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 investigate practical finite-blocklength classical–quantum channel coding over the quantum amplitude damping channel (ADC), aiming to transmit classical information reliably through quantum outputs. Our findings indicate that for any finite blocklength, a naive (uncoded) approach fails to offer any advantage over the ADC. Instead, sophisticated encoding strategies that leverage both classical error-correcting codes and quantum input states are crucial for realizing quantum performance gains at finite blocklengths.

We consider the problem of communication-constrained collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. A consensus-based algorithm is studied under the framework of differential privacy in order to protect each agent’s data. We give a theoretical convergence analysis of the proposed consensus-based algorithm for any bounded unknown distributions on the agents’ data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data, under an oracle decision rule and under some restrictions on the privacy level and the agents’ connectivity, which illustrates the benefit of private collaboration in an online setting under a communication restriction on the agents. The theoretical faster-than-local convergence guarantee is backed up by several numerical results.

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy and data variance estimation. Two differential privacy mechanisms protecting the releases of each agent’s current sample mean and two data variance estimation schemes are proposed, and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data. Moreover, we provide analytical performance curves for the case with an oracle class estimator, i.e., the class structure of the agents, where agents receiving data from distributions with the same mean are considered to be in the same class, is known. The theoreticalfaster-than-localconvergence guarantee is backed up by extensive numerical results showing that for a considered scenario with 200 agents from two or three classes the proposed approach indeed converges much faster than a fully local approach, and performs comparably to the ideal (all-data-public) case. This illustrates the benefit of private collaboration in an online setting.

We propose a new interpolation-based error decoding algorithm for (n,k) Reed-Solomon (RS) codes over a finite field of size q, where n = q − 1 is the length and k is the dimension. In particular, we employ the fast Fourier transform (FFT) together with properties of a circulant matrix associated with the error interpolation polynomial and some known results from elimination theory in the decoding process. The asymptotic computational complexity of the proposed algorithm for correcting any $t \leq \left\lfloor {\frac{{n - k}}{2}} \right\rfloor$ errors in an (n,k) RS code is of order ${\mathcal{O}}\left( {t{{\log }^2}t} \right)$ and ${\mathcal{O}}\left( {n{{\log }^2}n\log \log n} \right)$ over FFT-friendly and arbitrary finite fields, respectively, achieving the best currently known asymptotic decoding complexity, proposed for the same set of parameters.

We study the problem of pliable private information retrieval with side information (PPIR-SI) for the single server case. In PPIR, the messages are partitioned into nonoverlapping classes and stored in a number of noncolluding databases. The user wishes to retrieve any one message from a desired class while revealing no information about the desired class identity to the databases. In PPIR-SI, the user has prior access to some side information in the form of messages from different classes and wishes to retrieve any one new message from a desired class, i.e., the message is not included in the side information set, while revealing no information about the desired class to the databases. We characterize the capacity of (linear) single-server PPIR-SI for the case where the user’s side information is unidentified, i.e., the user is oblivious of the identities of its side information messages and the database structure. We term this case PPIR-USI. Surprisingly, we show that having side information, in PPIR-USI, is disadvantageous, in terms of the download rate, compared to PPIR.

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy. Two privacy mechanisms are proposed and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data. Numerical results show that for a considered scenario the proposed approach converges much faster than a fully local approach where agents do not share data, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.

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.

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.

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 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 straggler resiliency in decentralized learning using stochastic gradient descent under the notion of network differential privacy (DP). In particular, we extend the recently proposed framework of privacy amplification by decentralization by Cyffers and Bellet to include training latency—comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for training over a logical ring for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. Our results show a trade-off between training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme. Finally, results when training a logistic regression model on a real-world dataset are presented.

Private computation in a distributed storage system (DSS) is a generalization of the private information retrieval (PIR) problem. In such a setting, a user wishes to compute a function of$f$messages stored in$n$noncolluding coded databases, i.e., databases storing data encoded with an$[n,k]$linear storage code, while revealing no information about the desired function to the databases. We consider the problem of private linear computation (PLC) for coded databases. In PLC, a user wishes to compute a linear combination over the$f$messages while keeping the coefficients of the desired linear combination hidden from the databases. For a DSS setup where data is stored using a code from a particular family of linear storage codes, we derive an outer bound on the PLC rate, which is defined as the ratio of the desired amount of information and the total amount of downloaded information. In particular, the proposed converse is valid for any number of messages and linear combinations, and depends on the rank of the coefficient matrix obtained from all linear combinations. Further, we present a PLC scheme with rate equal to the outer bound and hence settle the PLC capacity for the considered class of linear storage codes. Interestingly, the PLC capacity matches the maximum distance separable coded capacity of PIR for the considered class of linear storage codes.

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.

Private information retrieval protocols guarantee that a user canprivatelyandlosslesslyretrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates when all files are stored uncoded on a single server. Information leakage is measured in terms of the average success probability for the server of correctly guessing the identity of the desired file. The main findings are: i) The derivation of the optimal tradeoff between download rate, distortion, and information leakage when the file size isinfinite. Closed-form expressions of the optimal tradeoff for the special cases of “no-leakage” and “no-privacy” are also given. ii) A novel approach based on linear programming (LP) to construct schemes for a finite file size and an arbitrary number of files. The proposed LP approach can be leveraged to find provably optimal schemes with corresponding closed-form expressions for the rate-distortion-leakage tradeoff when the database contains at most four bits. Finally, for a database that contains 320 bits, we compare two construction methods based on the LP approach with a nonconstructive scheme downloading subsets of files using a finite-length lossy compressor based on random coding.

