Zero-Error Coding via Classical and Quantum Channels in Sensor Networks

Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward error correction (FEC) between two communication parties. However, by applying zero-error coding one can assure information fidelity while signals are transmitted in sensor networks. In this study, we investigate zero-error coding via both classical and quantum channels, which consist of n obfuscated symbols such as Shannon’s zero-error communication. As a contrast to the standard classical zero-error coding, which has a computational complexity of O(2n), a general approach is proposed herein to find zero-error codewords in the case of quantum channel. This method is based on a n-symbol obfuscation model and the matrix’s linear transformation, whose complexity dramatically decreases to O(n2). According to a comparison with classical zero-error coding, the quantum zero-error capacity of the proposed method has obvious advantages over its classical counterpart, as the zero-error capacity equals the rank of the quantum coefficient matrix. In particular, the channel capacity can reach n when the rank of coefficient matrix is full in the n-symbol multilateral obfuscation quantum channel, which cannot be reached in the classical case. Considering previous methods such as low density parity check code (LDPC), our work can provide a means of error-free communication through some typical channels. Especially in the quantum case, zero-error coding can reach both a high coding efficiency and large channel capacity, which can improve the robustness of communication in sensor networks.

[1]  Xiaojun Wang,et al.  Matrix Coding-Based Quantum Image Steganography Algorithm , 2019, IEEE Access.

[2]  Ching-Nung Yang,et al.  An Efficient and Secure Arbitrary N-Party Quantum Key Agreement Protocol Using Bell States , 2018 .

[3]  David P. DiVincenzo,et al.  Quantum information and computation , 2000, Nature.

[4]  Guoan Bi,et al.  A Survey on Protograph LDPC Codes and Their Applications , 2015, IEEE Communications Surveys & Tutorials.

[5]  Simone Severini,et al.  Zero-Error Communication via Quantum Channels, Noncommutative Graphs, and a Quantum Lovász Number , 2010, IEEE Transactions on Information Theory.

[6]  W. Wootters,et al.  A single quantum cannot be cloned , 1982, Nature.

[7]  Jiangtao Xi,et al.  Robust Entangled-Photon Ghost Imaging with Compressive Sensing , 2019, Sensors.

[8]  Tadashi Wadayama,et al.  On zero error capacity of Nearest Neighbor Error channels with multilevel alphabet , 2016, 2016 International Symposium on Information Theory and Its Applications (ISITA).

[9]  Wenjie Liu,et al.  Full-Blind Delegating Private Quantum Computation , 2020, ArXiv.

[10]  Jaein Jeong,et al.  Forward Error Correction in Sensor Networks , 2003 .

[11]  Ka-Di Zhu,et al.  Fano Effect and Quantum Entanglement in Hybrid Semiconductor Quantum Dot-Metal Nanoparticle System , 2017, Sensors.

[12]  Tie Qiu,et al.  Robustness Optimization Scheme With Multi-Population Co-Evolution for Scale-Free Wireless Sensor Networks , 2019, IEEE/ACM Transactions on Networking.

[13]  Dan Stahlke,et al.  Quantum Zero-Error Source-Channel Coding and Non-Commutative Graph Theory , 2016, IEEE Transactions on Information Theory.

[14]  Simone Severini,et al.  On Zero-Error Communication via Quantum Channels in the Presence of Noiseless Feedback , 2015, IEEE Transactions on Information Theory.

[15]  Gianluigi Liva,et al.  Improving the Decoding Threshold of Tailbiting Spatially Coupled LDPC Codes by Energy Shaping , 2018, IEEE Communications Letters.

[16]  Salman Beigi,et al.  On the Complexity of Computing Zero-Error and Holevo Capacity of Quantum Channels , 2007, 0709.2090.

[17]  Zhiguo Qu,et al.  A Novel Quantum Stegonagraphy Based on Brown States , 2018 .

[18]  Keqiu Li,et al.  SIGMM: A Novel Machine Learning Algorithm for Spammer Identification in Industrial Mobile Cloud Computing , 2019, IEEE Transactions on Industrial Informatics.

[19]  Claude E. Shannon,et al.  The zero error capacity of a noisy channel , 1956, IRE Trans. Inf. Theory.

[20]  Runyao Duan,et al.  Super-Activation of Zero-Error Capacity of Noisy Quantum Channels , 2009, 0906.2527.

[21]  Yong Xu,et al.  Multiparty quantum sealed-bid auction using single photons as message carrier , 2016, Quantum Inf. Process..

[22]  Gang Xu,et al.  Quantum Image Steganography Protocol Based on Quantum Image Expansion and Grover Search Algorithm , 2019, IEEE Access.

[23]  Le Sun,et al.  Effect of quantum noise on deterministic remote state preparation of an arbitrary two-particle state via various quantum entangled channels , 2017, Quantum Information Processing.

[24]  R. Mousoli,et al.  Controlled cyclic remote state preparation of arbitrary qubit states , 2018 .

[25]  Peter Lancaster,et al.  The theory of matrices , 1969 .

[26]  László Lovász,et al.  On the Shannon capacity of a graph , 1979, IEEE Trans. Inf. Theory.

[27]  Jianxin Chen,et al.  Superactivation of the Asymptotic Zero-Error Classical Capacity of a Quantum Channel , 2009, IEEE Transactions on Information Theory.

[28]  Lei Liu,et al.  TMED: A Spider-Web-Like Transmission Mechanism for Emergency Data in Vehicular Ad Hoc Networks , 2018, IEEE Transactions on Vehicular Technology.

[29]  Ching-Nung Yang,et al.  Quantum Relief algorithm , 2018, Quantum Information Processing.