Regenerating codes for distributed storage have attracted much research interest in the past decade. Such codes trade the bandwidth needed to repair a failed node with the overall amount of data stored in the network. Minimum storage regenerating (MSR) codes are an important class of optimal regenerating codes that minimize (first) the amount of data stored per node and (then) the repair bandwidth. Specifically, an [n, k, d]-(α) MSR code C over Fqstores a file F consisting of αk symbols over Fqamong n nodes, each storing α symbols, in such a way that: 1) the file F can be recovered by downloading the content of any k of then nodes and 2) the content of any failed node can be reconstructed by accessing any d of the remaining n - 1 nodes and downloading α/(d-k+1) symbols from each of these nodes. In practice, the file F is typically available in uncoded form on some k of the n nodes, known as systematic nodes, and the defining node-repair condition above can be relaxed to requiring the optimal repair bandwidth for systematic nodes only. Such codes are called systematic-repair MSR codes. Unfortunately, finite-α constructions of [n, k, d] MSR codes are known only for certain special cases: either low rate, namely k/n ≤ 0.5, or high repair connectivity, namely d = n - 1. Our main result in this paper is a finite-α construction of systematic-repair [n, k, d] MSR codes for all possible values of parameters n, k, d. We also introduce a generalized construction for [n, k] MSR codes to achieve the optimal repair bandwidth for all values of d simultaneously.

Regenerating codes for distributed storage have attracted much research interest in the past decade. Such codes trade the bandwidth needed to repair a failed node with the overall amount of data stored in the network. Minimum storage regenerating (MSR) codes are an important class of optimal regenerating codes that minimize (first) the amount of data stored per node and (then) the repair bandwidth. Specifically, an [n, k, d]-(α) MSR code C over Fqis defined as follows. Using such a code C, a file F consisting of αk symbols over Fq can be distributed among n nodes, each storing α symbols, in such a way that: . the file F can be recovered by downloading the content of any k of the n nodes; and . the content of any failed node can be reconstructed by accessing any d of the remaining n -1 nodes and downloading α/(d-k+1) symbols from each of these nodes. A common practical requirement for regenerating codes is to have the original file F available in uncoded form on some k of the n nodes, known as systematic nodes. In this case, several authors relax the defining node-repair condition above, requiring the optimal repair bandwidth of dα/(d-k+1) symbols for systematic nodes only. We shall call such codes systematic-repair MSR codes. Unfortunately, explicit constructions of [n, k, d] MSR codes are known only for certain special cases: either low rate, namely k/n ≤ 0.5, or high repair connectivity, namely d = n -1. Although setting d = n - 1 minimizes the repair bandwidth, it may be impractical to connect to all the remaining nodes in order to repair a single failed node. Our main result in this paper is an explicit construction of systematic-repair [n, k, d] MSR codes for all possible values of parameters n, k, d. In particular, we construct systematic-repair MSR codes of high rate k/n > 0.5 and low repair connectivity k ≤ d ≤ n - 1. Such codes were not previously known to exist. In order to construct these codes, we solve simultaneously several repair scenarios, each of which is expressible as an interference alignment problem. Extension of our results beyond systematic repair remains an open problem.

We consider the problem of synchronizing coded data in distributed storage networks undergoing insertion and deletion edits. We present modifications of distributed storage codes that allow updates in the parity-check values to be performed with one round of communication at low bit rates and with small storage overhead. Our main contributions are novel protocols for synchronizing frequently updated and semi-static data based on functional intermediary coding involving permutation and Vandermonde matrices.

We consider the problem of synchronizing data in distributed storage networks under edits that include deletions and insertions. We present modifications of codes on distributed storage systems that allow updates in the parity-check values to be performed with one round of communication at low bit rates and a small storage overhead. Our main contributions are novel protocols for synchronizing both frequently updated and semi-static data, and protocols for data deduplication applications, based on intermediary coding using permutation and Vandermonde matrices.

We consider linear cyclic codes with the locality property, or locally recoverable codes (LRC codes). A family of LRC codes that generalizes the classical construction of Reed-Solomon codes was constructed in a recent paper by I. Tamo and A. Barg (IEEE Trans. IT, no. 8, 2014). In this paper we focus on the optimal cyclic codes that arise from the general construction. We give a characterization of these codes in terms of their zeros, and observe that there are many equivalent ways of constructing optimal cyclic LRC codes over a given field. We also study subfield subcodes of cyclic LRC codes (BCH-like LRC codes) and establish several results about their locality and minimum distance.

Codes for storage systems aim to minimize the repair locality, which is the number of disks (or nodes) that participate in the repair of a single failed disk. Simultaneously, the code must sustain a high rate, operate on a small finite field to be practically significant and be tolerant to a large number of erasures. To this end, we construct new families of binary linear codes that have an optimal dimension (rate) for a given minimum distance and locality. Specifically, we construct cyclic codes that are locally repairable for locality 2 and distances 2, 6 and 10. In doing so, we discover new upper bounds on the code dimension, and prove the optimality of enabling local repair by provisioning disjoint groups of disks. Finally, we extend our construction to build codes that have multiple repair sets for each disk.

We study the exact-repair tradeoff between storage and repair bandwidth in distributed storage systems. We give new inner bounds for the tradeoff region and provide code constructions that achieve these bounds.

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 ℓ. 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 ℓ 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 ℓ, which is exponential in k. It has been conjectured that for a fixed number of parity nodes, it is in fact necessary for ℓ to be exponential in k. In this paper, we provide a new log-squared converse bound on k for a given ℓ, and prove that k ≤ 2 log2I(logδℓ + 1), for an arbitrary number of parity nodes r = n - k, where δ = r/(r - 1).

Codes for distributed storage systems are often designed to sustain failure of multiple storage disks. Specifically, an (n, k) 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 ℓ. MDS array codes can potentially repair a single erasure using a fraction l/(n - k) of data stored in the surviving nodes. We ask the following question: for a given (n, k), what is the minimum vector-length or sub-packetization factor ℓ 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 [1] have a sub-packetization factor I which is exponential in k. It has been conjectured [2] that for a fixed number of parity nodes, it is in fact necessary for ℓ to be exponential in k. In this paper, we provide new converse bounds on k for a given ℓ We prove that k ≤ ℓ2for an arbitrary but fixed number of parity nodes r = n ™ k. For the practical case of 2 parity nodes, we prove a stronger result that k ≤ 4ℓ.

This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.

Remote evaluation of the mobility characteristics of a fluid enclosed in the pores of subsurface rocks is an issue of the major interest in the petroleum industry. In a last decade several techniques to perform such an evaluation based on attributes derived from seismic images have been proposed. Although these techniques have foundation in theory and have delivered results of reasonable quality in a number of cases, they are still considered experimental and sparsely used in industry. It is due to the fact that enormous complexity of the recorded seismic data makes any interpretation impossible without a sophisticated procedure of seismic imaging. This procedure in turn introduces significant distortions in the data. The question whether subtle spectral effects characterizing a fluid moving in porous rocks are still observable after seismic imaging remains under debate. It is the question that is investigated in this paper from the signal processing prospective.