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    ์ƒˆ๋กœ์šด ์†Œ์‹ค ์ฑ„๋„์„ ์œ„ํ•œ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ ๋ฐ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ๋ฐ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„ LDPC ๋ถ€ํ˜ธ์˜ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๋…ธ์ข…์„ .In this dissertation, three main contributions are given asi) new two-stage automorphism group decoders (AGD) for cyclic codes in the erasure channel, ii) new constructions of binary and ternary locally repairable codes (LRCs) using cyclic codes and existing LRCs, and iii) new constructions of high-rate generalized root protograph (GRP) low-density parity-check (LDPC) codes for a nonergodic block interference and partially regular (PR) LDPC codes for follower noise jamming (FNJ), are considered. First, I propose a new two-stage AGD (TS-AGD) for cyclic codes in the erasure channel. Recently, error correcting codes in the erasure channel have drawn great attention for various applications such as distributed storage systems and wireless sensor networks, but many of their decoding algorithms are not practical because they have higher decoding complexity and longer delay. Thus, the AGD for cyclic codes in the erasure channel was introduced, which has good erasure decoding performance with low decoding complexity. In this research, I propose new TS-AGDs for cyclic codes in the erasure channel by modifying the parity check matrix and introducing the preprocessing stage to the AGD scheme. The proposed TS-AGD is analyzed for the perfect codes, BCH codes, and maximum distance separable (MDS) codes. Through numerical analysis, it is shown that the proposed decoding algorithm has good erasure decoding performance with lower decoding complexity than the conventional AGD. For some cyclic codes, it is shown that the proposed TS-AGD achieves the perfect decoding in the erasure channel, that is, the same decoding performance as the maximum likelihood (ML) decoder. For MDS codes, TS-AGDs with the expanded parity check matrix and the submatrix inversion are also proposed and analyzed. Second, I propose new constructions of binary and ternary LRCs using cyclic codes and existing two LRCs for distributed storage system. For a primitive work, new constructions of binary and ternary LRCs using cyclic codes and their concatenation are proposed. Some of proposed binary LRCs with Hamming weights 4, 5, and 6 are optimal in terms of the upper bounds. In addition, the similar method of the binary case is applied to construct the ternary LRCs with good parameters. Also, new constructions of binary LRCs with large Hamming distance and disjoint repair groups are proposed. The proposed binary linear LRCs constructed by using existing binary LRCs are optimal or near-optimal in terms of the bound with disjoint repair group. Last, I propose new constructions of high-rate GRP LDPC codes for a nonergodic block interference and anti-jamming PR LDPC codes for follower jamming. The proposed high-rate GRP LDPC codes are based on nonergodic two-state binary symmetric channel with block interference and Nakagami-mm block fading. In these channel environments, GRP LDPC codes have good performance approaching to the theoretical limit in the channel with one block interference, where their performance is shown by the channel threshold or the channel outage probability. In the proposed design, I find base matrices using the protograph extrinsic information transfer (PEXIT) algorithm. Also, the proposed new constructions of anti-jamming partially regular LDPC codes is based on follower jamming on the frequency-hopped spread spectrum (FHSS). For a channel environment, I suppose follower jamming with random dwell time and Rayleigh block fading environment with M-ary frequnecy shift keying (MFSK) modulation. For a coding perspective, an anti-jamming LDPC codes against follower jamming are introduced. In order to optimize the jamming environment, the partially regular structure and corresponding density evolution schemes are used. A series of simulations show that the proposed codes outperforms the 802.16e standard in the presence of follower noise jamming.