the decode-and-forward strategy and operates in half-duplex mode. The proposed distributed code can be viewed as a serially concatenated code (SCC). Thus, at the destination decoding is performed in an iterative fashion which resembles the decoding of a classic SCC. We consider the two scenarios where the sources transmit over orthogonal channels and where they do not. For the latter, interleave-division multiple-access is used for multiuser detection. For both scenarios we optimize the transmission time allocated to the sources and to the relay and compute the achievable rates. The proposed scheme achieves very low error rates and offers significant performance gains with respect to non-cooperation, even for a very large number of sources. Furthermore, it provides a high flexibility in terms of code rate, number of sources, overall system rate and error protection.

In this work, we propose a concatenated coding scheme with iterative decoding for a bit-shift channel. In more detail, we consider the serial concatenation of an outer error-correcting code with an inner modulation code, possibly preceded by an accumulator to improve iterative decoding performance. The bit-shift channel was originally proposed for magnetic and optical recoding channels, but has recently been popular for inductively coupled channels. In particular, we search for optimal encoder mappings from an iterative decoding perspective for the inner modulation code, which has been designed to be single bit-shift error-correcting and also to have large average power. This is important in inductively coupled channels, since the receiver (or tag) gets its entire power from the received signal, and the information should be modulated in a way that maximizes the power transferred to the tag.

In this paper, a distributed turbo-like coding scheme for wireless networks with relays is proposed. We consider a scenario where multiple sources communicate with a single destination with the help of a relay. The proposed scheme can be regarded as of the decode-and-forward type. The relay decodes the information from the sources and it properly combines and re-encodes them to generate some extra redundancy, which is transmitted to the destination. The amount of redundancy generated by the relay can simply be adjusted according to requirements in terms of performance, throughput and/or power. At the destination, decoding of the information of all sources is performed jointly exploiting the redundancy provided by the relay in an iterative fashion. The overall communication network can be viewed as a serially concatenated code. The proposed distributed scheme achieves significant performance gains with respect to the non-cooperation system, even for a very large number of users. Furthermore, it presents a high flexibility in terms of code rate, block length and number of users.

In this paper, we analyze the asymptotic stopping set distribution of 3-dimensional turbo code (3D-TC) ensembles, consisting of a parallel turbo code concatenated in series with an inner accumulator which encodes only a fraction λ of the turbo code parity bits. We show that, for certain parameters, the stopping distance of 3D-TC ensembles asymptotically grows linearly with the block length, i.e., 3D-TCs are good for the binary erasure channel. We also consider random puncturing of non-systematic bits and show that higher (or some) linear growth rate is obtained for decreasing values of λ, contrary to the asymptotic minimum distance, whose growth rate decreases with decreasing values of λ. Finally, iterative convergence thresholds of 3D-TC ensembles are analyzed by means of extrinsic information transfer charts.

We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations.

Wireless networks can exploit an implicit distributed diversity by the use of cooperative systems thanks to the broadcast nature of the radio link. Cooperation leads to improvements in terms of throughput and/or performance and decreases the sensitivity to channel variations. In this paper, a practical approach for a two-user cooperative wireless network is proposed based on a particular hybrid automatic repeat request (HARQ) protocol and turbo coded cooperation. In particular, we propose a two-level ARQ protocol where decision on retransmission is conditioned by two levels; first by the feedback from the destination and second by the feedback from the partner node. The proposed two-level ARQ protocol, combined with turbo coded cooperation, is designed to guarantee inter-user channel improvement through the relay-level ARQ, and consequently better overall system performance and higher throughput by controlling retransmission at destination side.

Ternary channels can be used to model the behavior of some memory devices, where information is stored in three different levels. In this paper, error correcting coding for a ternary channel where some of the error transitions are not allowed, is considered. The resulting channel is nonsymmetric, therefore, classical linear codes are not optimal for this channel. We define the maximum-likelihood (ML) decoding rule for ternary codes over this channel and show that it depends on the channel error probability. An alternative decoding rule which depends only on code properties, called dA-decoding, is then proposed. It is shown that dA-decoding and ML decoding are equivalent, i.e., dA-decoding is optimal, under certain conditions. Assuming dA-decoding, we characterize the error correcting capabilities of ternary codes over the nonsymmetric ternary channel. We also derive an upper bound and a constructive lower bound on the size of codes. The results arising from the constructive lower bound are then compared, for short sizes, to optimal codes (in terms of code size) found by a clique-based search. It is shown that the proposed construction method gives good codes, and that in some cases the codes are optimal.

