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In order to accommodate the ever-growing data from various, possibly independent, sources and the dynamic nature of data usage rates in practical applications, modern cloud data storage systems are required to be scalable, flexible, and heterogeneous. The recent rise of the blockchain technology is also moving various information systems towards decentralization to achieve high privacy at low costs. While codes with hierarchical locality have been intensively studied in the context of centralized cloud storage due to their effectiveness in reducing the average reading time, those for decentralized storage networks (DSNs) have not yet been discussed. In this paper, we propose a joint coding scheme where each node receives extra protection through the cooperation with nodes in its neighborhood in a heterogeneous DSN with any given topology. This work extends and subsumes our prior work on coding for centralized cloud storage. In particular, our proposed construction not only preserves desirable properties such as scalability and flexibility, which are critical in dynamic networks, but also adapts to arbitrary topologies, a property that is essential in DSNs but has been overlooked in existing works. [ABSTRACT FROM AUTHOR]

The two-dimensional magnetic recording (TDMR) technology promises storage densities of 10 Tbits/in2. However, when tracks are squeezed together, a bit stored in the two-dimensional (TD) grid suffers inter-symbol interference (ISI) from adjacent bits in the same track and inter-track interference (ITI) from nearby bits in the adjacent tracks. A bit is highly likely to be read incorrectly if it is isolated in the middle of a $3 \times 3$ square, surrounded by its complements, horizontally and vertically. We improve the reliability of TDMR systems by designing TD constrained codes that prevent these square isolation patterns. We exploit the way TD read heads operate to design our codes, and we focus on TD read heads that collect signals from three adjacent tracks. We represent the two-dimensional square isolation constraint as a one-dimensional constraint on an alphabet of eight non-binary symbols. We use this new representation to construct a non-binary lexicographically-ordered constrained code where one third of the input information bits are unconstrained. Our TD constraint codes are capacity-achieving, and the data protection is achieved with redundancy less than 3% and at modest complexity. [ABSTRACT FROM AUTHOR]

Line codes make it possible to mitigate interference, to prevent short pulses, and to generate streams of bipolar signals with no direct-current (DC) power content through balancing. They find application in magnetic recording (MR) devices, in Flash devices, in optical recording devices, and in some computer standards. This paper introduces a new family of fixed-length, binary constrained codes, named lexicographically-ordered constrained codes (LOCO codes), for bipolar non-return-to-zero signaling. LOCO codes are capacity-achieving, the lexicographic indexing enables simple, practical encoding and decoding, and this simplicity is demonstrated through analysis of circuit complexity. LOCO codes are easy to balance, and their inherent symmetry minimizes the rate loss with respect to unbalanced codes having the same constraints. Furthermore, LOCO codes that forbid certain patterns can be used to alleviate inter-symbol interference in MR systems and inter-cell interference in Flash systems. Numerical results demonstrate a gain of up to 10% in rate achieved by LOCO codes with respect to other practical constrained codes, including run-length-limited codes, designed for the same purpose. Simulation results suggest that it is possible to achieve a channel density gain of about 20% in MR systems by using a LOCO code to encode only the parity bits, limiting the rate loss, of a low-density parity-check code before writing. [ABSTRACT FROM AUTHOR]

We extend the lifetime of Flash memory in embedded processors by exploiting the fact that data from sensors is inherently analog. Prior work in the computer architecture community has assumed that all data is digital and has overlooked the opportunities available when working with analog data, such as the data recorded by sensors. In this paper, we introduce redundancy into the quantization of sensor data in order to provide several alternative representations. Notably, we tradeoff distortion—the difference between the sensed analog value and the digital quantization of that value—to improve lifetime. Our simulations show that when combining rate, distortion, and lifetime tradeoffs we can extend Flash lifetime at a far smaller capacity cost compared to prior work. More specifically the simulated system shows that it is possible to achieve up to $2.75 \times$ less capacity cost compared to redundant Flash memory and $1.29 \times$ less capacity cost compared to the state of the art coding schemes. [ABSTRACT FROM AUTHOR]

Distributed storage systems employ codes to provide resilience to failure of multiple storage disks. In particular, an $(n, k)$ maximum distance separable (MDS) code stores k$ symbols in n$ disks such that the overall system is tolerant to a failure of up to n-k$ disks. However, access to at least k$ disks is still required to repair a single erasure. To reduce repair bandwidth, array codes are used where the stored symbols or packets are vectors of length \ell$ . The MDS array codes have the potential to repair a single erasure using a fraction 1/(n-k)$ of data stored in the remaining disks. We introduce new methods of analysis, which capitalize on the translation of the storage system problem into a geometric problem on a set of operators and subspaces. In particular, we ask the following question: for a given (n, k)$ , what is the minimum vector-length or subpacketization factor \ell$ required to achieve this optimal fraction? For exact recovery of systematic disks in an MDS code of low redundancy, i.e., k/n>1/2$ , the best known explicit codes have a subpacketization factor \ell$ , which is exponential in k , for an arbitrary number of parity nodes $r=n-k$ , where $\delta=r/(r-1)$ . [ABSTRACT FROM AUTHOR]

Code diversity integrates space-time coding with beamforming by using a small number of feedback bits to select from a family of space-time codes. Different codes lead to different induced channels at the receiver, where Channel State Information (CSI) is used to instruct the transmitter how to choose the code. Feedback can be combined with sub-optimal low complexity decoding of the component codes to match Maximum-Likelihood (ML) decoding performance of any individual code in the family. It can also be combined with ML decoding of the component codes to improve performance beyond ML decoding performance of any individual code. Prior analysis of code diversity did not take into account the effect of the mobile speed and the delay in the feedback channel. This paper demonstrates the practicality of code diversity in space-time coded systems by showing that performance gains based on instantaneous feedback are largely preserved when long-range prediction of time-varying correlated fading channels is employed to compensate for the effect of the feedback delay. To maintain prediction accuracy for realistic SNR, noise reduction that employs oversampled pilots is used prior to fading prediction. We also propose a robust low pilot rate method that utilizes interleaving to improve the spectral efficiency. Simulations are presented for two channel models: the conventional Jakes model and a realistic physical channel model where the parameters associated with the reflectors vary in time and the arrival rays have different strengths and asymmetric arrival angles. [ABSTRACT FROM PUBLISHER]

In this paper, we study a practical coding scheme for the dying binary erasure channel (DBEC), which is a binary erasure channel (BEC) subject to a random fatal failure. We consider the rateless codes and optimize the degree distribution to maximize the average recovery probability. In particular, we first study the upper bound of the average recovery probability, based on which we define the objective function as the gap between the upper bound and the average recovery probability achieved by a particular degree distribution. We then seek the optimal degree distribution by minimizing the objective function. A simple and heuristic approach is also proposed to provide a suboptimal but good degree distribution. Simulation results are presented to show the significant performance gain over the conventional LT codes. [ABSTRACT FROM PUBLISHER]