We consider the problem of private polynomial computation (PPC) from a distributed storage system (DSS). In such setting a user wishes to compute a multivariate polynomial of degree at most$g$over$f$variables (or messages) stored in$n$noncolluding coded databases, i.e., databases storing data encoded with an$[n,k]$linear storage code, while revealing no information about the desired polynomial evaluation to the databases. For a DSS setup where data is stored using linear storage codes, we derive an outer bound on the PPC rate, which is defined as the ratio of the (minimum) desired amount of information and the total amount of downloaded information, and construct two novel PPC schemes. In the first scheme, we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product private information retrieval and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates, while asymptotically, as$f\rightarrow \infty $, the systematic scheme gives a significantly better computation retrieval rate compared to all known schemes up to some storage code rate that depends on the maximum degree of the candidate polynomials.

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the tradeoff between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this tradeoff can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself, minimizing the download cost. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with multiple file download.

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.

Private information retrieval protocols guarantee that a user can privately and losslessly retrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates in the single server scenario, i.e., all files are stored uncoded on a single server. In particular, we derive the optimal tradeoff between download rate, distortion, and information leakage when the file size is infinite and the information leakage is measured in terms of the average success probability for the server of correctly guessing the identity of the requested file. Moreover, we present a novel approach based on linear programming to construct schemes for a finite file size and an arbitrary number of files. When the database contains at most four bits, this approach can be leveraged to find provably optimal schemes.

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.

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.

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.

In this work, we perform a complete failure analysis of the interval-passing algorithm (IPA) for compressed sensing. The IPA is an efficient iterative algorithm for reconstructing a k-sparse nonnegative n-dimensional real signal x from a small number of linear measurements y . In particular, we show that the IPA fails to recover x from y if and only if it fails to recover a corresponding binary vector of the same support, and also that only positions of nonzero values in the measurement matrix are of importance to the success of recovery. Based on this observation, we introduce termatiko sets and show that the IPA fails to fully recover x if and only if the support of x contains a nonempty termatiko set, thus giving a complete (graph-theoretic) description of the failing sets of the IPA. Two heuristics to locate small-size termatiko sets are presented. For binary column-regular measurement matrices with no 4-cycles, we provide a lower bound on the termatiko distance, defined as the smallest size of a nonempty termatiko set. For measurement matrices constructed from the parity-check matrices of array low-density parity-check codes, upper bounds on the termatiko distance equal to half the best known upper bound on the minimum distance are provided for column-weight at most 7, while for column-weight 3, the exact termatiko distance and its corresponding multiplicity are provided. Next, we show that adding redundant rows to the measurement matrix does not create new termatiko sets, but rather potentially removes termatiko sets and thus improves performance. An algorithm is provided to efficiently search for such redundant rows. Finally, we present numerical results for different specific measurement matrices and also for protograph-based ensembles of measurement matrices, as well as simulation results of IPA performance, showing the influence of small-size termatiko sets.

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 polynomial computation (PPC) over noncolluding coded databases. In such a setting a user wishes to compute a multivariate polynomial of degree at most g over f variables (or messages) stored in multiple databases while revealing no information about the desired polynomial to the databases. We construct two novel PPC schemes, where the first is a generalization of our previous work in private linear computation for coded databases. In this scheme we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product private information retrieval and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates compared to the best known schemes from the literature for a small number of messages, while in the asymptotic case the rates match.

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 consider the problem of private computation (PC) in a distributed storage system. In such a setting a user wishes to compute a function of f messages replicated across n noncolluding databases, while revealing no information about the desired function to the databases. We provide an information-theoretically accurate achievable PC rate, which is the ratio of the smallest desired amount of information and the total amount of downloaded information, for the scenario of nonlinear computation. For a large message size the rate equals the PC capacity, i.e., the maximum achievable PC rate, when the candidate functions are the f independent messages and one arbitrary nonlinear function of these. When the number of messages grows, the PC rate approaches an outer bound on the PC capacity. As a special case, we consider private monomial computation (PMC) and numerically compare the achievable PMC rate to the outer bound for a finite number of messages.

The performance of maximum-likelihood (ML) decoding on the binary erasure channel for finite-length low-density parity-check (LDPC) codes from two random ensembles is studied. A tightened union-type upper bound on the ML decoding error probability based on the precise coefficients of the average weight spectrum is presented. For LDPC codes from the Gallager ensemble and the Richardson-Urbanke ensemble, new upper bounds on the ML decoding performance based on computing the rank of submatrices of the code parity-check matrix are derived. A new lower bound on the ML decoding threshold followed from the latter error probability bound is obtained. An improved lower bound on the error probability for codes with a known estimate on the minimum distance is presented as well. A new low-complexity near-ML decoding algorithm for quasi-cyclic LDPC codes is proposed and simulated. Its performance is compared to the simulated belief propagation and ML decoding performance and simulated performance of the best known improved iterative decoding techniques, as well as, with the derived upper bounds on the ML decoding performance and with decoding thresholds obtained by the density evolution technique.

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.

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 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 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 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.