The serial concatenation of a repetition code with two or more accumulators has the advantage of a simple encoder structure. Furthermore, the resulting ensemble is asymptotically good and exhibits minimum distance growing linearly with block length. However, in practice these codes cannot be decoded by a maximum likelihood decoder, and iterative decoding schemes must be employed. For low-density parity-check codes, the notion of trapping sets has been introduced to estimate the performance of these codes under iterative message passing decoding. In this paper, we present a closed form finite length ensemble trapping set enumerator for repeat multiple accumulate codes by creating a trellis representation of trapping sets. We also obtain the asymptotic expressions when the block length tends to infinity and evaluate them numerically.

In this work, we consider a stopping set analysis of repeat multiple-accumulate (RMA) code ensembles formed by the serial concatenation of a repetition code with multiple accumulators. The RMA codes are assumed to be iteratively decoded in a constituent code oriented fashion using maximum a posteriori erasure correction in the constituent codes. We give stopping set enumerators for RMA code ensembles and show that their stopping distance hmin, defined as the size of the smallest nonempty stopping set, asymptotically grows linearly with the block length. Thus, the RMA code ensembles are good for the binary erasure channel. Furthermore, it is shown that, contrary to the asymptotic minimum distance dmin, whose growth rate coefficient increases with the number of accumulate codes, the hmingrowth rate coefficient diminishes with the number of accumulators. We also consider random puncturing and show that for sufficiently high code rates, the asymptotic hmindoes not grow linearly with the block length, contrary to the asymptotic dmin, whose growth rate coefficient approaches the Gilbert-Varshamov bound as the rate increases. Finally, we give iterative decoding thresholds to show the convergence properties.

In this work, we give good concatenated code ensembles for the binary erasure channel (BEC). In particular, we consider repeat multiple-accumulate (RMA) code ensembles formed by the serial concatenation of a repetition code with multiple accumulators, and the hybrid concatenated code (HCC) ensembles recently introduced by Koller et al. (5th Int. Symp. on Turbo Codes & Rel. Topics, Lausanne, Switzerland) consisting of an outer multiple parallel concatenated code serially concatenated with an inner accumulator. We introduce stopping sets for iterative constituent code oriented decoding using maximum a posteriori erasure correction in the constituent codes. We then analyze the asymptotic stopping set distribution for RMA and HCC ensembles and show that their stopping distance hmin, defined as the size of the smallest nonempty stopping set, asymptotically grows linearly with the block length. Thus, these code ensembles are good for the BEC. It is shown that for RMA code ensembles, contrary to the asymptotic minimum distance dmin, whose growth rate coefficient increases with the number of accumulate codes, the hmingrowth rate coefficient diminishes with the number of accumulators. We also consider random puncturing of RMA code ensembles and show that for sufficiently high code rates, the asymptotic hmindoes not grow linearly with the block length, contrary to the asymptotic dmin, whose growth rate coefficient approaches the Gilbert-Varshamov bound as the rate increases. Finally, we give iterative decoding thresholds for the different code ensembles to compare the convergence properties.

In this letter we consider the ensemble of codes formed by the serial concatenation of a Hamming code and two accumulate codes. We show that this ensemble is asymptotically good, in the sense that most codes in the ensemble have minimum distance growing linearly with the block length. Thus, the resulting codes achieve high minimum distances with high probability, about half or more of the minimum distance of a typical random linear code of the same rate and length in our examples. The proposed codes also show reasonably good iterative convergence thresholds, which makes them attractive for applications requiring high code rates and low error rates, such as optical communications and magnetic recording.

In this paper, a novel class of serially concatenated convolutional codes (SCCCs) is addressed. In contrast to standard SCCCs, where high rates are obtained by puncturing the outer code, the heavy puncturing is moved to the inner code, which can be punctured beyond the unitary rate. We derive analytical upper bounds on the error probability of this code structure by considering an equivalent code construction consisting of the parallel concatenation of two codes, and address suitable design guidelines for code optimization. It is shown that the optimal puncturing of the inner code depends on the outer code, i.e., it is interleaver dependent. This dependence cannot be tracked by the analysis for standard SCCCs, which fails in predicting code performance. Based on the considerations arising from the bounds analysis, we construct a family of rate-compatible SCCCs with a high level of flexibility and a good performance over a wide range of code rates, using simple constituent codes. The error rate performance of the proposed codes is found to be better than that of standard SCCCs, especially for high rates, and comparable to the performance of more complex turbo codes.