์ด ๋…ผ๋ฌธ์—์„œ๋Š”, i) ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ , ii) ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ(LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•, ๋ฐ iii) ๋ธ”๋ก ๊ฐ„์„ญ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ๊ณ ๋ถ€ํšจ์œจ์˜ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„(generalized root protograph, GRP) LDPC ๋ถ€ํ˜ธ ๋ฐ ์ถ”์  ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ํ•ญ์žฌ๋ฐ ๋ถ€๋ถ„ ๊ท ์ผ (anti-jamming paritally regular, AJ-PR) LDPC ๋ถ€ํ˜ธ๊ฐ€ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ฒซ๋ฒˆ์งธ๋กœ, ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ตœ๊ทผ ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ ํ˜น์€ ๋ฌด์„  ์„ผ์„œ ๋„คํŠธ์›Œํฌ ๋“ฑ์˜ ์‘์šฉ์œผ๋กœ ์ธํ•ด ์†Œ์‹ค ์ฑ„๋„์—์„œ์˜ ์˜ค๋ฅ˜ ์ •์ • ๋ถ€ํ˜ธ ๊ธฐ๋ฒ•์ด ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ๋ณตํ˜ธ๊ธฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋†’์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๊ธด ์ง€์—ฐ์œผ๋กœ ์ธํ•ด ์‹ค์šฉ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋‚ฎ์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ผ ์ˆ˜ ์žˆ๋Š” ์ˆœํ™˜ ๋ถ€ํ˜ธ์—์„œ ์ด๋‹จ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŒจ๋ฆฌํ‹ฐ ๊ฒ€์‚ฌ ํ–‰๋ ฌ์„ ๋ณ€ํ˜•ํ•˜๊ณ , ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๋„์ž…ํ•œ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ณตํ˜ธ๊ธฐ๋Š” perfect ๋ถ€ํ˜ธ, BCH ๋ถ€ํ˜ธ ๋ฐ ์ตœ๋Œ€ ๊ฑฐ๋ฆฌ ๋ถ„๋ฆฌ (maximum distance separable, MDS) ๋ถ€ํ˜ธ์— ๋Œ€ํ•ด์„œ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ˆ˜์น˜ ๋ถ„์„์„ ํ†ตํ•ด, ์ œ์•ˆ๋œ ๋ณตํ˜ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด์˜ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ณด๋‹ค ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๋ณด์ด๋ฉฐ, ๋ช‡๋ช‡์˜ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ตœ๋Œ€ ์šฐ๋„ (maximal likelihood, ML)๊ณผ ๊ฐ™์€ ์ˆ˜์ค€์˜ ์„ฑ๋Šฅ์ž„์„ ๋ณด์ธ๋‹ค. MDS ๋ถ€ํ˜ธ์˜ ๊ฒฝ์šฐ, ํ™•์žฅ๋œ ํŒจ๋ฆฌํ‹ฐ๊ฒ€์‚ฌ ํ–‰๋ ฌ ๋ฐ ์ž‘์€ ํฌ๊ธฐ์˜ ํ–‰๋ ฌ์˜ ์—ญ์—ฐ์‚ฐ์„ ํ™œ์šฉํ•˜์˜€์„ ๊ฒฝ์šฐ์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ (LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋กœ์„œ, ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์—ฐ์ ‘์„ ํ™œ์šฉํ•œ ์ด์ง„ ๋ฐ ์‚ผ์ง„ LRC ์„ค๊ณ„ ๊ธฐ๋ฒ•์ด ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ตœ์†Œ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ๊ฐ€ 4,5, ํ˜น์€ 6์ธ ์ œ์•ˆ๋œ ์ด์ง„ LRC ์ค‘ ์ผ๋ถ€๋Š” ์ƒํ•œ๊ณผ ๋น„๊ตํ•ด ๋ณด์•˜์„ ๋•Œ ์ตœ์  ์„ค๊ณ„์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋น„์Šทํ•œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ข‹์€ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์‚ผ์ง„ LRC๋ฅผ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์™ธ์— ๊ธฐ์กด์˜ LRC๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํฐ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ์˜ ์ƒˆ๋กœ์šด LRC๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ LRC๋Š” ๋ถ„๋ฆฌ๋œ ๋ณต๊ตฌ ๊ตฐ ์กฐ๊ฑด์—์„œ ์ตœ์ ์ด๊ฑฐ๋‚˜ ์ตœ์ ์— ๊ฐ€๊นŒ์šด ๊ฐ’์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, GRP LDPC ๋ถ€ํ˜ธ๋Š” Nakagami-mm ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ธ”๋ก ๊ฐ„์„ญ์ด ์žˆ๋Š” ๋‘ ์ƒํƒœ์˜ ์ด์ง„ ๋Œ€์นญ ์ฑ„๋„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฑ„๋„ ํ™˜๊ฒฝ์—์„œ GRP LDPC ๋ถ€ํ˜ธ๋Š” ํ•˜๋‚˜์˜ ๋ธ”๋ก ๊ฐ„์„ญ์ด ๋ฐœ์ƒํ–ˆ์„ ๊ฒฝ์šฐ, ์ด๋ก ์  ์„ฑ๋Šฅ์— ๊ฐ€๊นŒ์šด ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋Ÿฌํ•œ ์ด๋ก  ๊ฐ’์€ ์ฑ„๋„ ๋ฌธํ„ฑ๊ฐ’์ด๋‚˜ ์ฑ„๋„ outage ํ™•๋ฅ ์„ ํ†ตํ•ด ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ์„ค๊ณ„์—์„œ๋Š”, ๋ณ€ํ˜•๋œ PEXIT ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์ดˆ ํ–‰๋ ฌ์„ ์„ค๊ณ„ํ•œ๋‹ค. ๋˜ํ•œ AJ-PR LDPC ๋ถ€ํ˜ธ๋Š” ์ฃผํŒŒ์ˆ˜ ๋„์•ฝ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์žˆ๋Š” ํ™˜๊ฒฝ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ฑ„๋„ ํ™˜๊ฒฝ์œผ๋กœ MFSK ๋ณ€๋ณต์กฐ ๋ฐฉ์‹์˜ ๋ ˆ์ผ๋ฆฌ ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ฌด์ž‘์œ„ํ•œ ์ง€์† ์‹œ๊ฐ„์ด ์žˆ๋Š” ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์žฌ๋ฐ ํ™˜๊ฒฝ์œผ๋กœ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๋ถ€๋ถ„ ๊ท ์ผ ๊ตฌ์กฐ ๋ฐ ํ•ด๋‹น๋˜๋Š” ๋ฐ€๋„ ์ง„ํ™” (density evolution, DE) ๊ธฐ๋ฒ•์ด ํ™œ์šฉ๋œ๋‹ค. ์—ฌ๋Ÿฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์กด์žฌํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆ๋œ ๋ถ€ํ˜ธ๊ฐ€ 802.16e์— ์‚ฌ์šฉ๋˜์—ˆ๋˜ LDPC ๋ถ€ํ˜ธ๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1.1 Background 1.2 Overview of Dissertation 1.3 Notations 2 Preliminaries 2.1 IED and AGD for Erasure Channel 2.1.1 Iterative Erasure Decoder 2.1.1 Automorphism Group Decoder 2.2. Binary Locally Repairable Codes for Distributed Storage System 2.2.1 Bounds and Optimalities of Binary LRCs 2.2.2 Existing Optimal Constructions of Binary LRCs 2.3 Channels with Block Interference and Jamming 2.3.1 Channels with Block Interference 2.3.2 Channels with Jamming with MFSK and FHSS Environment. 3 New Two-Stage Automorphism Group Decoders for Cyclic Codes in the Erasure Channel 3.1 Some Definitions 3.2 Modification of Parity Check Matrix and Two-Stage AGD 3.2.1 Modification of the Parity Check Matrix 3.2.2 A New Two-Stage AGD 3.2.3 Analysis of Modification Criteria for the Parity Check Matrix 3.2.4 Analysis of Decoding Complexity of TS-AGD 3.2.5 Numerical Analysis for Some Cyclic Codes 3.3 Construction of Parity Check Matrix and TS-AGD for Cyclic MDS Codes 3.3.1 Modification of Parity Check Matrix for Cyclic MDS Codes . 3.3.2 Proposed TS-AGD for Cyclic MDS Codes 3.3.3 Perfect Decoding by TS-AGD with Expanded Parity Check Matrix for Cyclic MDS Codes 3.3.4 TS-AGD with Submatrix Inversion for Cyclic MDS Codes . . 4 New Constructions of Binary and Ternary LRCs Using Cyclic Codes and Existing LRCs 4.1 Constructions of Binary LRCs Using Cyclic Codes 4.2 Constructions of Linear Ternary LRCs Using Cyclic Codes 4.3 Constructions of Binary LRCs with Disjoint Repair Groups Using Existing LRCs 4.4 New Constructions of Binary Linear LRCs with d โ‰ฅ 8 Using Existing LRCs 5 New Constructions of Generalized RP LDPC Codes for Block Interference and Partially Regular LDPC Codes for Follower Jamming 5.1 Generalized RP LDPC Codes for a Nonergodic BI 5.1.1 Minimum Blockwise Hamming Weight 5.1.2 Construction of GRP LDPC Codes 5.2 Asymptotic and Numerical Analyses of GRP LDPC Codes 5.2.1 Asymptotic Analysis of LDPC Codes 5.2.2 Numerical Analysis of Finite-Length LDPC Codes 5.3 Follower Noise Jamming with Fixed Scan Speed 5.4 Anti-Jamming Partially Regular LDPC Codes for Follower Noise Jamming 5.4.1 Simplified Channel Model and Corresponding Density Evolution 5.4.2 Construction of AJ-PR-LDPC Codes Based on DE 5.5 Numerical Analysis of AJ-PR LDPC Codes 6 Conclusion Abstract (In Korean)Docto

    Cooperative Local Repair in Distributed Storage

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    Erasure-correcting codes, that support local repair of codeword symbols, have attracted substantial attention recently for their application in distributed storage systems. This paper investigates a generalization of the usual locally repairable codes. In particular, this paper studies a class of codes with the following property: any small set of codeword symbols can be reconstructed (repaired) from a small number of other symbols. This is referred to as cooperative local repair. The main contribution of this paper is bounds on the trade-off of the minimum distance and the dimension of such codes, as well as explicit constructions of families of codes that enable cooperative local repair. Some other results regarding cooperative local repair are also presented, including an analysis for the well-known Hadamard/Simplex codes.Comment: Fixed some minor issues in Theorem 1, EURASIP Journal on Advances in Signal Processing, December 201