Absorbing sets (ASs) are combinatorially defined objects existing in the Tanner graph of a low-density parity-check (LDPC) code that have been shown to cause failures in the iterative message-passing decoder when transmission occurs over the additive white Gaussian noise channel. In this paper, we propose an edge spreading approach to construct high rate array-based spatially-coupled LDPC codes by jointly optimizing the AS spectrum and the minimum distance. By considering general edge spreadings and by considering a larger memory, we show that strictly better codes can be constructed, both in terms of achievable minimum distance for small-to-moderate block lengths and in terms of the number of small ASs.

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.

In this work, we present an explicit construction of valid inequalities (using no auxiliary variables) for the convex hull of the so-called constant-weight embedding of a single parity-check (SPC) code over any prime field. The construction is based on classes of building blocks that are assembled to form the left-hand side of an inequality according to several rules. In the case of almost doubly-symmetric valid classes we prove that the resulting inequalities are all facet-defining, while we conjecture this to be true if and only if the class is valid and symmetric. Such sets of inequalities have not appeared in the literature before, have a strong theoretical interest, and can be used to develop an efficient (relaxed) adaptive linear programming decoder for general (non-SPC) linear codes over prime fields.

In this work, we propose a new technique of spatially-coupled low-density parity-check coding within a threshold-based lossy forwarding protocol for a multiple access relaying system. Here, block Rayleigh fading is assumed for all transmission links and error-free decoding at the relay is not required. Two schemes are presented in which the relay computes log-likelihood ratios (LLRs) of the network-coded symbols (from the sources) based on the received signals. By comparing the LLRs to a preset threshold, the relay decides to forward hard decisions when the network-coded symbol reliability is higher than the preset threshold. Otherwise, the relay decides to stay silent (first scheme) or to forward the LLRs to the destination (second scheme). Finally, we modify the LLR combining at the destination, based on an expression for the uncoded bit-error probability which is tailored to the proposed schemes. Simulation results demonstrate that the proposed relay protocol yields an improved bit-error rate performance compared to competitive schemes proposed in the literature.

An array low-density parity-check (LDPC) code is a quasi-cyclic LDPC code specified by two integers q and m, where q is an odd prime and m ≤ q. The exact minimum distance, for small q and m, has been calculated, and tight upper bounds on it for m ≤ 7 have been derived. In this work, we study the minimum distance of the spatially-coupled version of these codes. In particular, several tight upper bounds on the optimal minimum distance for coupling length at least two and m = 3; 4; 5, that are independent of q and that are valid for all values of q ≥ q0where q0depends on m, are presented. Furthermore, we show by exhaustive search that by carefully selecting the edge spreading or unwrapping procedure, the minimum distance (when q is not very large) can be significantly increased, especially for m = 5.

In this work, we consider adaptive linear programming (LP) decoding of ternary linear codes, i. e., linear codes over the finite field Fqwith q = 3 elements. In particular, we characterize completely the codeword polytope (or the convex hull) of the binary image, under Flanagan's embedding, of a ternary single parity-check code. Then, this characterization is used to develop an efficient adaptive LP decoder for ternary codes. Numerical experiments confirm that this decoder is very efficient compared to a static LP decoder and scales well with both block length and check node degree. Finally, we briefly consider the case of nonbinary codes over the finite field Fqwith q = 3melements, where m > 1 is a positive integer.

In this work, we consider efficient maximum-likelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel (IEEE Trans. Inf. Theory, 2012) for obtaining lower bounds. We have compared our proposed algorithm to the state-of-the-art commercial integer program solver CPLEX, and for all considered codes our approach is faster for both low and high signal-to-noise ratios. For instance, for the benchmark (155, 64) Tanner code our algorithm is more than 11 times as fast as CPLEX for an SNR of 1.0 dB on the additive white Gaussian noise channel. By a small modification, our algorithm can be used to calculate the minimum distance, which we have again verified to be much faster than using the CPLEX solver.

In this work, we present an upper bound on the minimum distance of array low-density parity-check (LDPC) codes. An array LDPC code is a quasi-cyclic LDPC code specified by two integers q and m, where q is an odd prime and m ≤ q. In the literature, the minimum distance of these codes (denoted by d(q,m)) has been thoroughly studied for m ≤ 5. Both exact results, for small values of q and m, and general (i.e., independent of q) bounds have been established. For m ≤ 6, the best known minimum distance upper bound, derived by Mittelholzer (IEEE Int. Symp. Inf. Theory, Jun./Jul. 2002), is d(q, 6) ≤ 32. In this work, we derive an improved upper bound of d(q, 6) ≤ 20 by using the concept of a template support matrix of a codeword. The bound is tight with high probability in the sense that we have not been able to find codewords of strictly lower weight for several values of q using a minimum distance probabilistic algorithm. Finally, we provide new specific minimum distance results for m ≤ 6 and low-to-moderate values of q ≤ 79.

A counter braid (CB) is a novel counter architecture for per-flow measurements on high-speed links. CBs were introduced by Lu et al. in 2007 and they have an asymptotic compression rate (under optimal decoding) matching the entropy lower bound of the flow size distribution. A CB has a layered structure and compresses the flow sizes “on-the-fly” as new packets arrive. In this work, we apply spatial coupling to CBs and show numerically that spatially-coupled CBs (SC-CBs) exhibit improved iterative decoding thresholds. Furthermore, we show that single-layer SC-CBs, which are in fact compressed sensing schemes for nonnegative signals, have a superior undersampling-sparsity phase transition trajectory (under iterative decoding) in the sparse region compared to random Gaussian measurement matrices with ℓ1-norm minimization reconstruction.

This paper discusses a new channel model and code design for the reader-to-tag channel in near-field passive radio frequency identification (RFID) systems using inductive coupling as a power transfer mechanism. If the receiver resynchronizes its internal clock each time a bit is detected, the bit-shift channel used previously in the literature to model the reader-to-tag channel needs to be modified. In particular, we propose a discretized Gaussian shift channel as a new channel model in this scenario. We introduce the concept of quantifiable error avoidance, which is much simpler than error correction. The capacity is computed numerically, and we also design some new simple codes for error avoidance on this channel model based on insights gained from the capacity calculations. Finally, some simulation results are presented to compare the proposed codes to the Manchester code and two previously proposed codes for the bit-shift channel model.

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.

In this paper, we study the minimum/stopping distance of array low-density parity-check (LDPC) codes. An array LDPC code is a quasi-cyclic LDPC code specified by two integers q and m, where q is an odd prime and m q. In the literature, the minimum/stopping distance of these codes (denoted by d(q, m) and h(q, m), respectively) has been thoroughly studied for m 5. Both exact results, for small values of q and m, and general (i.e., independent of q) bounds have been established. For m = 6, the best known minimum distance upper bound, derived by Mittelholzer, is d(q, 6) 32. In this paper, we derive an improved upper bound of d(q, 6) 20 and a new upper bound d(q, 7) 24 by using the concept of a template support matrix of a codeword/stopping set. The bounds are tight with high probability in the sense that we have not been able to find codewords of strictly lower weight for several values of q using a minimum distance probabilistic algorithm. Finally, we provide new specific minimum/stopping distance results for m 7 and low-to-moderate values of q ≤79.

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.

In this work, we consider the power spectrum of error-control codes designed for the reader-to-tag channel in near-field passive radio frequency identification (RFID) systems using inductive coupling as a power transfer mechanism. In contrast to previous works, binary phase-shift keying is considered, and the power spectral density is computed for two of the codes (and one runlength constraint) considered in a recent paper by Barbero et al. (Inf. Theory Appl., San Diego, CA, 2011), and for two new codes introduced in this paper. Furthermore, we compute the total power transferred to the tag as a function of the inter-coil separation for different codes (and one runlength constraint).

We describe the application of edge-local complementation (ELC) to a Tanner graph associated with a binary linear code, C. Various properties of ELC are described, mainly the special case of isomorphic ELC operations and the relationship to the automorphism group of the code, Aut(C), and the generalization of ELC to weight-bounding ELC (WB-ELC) operations under which the number of edges remains upper-bounded. ELC generates all systematic parity-check matrices (the orbit) of the code, so WB-ELC facilitates a restriction to low-weight matrices of this orbit. We propose using ELC and WB-ELC as a source of diversity, to improve iterative soft-input soft-output decoding of high-density parity-check (HDPC) codes, with the sum-product algorithm (SPA). A motivation of ELC-enhanced SPA decoding is locality; that diversity is achieved by local graph action, and is well-suited to the local actions that constitute the SPA and allows for parallel software implementation. Simulation data on the error-rate performance of the proposed SPA-ELC and SPA-WBELC iterative decoding algorithms is shown for several HDPC codes. A gain is reported over SPA decoding, and over a recently proposed algorithm to decode HDPC codes using permutations from Aut(C). ELC-enhanced decoding extends the scope of iterative decoding to codes with trivial Aut(C).

An interleaver is a critical component for the channel coding performance of turbo codes. Algebraic constructions are of particular interest because they admit analytical designs and simple, practical hardware implementations. Also, the recently proposed quadratic permutation polynomial (QPP)-based interleavers by Sun and Takeshita have provided excellent performance for short-to-medium block lengths, and have been selected for the 3GPP LTE standard. In this paper, we derive some upper bounds on the best achievable minimum distance dminof QPP-based conventional binary turbo codes (with tailbiting termination, or dual termination when the interleaver length N is sufficiently large) that are tight for larger block sizes. In particular, we show that the minimum distance is at most 2(2v+1+ 9), independent of the interleaver length, when there exists an inverse polynomial of degree two, where v is the degree of the primitive feedback and monic feedforward polynomials. However, allowing the QPP to have no inverse polynomials of degree two may give strictly larger minimum distances (and lower multiplicities). In particular, we provide several QPPs with no quadratic inverse for some of the 3GPP LTE interleaver lengths giving a dminwith the 3GPP LTE constituent encoders which is strictly larger than 50. For instance, we have found a QPP for N = 6016 which gives an estimated dminof 57. Furthermore, we provide the exact minimum distances and the corresponding multiplicities for all 3GPP LTE turbo codes (with dual termination) which shows that the best minimum distance is 51. Finally, we compute the best achievable minimum distances with QPP interleavers for all 3GPP LTE interleaver lengths N ≤ 4096, and compare these minimum distances with the ones we get when using the 3GPP LTE polynomials.