Thanks to the probabilistic message passing performed between its component decoders, a turbo decoder is able to provide strong error correction close to the theoretical limit. However, the minimum Hamming distance (dmin) of a turbo code may not be sufficiently large to ensure large asymptotic gains at very low error rates (the so-called flattening effect). Increasing the dminof a turbo code may involve using component encoders with a large number of states, devising more sophisticated internal permutations, or increasing the number of component encoders. This paper addresses the latter option and proposes a modified turbo code in which a fraction of the parity bits are encoded by a rate-1, third encoder. The result is a noticeably increased dmin, which improves turbo decoder performance at low error rates. Performance comparisons with turbo codes and serially concatenated convolutional codes are given.

In this paper, serially concatenated continuous phase modulation (SCCPM) is considered for the uplink of satellite communications. A three-step design procedure is proposed to optimize the association of the outer code and the CPM for a wide range of spectral efficiencies, ranging from 0.75 to 2.25 bit/s/Hz. Firstly, EXIT chart analysis is applied to derive general guidelines for choosing SCCPM parameters. A significant result is that a high-rate outer code is required to achieve good convergence threshold, given a spectral efficiency. At a second stage, union bounds to the error probability are considered to choose the outer code under the constraints arising from the EXIT charts analysis. From this analysis, extended BCH codes and extended Hamming codes are proposed as outer code for broadband and narrowband transmission, respectively. For the latter, double-binary convolutional codes and symbol interleaving is also proposed as a valid alternative. Finally, combining both EXIT charts and union bounds we optimize the association of code and CPM to achieve both low error rates and good convergence. The proposed concatenated structure offers very low error floors and good performance in the waterfall region for all considered spectral efficiencies. A significant improvement with respect to previous concatenated CPM schemes is shown.

In this paper, we propose a rate-compatible serially concatenated structure consisting of an outer linear extended BCH code and an inner recursive systematic convolutional code. Rate flexibility is achieved by puncturing the inner code. A two- step code design procedure combining analytical union bounds with Extrinsic Information Transfer charts is used to obtain codes offering very good performance in both the waterfall and the error floor regions over a wide range of code rates. The resulting codes show interesting advantages in terms of convergence and error floor compared to similar structures using convolutional codes as outer codes.

In this paper, serially concatenated continuous phase modulation (CPM) is considered. A concatenated structure consisting of a short extended BCH code as outer code is proposed, targeting a wide choice of spectral efficiencies, ranging from 0.75 to 2.25 bit/s/Hz. A two-step design procedure combining EXIT charts analysis and union bound techniques is used to optimize the association of the outer code and the CPM. An exhaustive study of several quaternary and octal CPM schemes is performed. The proposed concatenated structure offers very low error floors (frame error rate below 10-6) and good performance in the waterfall region for all spectral efficiencies. A significant improvement with respect to previous concatenated CPM schemes is shown. The envisaged application of the proposed scheme is the return link of broadband satellite communications.

The analysis and design of spacetime codes for correlated fading channels when the diversity gain is large enough is considered. We derive a simple form for a distance metric that characterizes the code performance in the presence of transmit correlation, and propose some design criteria to build good space time trellis codes (STTCs) for correlated channels. For the case of two transmit antennas, we show that in strongly correlated channels, performance is governed by the constellation that results from the sum of the constellations associated with the transmit antennas. This suggests the use of new constellations to design better codes for correlated channels. The design criteria are then extended to any number of transmit antennas. Based on these criteria, we derive new STTCs for two and three transmit antennas that perform much better in correlated channels than the STTC optimized for the independent and identically distributed case. We also consider set partitioning applied to the sum constellation as a simple technique to design good codes for correlated channels. The codes derived show performance close to the codes found by an exhaustive search. Finally, we consider antenna selection as an alternative to build good codes for more than two antennas in fading-correlated scenarios.