    Design and Analysis of Graph-based Codes Using Algebraic Lifts and Decoding Networks

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    Error-correcting codes seek to address the problem of transmitting information efficiently and reliably across noisy channels. Among the most competitive codes developed in the last 70 years are low-density parity-check (LDPC) codes, a class of codes whose structure may be represented by sparse bipartite graphs. In addition to having the potential to be capacity-approaching, LDPC codes offer the significant practical advantage of low-complexity graph-based decoding algorithms. Graphical substructures called trapping sets, absorbing sets, and stopping sets characterize failure of these algorithms at high signal-to-noise ratios. This dissertation focuses on code design for and analysis of iterative graph-based message-passing decoders. The main contributions of this work include the following: the unification of spatially-coupled LDPC (SC-LDPC) code constructions under a single algebraic graph lift framework and the analysis of SC-LDPC code construction techniques from the perspective of removing harmful trapping and absorbing sets; analysis of the stopping and absorbing set parameters of hypergraph codes and finite geometry LDPC (FG-LDPC) codes; the introduction of multidimensional decoding networks that encode the behavior of hard-decision message-passing decoders; and the presentation of a novel Iteration Search Algorithm, a list decoder designed to improve the performance of hard-decision decoders. Adviser: Christine A. Kelle

    Coding for the Clouds: Coding Techniques for Enabling Security, Locality, and Availability in Distributed Storage Systems

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    Cloud systems have become the backbone of many applications such as multimedia streaming, e-commerce, and cluster computing. At the foundation of any cloud architecture lies a large-scale, distributed, data storage system. To accommodate the massive amount of data being stored on the cloud, these distributed storage systems (DSS) have been scaled to contain hundreds to thousands of nodes that are connected through a networking infrastructure. Such data-centers are usually built out of commodity components, which make failures the norm rather than the exception. In order to combat node failures, data is typically stored in a redundant fashion. Due to the exponential data growth rate, many DSS are beginning to resort to error control coding over conventional replication methods, as coding offers high storage space efficiency. This paradigm shift from replication to coding, along with the need to guarantee reliability, efficiency, and security in DSS, has created a new set of challenges and opportunities, opening up a new area of research. This thesis addresses several of these challenges and opportunities by broadly making the following contributions. (i) We design practically amenable, low-complexity coding schemes that guarantee security of cloud systems, ensure quick recovery from failures, and provide high availability for retrieving partial information; and (ii) We analyze fundamental performance limits and optimal trade-offs between the key performance metrics of these coding schemes. More specifically, we first consider