Inductive coupling is a technique wherein one device (the reader) induces an electrical current in another device (the tag), thereby providing not only power for the tag, but also a communication channel. In this paper, we focus exclusively on the reader-to-tag channel. The first part of this paper presents modulation codes that possess a high minimum and a high average power. This is important, since the 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. The presented modulation codes compare favorably to codes used in radio frequency identification applications today. The second part of the paper describes modulation codes with some error-correcting capabilities. In fact, most errors in the reader-to-tag channel are due to incorrect timing. Here, we propose to model the timing errors in the reader-to-tag communication channel by a simple bit-shift channel, and we will present optimal (in the sense of maximizing the code rate for a given block length) single bit-shift error-correcting codes for this simple bit-shift channel that also have large average power.

In an earlier transactions paper, Rosnes and Ytrehus presented an efficient algorithm for determining all stopping sets of low-density parity-check (LDPC) codes, up to a specified weight, and also gave results for a number of well-known codes including the family of IEEE 802.16e LDPC codes, commonly referred to as the WiMax codes. It is the purpose of this short paper to review the algorithm for determining the initial part of the stopping set weight spectrum (which includes the codeword weight spectrum), and to provide some improvements to the algorithm. As a consequence, complete stopping set weight spectra up to weight 32 (for selected IEEE 802.16e LDPC codes) can be provided, while in previous work only stopping set weights up to 28 are reported. In the published standard for the IEEE 802.16e codes there are two methods of construction presented, depending upon the code rate and the code length. We compare the stopping sets of the resulting codes and provide complete stopping set weight spectra (up to five terms) for all IEEE 802.16e LDPC codes using both construction methods.

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.

In this work, we consider iterative soft-decision decoding of binary linear codes using a generalized Tanner graph. A generalized Tanner graph is constructed from the code's Tanner graph representation under the objective of mitigating the effect of small cycles. Then, iterative decoding is applied to the generalized Tanner graph using trellis-based soft-input soft-output decoding in the generalized check nodes. When combined with the stochastic shifting algorithm of Jiang and Narayanan (IEEE Commun. Letters, 2004), the proposed decoding method provides both improved error rate performance and reduced average decoding complexity, without increasing the worst-case decoding complexity.

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 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, 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.

This work is an extension of our previous work on an iterative soft-decision decoder for high-density parity-check codes, using a graph-local operation known as edge-local complementation (ELC). Inferred least reliable codeword positions are targeted by an ELC stage in between sum-product algorithm iterations. A gain is shown over related iterative decoding algorithms - mainly due to an improved heuristic to determine optimum ELC locations in the Tanner graph - both in error-rate performance, as well as complexity in terms of a significant reduction in the required number of ELC operations. We also present a novel damping operation, generalized to the graph-local setting where extrinsic information remains on edges not affected by ELC.

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.

In this work, we will consider coding for a bit-shift channel with applications to inductively coupled channels. Inductive coupling is a technique wherein one device (the reader) induces an electrical current in another device (the tag), thereby providing not only power for the tag, but also a communications channel. Most errors in the reader-to-tag channel are due to incorrect timing. In this work, we propose to model the timing errors in the reader-to-tag communications channel by a simple bit-shift channel. We will present optimal single bit-shift error-correcting codes for this simple bit-shift channel that also have large average power. This is important, since the 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, we propose to enhance the performance of the sum-product algorithm (SPA) by interleaving SPA iterations with a random local graph update rule. This rule is known as edge local complementation (ELC), and has the effect of modifying the Tanner graph while preserving the code. We have previously shown how the ELC operation can be used to implement an iterative permutation group decoder (SPA-PD)-one of the most successful iterative soft-decision decoding strategies at small blocklengths. In this work, we exploit the fact that ELC can also give structurally distinct parity-check matrices for the same code. Our aim is to describe a simple iterative decoder, running SPA-PD on distinct structures, based entirely on random usage of the ELC operation. This is called SPA-ELC, and we focus on small blocklength codes with strong algebraic structure. In particular, we look at the extended Golay code and two extended quadratic residue codes. Both error rate performance and average decoding complexity, measured by the average total number of messages required in the decoding, significantly outperform those of the standard SPA, and compares well with SPA-PD. However, in contrast to SPA-PD, which requires a global action on the Tanner graph, we obtain a performance improvement via local action alone. Such localized algorithms are of mathematical interest in their own right, but are also suited to parallel/distributed realizations.

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 paper, we consider the pairwise error probability (PEP) of a linear programming (LP) decoder for a general binary linear code as formulated by Feldman(IEEE Trans. Inf. Theory, Mar. 2005) on an independent (or memoryless) Rayleigh flat-fading channel with coherent detection and perfect channel state information (CSI) at the receiver. Let${\bf H}$be a parity-check matrix of a binary linear code and consider LP decoding based on${\bf H}$. The output of the LP decoder is always apseudocodeword. We will show that the PEP of decoding to a pseudocodeword${\mmb \omega}$when the all-zero codeword is transmitted on the above-mentioned channel, behaves asymptotically as$K({\mmb \omega}) \cdot (E_s/N_0)^{-\vert\chi({\mmb \omega})\vert}$, where$\chi({\mmb \omega})$is the support set of${\mmb \omega}$, i.e., the set of nonzero coordinates,$E_s/N_0$is the average signal-to-noise ratio (SNR), and$K({\mmb \omega})$is a constant independent of the SNR. Note that the support set$\chi({\mmb \omega})$of${\mmb \omega}$is astopping set. Thus, the asymptotic decay rate of the error probability with the average SNR is determined by the size of the smallest nonempty stopping set in the Tanner graph of${\bf H}$. As an example, we analyze the well-known$(155,64)$Tanner code and present performance curves on the independent Rayleigh flat-fading channel.