In this paper, the design of a fully analog iterative decoder for a serially concatenated convolutional code is presented. The decoder is reconfigurable in both block length and code rate. An interleaver size up to 2400 bit is considered. The decoder core implements a single SISO working on a window of the whole code trellis. It is then reused several times to decode the two constituent codes. The resulting decoder performs iterations, but it is fully analog. The extrinsic information exchanged in the decoding process is stored in an analog memory and permuted through a reconfigurable interleaver. Behavioral analysis of the decoder as well as precision and mismatch impact on performance are reported in the paper.

In this paper, we present an all-analog implementation of the rate-1/3, block length 40, UMTS turbo decoder. The prototype was designed and fabricated in a 0.35$mu$m CMOS technology and operates at 3.3 V. We also introduce a discrete-time first-order model for analog decoders which allows fast BER simulations, while taking into account circuit transient behavior and component mismatch. The model is applied to the rate-1/3 analog turbo decoder for UMTS defined in the 3GPP standard, and the discrete-time model predictions are compared with the decoder experimental performance and the transistor-level simulations. These results demonstrated that this model can be successfully used as a tool to both predict analog decoder performance and give design guidelines for complex decoders, for which circuit-level simulations are impractical.

In this paper, we present an all-analog implementation of the rate-1/3, block length 40, universal mobile telecommunications system (UMTS) turbo decoder. The prototype was designed and fabricated in 0.35$\mu$m complementary metal-oxide-semiconductor technology and operates at 3.3 V. We also introduce a discrete-time first-order model for analog decoders which allows fast bit-error rate simulations, while taking into account circuit transient behavior and component mismatch. The model is applied to the rate-1/3 analog turbo decoder for UMTS defined in the Third Generation Partnership Project standard, and the discrete-time model predictions are compared with the decoder experimental performance and the transistor-level simulations. These results demonstrated that this model can be successfully used as a tool to both predict analog decoder performance and give design guidelines for complex decoders, for which circuit-level simulations are impractical.

In this paper, we discuss the design and testing results of an analog 0.35 /spl mu/m CMOS turbo decoder for the rate-1/3, 40 bit UMTS turbo code. The prototype was successfully tested at nominal conditions (2 Mbit/s), with an overall power consumption of 10.3 mW at 3.3 V. The tested BER curve shows a limited performance loss (about 0.5 dB) with respect to that of the digital implementation. We also discuss a discrete-time model of the analog decoder which allows us to run BER simulations including circuit transient behavior and device mismatch in a very short time. Circuit-level simulations demonstrate the validity of our model. According to the discrete-time simulation, a significant contribution to the performance loss is due to device mismatch.

We provide a performance analysis of a new class of serial concatenated convolutional codes (SCCC) where the inner encoder can be punctured beyond the unitary rate. The puncturing of the inner encoder is not limited to inner coded bits, but extended to systematic bits. We derive analytical upper bounds to the error probability of this particular code structure and address suitable design guidelines for the inner code puncturing patterns. We show that the proportion of systematic and parity bits to be deleted strongly depends on the SNR region of interest. Furthermore, we show that puncturing of the inner code systematic bits should be interleaver dependent. Based on these considerations, we derive design guidelines to obtain well-performing rate-compatible SCCCs families. Throughout the paper, the performance of the proposed codes are compared with analytical bounds, and with the performance of PCCC and SCCC proposed in literature

The design and test results of a three-metal, double-poly, 0.35 μm; CMOS analog turbo decoder for the rate-1/3, block length 40, UMTS turbo code, are presented. A discrete-time model of analog decoding networks is also presented. This model can be used as a tool to both predict chip performance in a short time and give design guidelines for complex decoders, for which circuit-level simulations are impractical.

We consider the use of puncturing under the scope of space time turbo trellis codes. On one hand we consider the use of puncturing as a simple mechanism to allow varying the transmission rate, investigating the performance improvement/degradation when decreasing/increasing the spectral efficiency of the code. Then, we focus on the application of the EXIT chart technique to analyse the effect of puncturing on the code performance, and propose guidelines for the design of most suitable puncturing schemes.