the problem of achieving information-theoretic security in DSS against an eavesdropper that can observe a limited number of nodes. We present a framework that enables design of secure repair-efficient codes through a joint construction of inner and outer codes. Then, we consider a practically appealing notion of weakly secure coding, and construct coset codes that can weakly secure a wide class of regenerating codes that reduce the amount of data downloaded during node repair. Second, we consider the problem of meeting repair locality constraints, which specify the number of nodes participating in the repair process. We propose a notion of unequal locality, which enables different locality values for different nodes, ensuring quick recovery for nodes storing important data. We establish tight upper bounds on the minimum distance of linear codes with unequal locality, and present optimal code constructions. Next, we extend the notion of locality from the Hamming metric to the rank and subspace metrics, with the goal of designing codes for efficient data recovery from special types of correlated failures in DSS.We construct a family of locally recoverable rank-metric codes with optimal data recovery properties. Finally, we consider the problem of providing high availability, which is ensured by enabling node repair from multiple disjoint subsets of nodes of small size. We study codes with availability from a queuing-theoretical perspective by analyzing the average time necessary to download a block of data under the Poisson request arrival model when each node takes a random amount of time to fetch its contents. We compare the delay performance of the availability codes with several alternatives such as conventional erasure codes and replication schemes

    Some new constructions of optimal linear codes and alphabet-optimal (r,ฮด)(r,\delta)-locally repairable codes

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    In distributed storage systems, locally repairable codes (LRCs) are designed to reduce disk I/O and repair costs by enabling recovery of each code symbol from a small number of other symbols. To handle multiple node failures, (r,ฮด)(r,\delta)-LRCs are introduced to enable local recovery in the event of up to ฮดโˆ’1\delta-1 failed nodes. Constructing optimal (r,ฮด)(r,\delta)-LRCs has been a significant research topic over the past decade. In \cite{Luo2022}, Luo \emph{et al.} proposed a construction of linear codes by using unions of some projective subspaces within a projective space. Several new classes of Griesmer codes and distance-optimal codes were constructed, and some of them were proved to be alphabet-optimal 22-LRCs. In this paper, we first modify the method of constructing linear codes in \cite{Luo2022} by considering a more general situation of intersecting projective subspaces. This modification enables us to construct good codes with more flexible parameters. Additionally, we present the conditions for the constructed linear codes to qualify as Griesmer codes or achieve distance optimality. Next, we explore the locality of linear codes constructed by eliminating elements from a complete projective space. The novelty of our work lies in establishing the locality as (2,pโˆ’2)(2,p-2), (2,pโˆ’1)(2,p-1), or (2,p)(2,p)-locality, in contrast to the previous literature that only considered 22-locality. Moreover, by combining analysis of code parameters and the C-M like bound for (r,ฮด)(r,\delta)-LRCs, we construct some alphabet-optimal (2,ฮด)(2,\delta)-LRCs which may be either Griesmer codes or not Griesmer codes. Finally, we investigate the availability and alphabet-optimality of (r,ฮด)(r,\delta)-LRCs constructed from our modified framework.Comment: 25 page
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