In this work, we introduce an efficient algorithm to find all stopping sets, of size less than some threshold, of a fixed low-density parity-check (LDPC) matrix. The solution is inspired by the algorithm proposed by Rosnes and Ytrehus in 2005 to find an exhaustive list of all small-size turbo stopping sets in a turbo code. The efficiency of the proposed algorithm is demonstrated by several numerical examples. For instance, we have applied the algorithm to the well-known (3, 5)-regular (155, 64) Tanner code and found all stopping sets of size at most 18 in about 1 min on a standard desktop computer. Also, we have verified that the minimum stopping set size of the (4896, 2474) Ramanujan-Margulis code is indeed 24, and that the corresponding multiplicity is exactly 204. Furthermore, we have applied the algorithm to the IEEE 802.16e LDPC codes and determined the minimum stopping set size and the corresponding multiplicity exactly for these codes. Finally, as an application, we present a greedy algorithm to find a small number of redundant parity checks to add to the original parity-check matrix in order to remove all stopping sets in the corresponding Tanner graph of size less than the minimum distance. An extensive case study of the (155, 64) Tanner code illustrates the usefulness of the algorithm, and we present a 110 times 155 redundant parity-check matrix for this code with no stopping sets of size less than the minimum distance. Simulation results of iterative decoding on the binary erasure channel show performance improvements for low-to-medium erasure probabilities when this redundant parity-check matrix is used for decoding.

In this paper, we introduce stopping sets for iterative row-column decoding of product codes using optimal constituent decoders. When transmitting over the binary erasure channel (BEC), iterative row-column decoding of product codes using optimal constituent decoders will either be successful, or stop in the unique maximum-size stopping set that is contained in the (initial) set of erased positions. Let Cpdenote the product code of two binary linear codes Ccand Crof minimum distances dc and drand second generalized Hamming weights d2(Cc) and d2(Cr), respectively. We show that the size sminof the smallest noncode- word stopping set is at least mm(drd2(Cc),dcd2(Cr)) > drdc, where the inequality follows from the Griesmer bound. If there are no codewords in Cpwith support set S, where S is a stopping set, then S is said to be a noncodeword stopping set. An immediate consequence is that the erasure probability after iterative row-column decoding using optimal constituent decoders of (finite-length) product codes on the BEC, approaches the erasure probability after maximum-likelihood decoding as the channel erasure probability decreases. We also give an explicit formula for the number of noncodeword stopping sets of size smin, which depends only on the first nonzero coefficient of the constituent (row and column) first and second support weight enumerators, for the case when d2(Cr)rand d2(Cc)c. Finally, as an example, we apply the derived results to the product of two (extended) Hamming codes and two Golay codes.

In this work, we introduce an efficient algorithm to find all stopping sets of size less than some threshold of a fixed low-density parity-check (LDPC) matrix. The solution is inspired by the algorithm proposed by Rosnes and Ytrehus in 2005 to find an exhaustive list of all small-size turbo stopping sets in a turbo code. The efficiency of the proposed algorithm is demonstrated by several numerical examples. For instance, we have applied the algorithm to the well-known (3, 5)-regular (155,64) Tanner code and found all stopping sets of size at most 18 in about one minute on a standard desktop computer. Also, we have verified that the minimum stopping set size of the (4896,2474) Ramanujan-Margulis code is indeed 24, and that the corresponding multiplicity is exactly 204.

In this work, we consider the pairwise error probability (PEP) of a linear programming (LP) decoder for a general binary linear code as formulated by Feldman et al. (IEEE Trans. Inform. Theory, Mar. 2005) on an independent (or memoryless) Rayleigh flat-fading channel with coherent detection and perfect channel state information (CSI) at the receiver. Let H be a parity-check matrix of a binary linear code and consider LP decoding based on H. The output of the LP decoder is always a pseudo-codeword. We will show that the PEP of decoding to a pseudo-codeword omega when the all-zero codeword is transmitted on an independent Rayleigh flat-fading channel with coherent detection and perfect CSI at the receiver, behaves asymptotically as K(omega)ldr(Es/NO)-chi(omega), where chi(omega) is the support set of omega, i.e., the set of non-zero coordinates, Es/NOis the average signal-to-noise ratio (SNR), and K(omega) is a constant independent of the SNR. Thus, the asymptotic decay rate of the error probability with the average SNR is determined by the size of the smallest non-empty stopping set in the Tanner graph of H.

This paper is devoted to the finite-length analysis of turbo decoding over the binary erasure channel (BEC). The performance of iterative belief-propagation decoding of low-density parity-check (LDPC) codes over the BEC can be characterized in terms ofstopping sets. We describe turbo decoding on the BEC which is simpler than turbo decoding on other channels. We then adapt the concept of stopping sets to turbo decoding and state an exact condition for decoding failure. Apply turbo decoding until the transmitted codeword has been recovered, or the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum-sizeturbo stopping setwhich is also a subset of the set of erased positions. Furthermore, we present some improvements of the basic turbo decoding algorithm on the BEC. The proposed improved turbo decoding algorithm has substantially better error performance as illustrated by the given simulation results. Finally, we give an expression for the turbo stopping set size enumerating function under the uniform interleaver assumption, and an efficient enumeration algorithm of small-size turbo stopping sets for a particular interleaver. The solution is based on the algorithm proposed by Garelloet al.in 2001 to compute an exhaustive list of all low-weight codewords in a turbo code.