This correspondence deals with the design and decoding of high-rate convolutional codes. After proving that every (n,n-1) convolutional code can be reduced to a structure that concatenates a block encoder associated to the parallel edges with a convolutional encoder defining the trellis section, the results of an exhaustive search for the optimal (n,n-1) convolutional codes is presented through various tables of best high-rate codes. The search is also extended to find the "best" recursive systematic convolutional encoders to be used as component encoders of parallel concatenated "turbo" codes. A decoding algorithm working on the dual code is introduced (in both multiplicative and additive form), by showing that changing in a proper way the representation of the soft information passed between constituent decoders in the iterative decoding process, the soft-input soft-output (SISO) modules of the decoder based on the dual code become equal to those used for the original code. A new technique to terminate the code trellis that significantly reduces the rate loss induced by the addition of terminating bits is described. Finally, an inverse puncturing technique applied to the highest rate "mother" code to yield a sequence of almost optimal codes with decreasing rates is proposed. Simulation results applied to the case of parallel concatenated codes show the significant advantages of the newly found codes in terms of performance and decoding complexity.

Recently, we proposed a new design technique to construct high-rate convolutional codes based on a structure formed by a block encoder and a simpler convolutional encoder (Graell i Amat, A. et al., IEEE Commun.. Lett., vol.5, no.11, p.453-5, 2001). The search technique was based on the optimization of the output weight enumerating function of the code. We now prove that every (n,n-1) convolutional code can be reduced to this structure. Following this result and suitably modifying our earlier search algorithm, we have been able to obtain the best (n, n-1) convolutional encoders to be used in the design of turbo codes. In this case, the search is aimed at the optimization of the input-output weight enumerating function of the encoders. We also derive an inverse puncturing method that can be applied to these high-rate convolutional codes to obtain a sequence of the (almost) best convolutional encoders. With such a method, a whole family of good encoders with different rates is obtained using the same encoder-decoder, thus permitting a great versatility that can be exploited in practical implementations.

Optimized high-rate convolutional codes are considered as the outer encoder of a serially concatenated structure where the inner encoder is replaced by the magnetic recording channel. Simulation results of the iterative decoding algorithm for an equalized Lorentzian channel model and a more realistic model that includes data-dependent transition noise are presented. The effect of precoder on performance is also studied, and simulation results are supported by EXIT chart analysis. All results refer to a comparison of the optimized codes with previously proposed schemes employing punctured codes or non optimized unpunctured codes with tail-biting decoding. Both trellis termination and tail-biting termination of the high-rate codes are studied. To terminate the code trellis we use the method derived by Amat, Montorsi and Benedetto, which only requires /spl nu/ (the code memory) tail-biting bits. Simulation results confirm the ML analysis: owing to their better distance properties, the scheme based on the new codes outperform state-of-the-art magnetic recording schemes based on both punctured and non optimized high-rate codes. The cost of using an unpunctured code versus the punctured one in terms of increased decoding complexity is turned into an advantage by applying to the high-rate code the soft-input soft-output (SISO) algorithm working on its dual trellis.

This paper considers the use of the best high-rate k/(k+1) convolutional codes obtained using the new construction technique described by Graell i Amat, Montorsi and Benedetto (see IEEE Communications Letters, vol.5, no.11, p.453-55, 2001) in a concatenated scheme. Simulation results for an AWGN channel and for a realistic magnetic recording channel are reported. It is shown that these codes, endowed with a decoding algorithm working on the dual code, yield performance improvements over the best known high-rate punctured codes with the same rate and memory in terms of both bit error probability and computational decoding complexity. For both the AWGN channel and the magnetic recording channel the new codes significantly lower the error floor with respect to known turbo-like code structures.

This paper presents an all-analog iterative decoding network for an EPR4 magnetic recording system. A powerful serially concatenated architecture is considered, consisting of a simple outer code, an interleaver with reasonable size and a rate 1 EPR4 channel as inner code. The analog chip design is based on analog 0.18 /spl mu/m CMOS technology. Simulation results for both digital and analog implementations are shown. Practical implementation issues such as considerations of mismatch effects over performance are also discussed.

We address several aspects of high-rate convolutional codes. Some results from an exhaustive search for codes optimized with respect to their distance spectrum, and encoders optimized with respect to their input-output weight enumerating function are presented. An additive version of the dual-SISO algorithm suitable to decode such codes with limited complexity is described, together with simulation results for both stand-alone and concatenated codes showing the codes performance improvement.