Localized Dimension Growth in Random Network Coding: A Convolutional Approach

We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC) algorithm to address the issue of field size in random network coding. ARCNC operates as a convolutional code, with the coefficients of local encoding kernels chosen randomly over a small finite field. The lengths of local encoding kernels increase with time until the global encoding kernel matrices at related sink nodes all have full rank. Instead of estimating the necessary field size a priori, ARCNC operates in a small finite field. It adapts to unknown network topologies without prior knowledge, by locally incrementing the dimensionality of the convolutional code. Because convolutional codes of different constraint lengths can coexist in different portions of the network, reductions in decoding delay and memory overheads can be achieved with ARCNC. We show through analysis that this method performs no worse than random linear network codes in general networks, and can provide significant gains in terms of average decoding delay in combination networks.Comment: 7 pages, 1 figure, submitted to IEEE ISIT 201

Distributed decoding of convolutional network error correction codes

A Viterbi-like decoding algorithm is proposed in this paper for generalized convolutional network error correction coding. Different from classical Viterbi algorithm, our decoding algorithm is based on minimum error weight rather than the shortest Hamming distance between received and sent sequences. Network errors may disperse or neutralize due to network transmission and convolutional network coding. Therefore, classical decoding algorithm cannot be employed any more. Source decoding was proposed by multiplying the inverse of network transmission matrix, where the inverse is hard to compute. Starting from the Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to the minimum error weight under our model. Inspired by Viterbi algorithm, we propose a Viterbi-like decoding algorithm based on minimum error weight of combined error vectors, which can be carried out directly at sink nodes and can correct any network errors within the capability of convolutional network error correction codes (CNECC). Under certain situations, the proposed algorithm can realize the distributed decoding of CNECC.Comment: the full version of manuscript for ISIT 201

Strong Secrecy Capacity of a Class of Wiretap Networks

This paper considers a special class of wiretap networks with a single source node and K sink nodes. The source message is encoded into a binary digital sequence of length N, divided into K subsequences, and sent to the K sink nodes respectively through noiseless channels. The legitimate receivers are able to obtain subsequences from arbitrary μ 1 = K α 1 sink nodes. Meanwhile, there exist eavesdroppers who are able to observe subsequences from arbitrary μ 2 = K α 2 sink nodes, where 0 ≤ α 2 < α 1 ≤ 1 . The goal is to let the receivers be able to recover the source message with a vanishing decoding error probability, and keep the eavesdroppers ignorant about the source message. It is clear that the communication model is an extension of wiretap channel II. Secrecy capacity with respect to the strong secrecy criterion is established. In the proof of the direct part, a codebook is generated by a randomized scheme and partitioned by Csiszár’s almost independent coloring scheme. Unlike the linear network coding schemes, our coding scheme is working on the binary field and hence independent of the scale of the network

Subspace Coding for Networks with Different Level Messages

We study the asymptotically-achievable rate region of subspace codes for wireless network coding, where receivers have different link capacities due to the access ways or the faults of the intermediate links in the network. Firstly, an outer bound of the achievable rate region in a two-receiver network is derived from a combinatorial method. Subsequently, the achievability of the outer bound is proven by code construction, which is based on superposition coding. We show that the outer bound can be achieved asymptotically by using the code presented by Koetter and Kschischang, and the outer bound can be exactly attained in some points by using a q-analog Steiner structure. Finally, the asymptotically-achievable rate region is extended to the general case when the network has m receivers with different levels

Achievable Rate Region under Linear Beamforming for Dual-Hop Multiple-Access Relay Network

Consider a network consisting of two independent single-antenna sources, a single-antenna destination and a helping multiple-antenna relay. This network is called a dual-hop multiple access relay network (MARN). In this network, sources transmit to the relay simultaneously in the first time slot. The relay retransmits the received sum-signal to the destination using a linear beamforming scheme in the second time slot. In this paper, we characterize the achievable rate region of MARN under linear beamforming. The achievable rate region characterization problem is first transformed to an equivalent “corner point” optimization problem with respect to linear beamforming matrix at the relay. Then, we present an efficient algorithm to solve it via only semi-definite programming (SDP). We further derive the mathematical close-forms of the maximum individual rates and the sum-rate. Finally, numerical results demonstrate the performance of the proposed schemes

Security Analysis and Improvements on a Remote Integrity Checking Scheme for Regenerating-Coding-Based Distributed Storage

Enabling remote data integrity checking with failure recovery becomes exceedingly critical in distributed cloud systems. With the properties of a lower repair bandwidth while preserving fault tolerance, regenerating coding and network coding (NC) have received much attention in the coding-based storage field. Recently, an outstanding outsourced auditing scheme named NC-Audit was proposed for regenerating-coding-based distributed storage. The scheme claimed that it can effectively achieve lightweight privacy-preserving data verification remotely for these networked distributed systems. However, our algebraic analysis shows that NC-Audit can be easily broken due to a potential defect existing in its schematic design. That is, an adversarial cloud server can forge some illegal blocks to cheat the auditor with a high probability when the coding field is large. From the perspective of algebraic security, we propose a remote data integrity checking scheme RNC-Audit by resorting to hiding partial critical information to the server without compromising system performance. Our evaluation shows that the proposed scheme has significantly lower overhead compared to the state-of-the-art schemes for distributed remote data auditing