An interleaver is a critical component for the channel coding performance of turbo codes. Algebraic constructions are of particular interest because they admit analytical designs and simple, practical hardware implementation. Also, the recently proposed quadratic permutation polynomial (QPP) based interleavers by Sun and Takeshita (IEEE Trans. Inform. Theory, Jan. 2005) provide excellent performance for short-to-medium block lengths. In this work the minimum distance of turbo codes with QPP-based interleavers is considered in detail. Large tables of optimum (in terms of turbo code minimum distance and multiplicity) QPPs for turbo codes with 8-state and 16-state constituent codes are presented. The minimum distances are compared to existing results in the literature on dithered relative prime (DRP) interleavers. The optimality of the new tables makes them an excellent source of information to advance the understanding of permutation polynomial (PP) based interleavers

This letter introduces a generalized version of Kay's estimator for the frequency of a single complex sinusoid in complex additive white Gaussian noise. The Kay estimator is a maximum-likelihood (ML) estimator at high signal-to-noise ratio (SNR) based on differential phase measurements with a delay of one symbol interval. In this letter, the corresponding ML estimator with an arbitrary delay in the differential phase measurements is derived. The proposed estimator reduces the variance at low SNR, compared with Kay's original estimator. For certain delay values, explicit expressions for the window function and the corresponding high SNR variance of the proposed generalized Kay (GK) estimator are presented. Furthermore, for some delay values, the window function is nearly uniform and the implementation complexity is reduced, compared with the original Kay estimator. For a delay value of two, we show that the variance at asymptotically high SNR approaches the Cramer-Rao bound as the sequence length tends to infinity. We also explore the effect of exchanging the order of summation and phase extraction for reduced-complexity reasons. The resulting generalized weighted linear predictor estimator and the GK estimator are compared with both autocorrelation-based and periodogram-based estimators in terms of computational complexity, estimation range, and performance at both low and high SNRs.

In this paper, we introduce an algorithm to optimize the performance in the error-floor region of bit-interleaved turbo-coded modulation (BITCM) on the additive white Gaussian noise channel. The key ingredient is an exact turbo code weight distribution algorithm producing a list of all codewords in the underlying turbo code of weight less than a given threshold. In BITCM, the information sequence is turbo-encoded, bit-interleaved, and mapped to signal points in a signal constellation. Using the union-bounding technique, we show that a well-designed bit interleaver is crucial to have a low error floor. Furthermore, the error-rate performance in the waterfall region depends on the bit interleaver, since the level of protection from channel noise on the bit level depends on the bit position and the neighboring bit values within the same symbol in the transmitted sequence. We observe a tradeoff between error-rate performance in the waterfall and error-floor regions, as illustrated by an extensive case study of a high-rate BITCM scheme. This tradeoff is typical in iterative decoding of turbo-like codes. The reported case study shows that it is possible to design bit interleavers with our proposed algorithm with equal or better performance in the waterfall region and superior performance in the error-floor region, compared with randomly generated bit interleavers. In particular, we were able to design BITCM schemes with maximum-likelihood decoding frame-error rates of 10-12and 10-17at 2.6 and 3.8 dB away from unconstrained channel capacity, at spectral efficiencies of 3.10 and 6.20 b/s/Hz using square 16 and 256-quadrature amplitude modulation signal constellations, respectively

In this work we consider the design of good rate-compatible puncturing patterns for turbo codes in the error floor region. The key ingredient is an exact turbo code weight distribution algorithm producing a list L of all codewords in a turbo code of weight less than a given threshold. The proposed puncturing pattern design algorithm is a two step procedure. In the first step of the algorithm the bit-positions to be punctured are chosen sequentially in a greedy manner. In more detail, we choose at each iteration step the bit-position that is contained in the fewest number of minimum weight codewords from L. Since the list L is not necessarily exhaustive after puncturing (i.e., it does not necessarily contain all codewords of weight less than some threshold of the punctured code), the list is recomputed after a predetermined number of bit-position selections. The second part of the algorithm uses the chosen bit-positions as a starting point for local hill climbing. Note that the algorithm produces families a rate-compatible puncturing patterns for turbo codes. When only the first step of the algorithm is performed, larger families of rate-compatible puncturing patterns are constructed. We illustrate the usefulness of the proposed algorithm by some case studies on both short and moderate-length turbo codes. The reported case studies show that it is possible to improve the minimum distance to some extent with irregular puncturing compared to regular puncturing

This paper is devoted to the finite-length analysis of turbo decoding over the binary erasure channel (BEC). The performance of iterative belief-propagation (BP) decoding of low-density parity-check (LDPC) codes over the BEC can be characterized in terms of stopping sets. In the first part we describe turbo decoding on the BEC which is simpler than turbo decoding on other channels. We then adapt the concept of stopping sets to turbo decoding and state an exact condition for decoding failure. Apply turbo decoding until the transmitted codeword has been recovered, or until the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum size turbo stopping set which is also a subset of the set of erased positions. In the second part we present some improvements of the basic turbo decoding algorithm on the BEC. The proposed improved turbo decoding algorithm has substantially better error performance as illustrated by the given simulation results

The performance of turbo decoding on the binary erasure channel (BEC) can be characterized in terms of turbo stopping sets. Apply turbo decoding until the transmitted codeword has been recovered, or until the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum size turbo stopping set which is also a subset of the set of erased positions. The concept of turbo stopping sets is an adaptation of stopping sets from the theory of iterative belief-propagation (BP) decoding of low-density parity-check (LDPC) codes. The main results in this work are an expression for the turbo stopping set size enumerating function under the uniform interleaver assumption, and an efficient enumeration algorithm of small-size turbo stopping sets for a particular interleaver. The solution is based on the algorithm proposed by Garello et al. in 2001 to compute an exhaustive list of all low-weight codewords in a turbo code

We discuss algorithms for determining exactly the lower terms of the weight distribution of a turbo code. Several improvements on the recently introduced algorithm by Garello et al. are outlined. The techniques presented in this letter improve the observed asymptotic complexity by a factor proportional to the information length. As an example, the improved algorithm is applied to the determination of the minimum distance of all universal mobile telecommunications system turbo codes. We further apply the improved algorithm to high-rate turbo codes using high-rate nonpunctured constituent codes. To reduce complexity, the constituent codes are represented by a minimal information bit-oriented trellis.

In this paper we introduce an algorithm to optimize the performance in the error floor region of bit-interleaved turbo-coded modulation (BITCM) on the additive white Gaussian noise (AWGN) channel. The key ingredient is an exact turbo code weight distribution algorithm producing a list of all codewords in the underlying turbo code of weight less than a given threshold. In BITCM, the information sequence is turbo-encoded, bit- interleaved, and mapped to signal points in a signal constellation. Using the union bounding technique, we show that a well-designed bit-interleaver is crucial to have a low error floor. Furthermore, the error rate performance in the waterfall region depends on the bit-interleaver, since the level of protection from channel noise on the bit-level depends on the bit-position and the neighboring bit values within the same symbol in the transmitted sequence. We observe a trade-off between error rate performance in the waterfall and error floor regions as illustrated by an extensive case study of a high-rate BITCM scheme. The reported case study shows that it is possible to design bit-interleavers with our proposed algorithm with equal or better performance in the waterfall region and superior performance in the error floor region compared to randomly generated bit-interleavers. In particular, we were able to design BITCM schemes with maximum-likelihood decoding frame error rates of 10/sup -12/ and 10/sup -17/ at 2.6 dB and 3.8 dB away from unconstrained channel capacity at spectral efficiencies of 3.10 and 6.20 b/s/Hz using square 16 and 256-QAM signal constellations, respectively.

In this work we introduce an algorithm to optimize the performance in the error floor region of bit-interleaved turbo-coded modulation (BITCM) on the additive white Gaussian noise (AWGN) channel. The key ingredient is an exact turbo code weight distribution algorithm producing a list of all codewords in the underlying turbo code of weight less than a given threshold. Using the union bounding technique, we show that a well-designed bit-interleaver is crucial to have a low error floor. Furthermore, the error rate performance in the waterfall region depends on the bit-interleaver, since the level of protection from channel noise on the bit-level depends on the bit-position and the neighboring bit values within the same symbol in the transmitted sequence. We observe a trade-off between error rate performance in the waterfall and error floor regions as illustrated by an extensive case study of a high-rate BITCM scheme.

We introduce general sphere packing bounds for convolutional codes. These improve upon the Heller bound [J.L.Heller (1968)] for high rate convolutional codes. For example, based on the Heller bound, McEliece [2, p. 1114] suggested that for a rate (n-1)/n code of free distance 5 with /spl nu/ memory elements in its minimum encoder, asymptotically as /spl nu/ /spl rarr/ /spl infin/ it holds that n /spl les/ 2/sup (/spl nu/+1)/2/. a simple corollary of our bounds shows that in this case, n /spl lsim/2/sup /spl nu//2/, an improvement by a factor of /spl radic/2. The bound can be further strengthened.

We introduce general sphere-packing bounds for convolutional codes. These improve upon the Heller (1968) bound for high-rate convolutional codes. For example, based on the Heller bound, McEliece (1998) suggested that for a rate (n - 1)/n convolutional code of free distance 5 with /spl nu/ memory elements in its minimal encoder it holds that n /spl les/ 2/sup (/spl nu/+1)/2/. A simple corollary of our bounds shows that in this case, n < 2/sup /spl nu//2/, an improvement by a factor of /spl radic/2. The bound can be further strengthened. Note that the resulting bounds are also highly useful for codes of limited bit-oriented trellis complexity. Moreover, the results can be used in a constructive way in the sense that they can be used to facilitate efficient computer search for codes.

This article introduces a generalized version of Kay's (1989) estimator for the frequency of a single complex sinusoid in complex additive white Gaussian noise (AWGN). Kay developed a maximum likelihood (ML) estimator based on differential phase measurements with a delay of one symbol interval. In this paper the corresponding ML estimator with an arbitrary delay in the differential phase measurements is derived. The proposed estimator reduces the variance at low SNR compared with Kay's original estimator. The penalty for the reduced variance is a reduced estimation range. With sufficient delay the window function in the generalized Kay estimator is nearly uniform and the implementation complexity is reduced. Performance comparisons with an estimator based on differential phase measurements with a uniform window function is given. The proposed estimator is also compared with the Fitz (1991) estimator.