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Secret Data Transmission in Wireless Sensor Network with Physical Layer Network Coding

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2017年第4期33卷 1055-1067页

作者： Liu, Qiao Wang, Yong Zhang, Wenjing Li, Hui Xidian Univ ISN Sch Cyber Engn Xian 710071 Peoples R China

In this paper, the physical layer network coding is applied into wireless sensor network for secret data transmission. The source node will send the gathered data to the sink node with help of multiple relay nodes. By applying the physical layer network coding, the received signal at the relay nodes is guaranteed to be a summed signal which cannot be separated. Thus, the attack from untrusted relays as well as external eavesdroppers can be prevented. Two types of coding algorithm has been discussed, one for BPSK coding and another for nested lattice coding. Analysis is also conduct for the proposed protocol. In the view of transmission, the proposed protocol can achieve the same time slots cost as the non -security protocol. In the view of security, the proposed protocol can successfully against the attacks identical with cryptography approach, however less hardware and computational complexity will be consumed for the proposed protocol.

关键词： WSN multiple hops transmission physical layer network coding nested lattice code physical layer security

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End-to-End Secure Multi-Hop Communication with Untrusted Relays

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2013年第1期12卷 1-11页

作者： He, Xiang Yener, Aylin Penn State Univ Dept Elect Engn University Pk PA 16802 USA

A multi-hop line network is considered, where each node can receive signals transmitted by its two neighbors. As such, the model embodies both the interference and broadcast aspects of wireless networks. The leftmost node wishes to send messages to the rightmost node, while keeping these messages confidential from all the intermediate relay nodes. In this setting where any or all of the relay nodes can be eavesdroppers, it is shown that end-to-end secure and reliable communication is possible. Notably, it is shown that an end-to-end secrecy rate that is independent of the number of hops, i.e., intermediate eavesdroppers, is achievable by means of a carefully designed transmission schedule, compute-and-forward relaying and coding strategy utilizing nested lattice codes. The achievable rate obtained indicates that imposing secrecy constraints penalizes the capacity by at most 1 bit per channel use. Therefore, it is concluded that information theoretic secrecy can be guaranteed for this model irrespective of eavesdropping relays and a fixed modest cost for the end-to-end rate.

关键词： Information theoretic secrecy nested lattice code line network untrusted relays

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An Algebraic Approach to Physical-Layer Network Coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2013年第11期59卷 7576-7596页

作者： Feng, Chen Silva, Danilo Kschischang, Frank R. Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada Univ Fed Santa Catarina Dept Elect Engn BR-88040900 Florianopolis SC Brazil

The problem of designing physical-layer network coding (PNC) schemes via nested lattices is considered. Building on the compute-and-forward (C&F) relaying strategy of Nazer and Gastpar, who demonstrated its asymptotic gain using information-theoretic tools, an algebraic approach is taken to show its potential in practical, nonasymptotic, settings. A general framework is developed for studying nested-lattice-based PNC schemes-called lattice network coding (LNC) schemes for short-by making a direct connection between C&F and module theory. In particular, a generic LNC scheme is presented that makes no assumptions on the underlying nested lattice code. C&F is reinterpreted in this framework, and several generalized constructions of LNC schemes are given. The generic LNC scheme naturally leads to a linear network coding channel over modules, based on which noncoherent network coding can be achieved. Next, performance/complexity tradeoffs of LNC schemes are studied, with a particular focus on hypercube-shaped LNC schemes. The error probability of this class of LNC schemes is largely determined by the minimum intercoset distances of the underlying nested lattice code. Several illustrative hypercube-shaped LNC schemes are designed based on Constructions A and D, showing that nominal coding gains of 3 to 7.5 dB can be obtained with reasonable decoding complexity. Finally, the possibility of decoding multiple linear combinations is considered and related to the shortest independent vectors problem. A notion of dominant solutions is developed together with a suitable lattice-reduction-based algorithm.

关键词： Finite generated modules over principal ideal domains lattice network coding nested lattice code Smith normal form

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Macro-pico amplitude-space sharing with layered interference alignment

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2015年第1期2015卷 1页

作者： Tian, Yafei Huang, Lifeng Yang, Chenyang Beihang Univ Sch Elect & Informat Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

Inter-cell interference leads to severe performance degradation in cellular networks, and the study of multi-user interference channel is the corner stone for solving this problem. Amplitude-space layered interference alignment (IA), as an effective complementation to the vector-space IA, is a promising method to increase the data rate in static interference channels. However, recent studies of layered IA has been focused on analyzing the degrees of freedom (DoF) or the achievable rate under specific channel constraints. In this paper, we propose a layered IA scheme that can work with arbitrary channel coefficients. We develop a layer partitioning method and optimize the active layer assignment through linear programming. An implementation scheme is then introduced with multi-level nested lattice codes where the signal and interference are nested in amplitude space, and the interference from different users is nestedly aligned. The performance of the proposed scheme is finally evaluated in homogeneous and heterogeneous cellular networks with practical settings.

关键词： Amplitude-space sharing Heterogeneous network Interference alignment nested lattice code

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Secret Key Generation for Correlated Gaussian Sources

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IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第6期58卷 3373-3391页

作者： Nitinawarat, Sirin Narayan, Prakash Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Syst Res Inst College Pk MD 20742 USA

Secret key generation by multiple terminals is considered based on their observations of jointly distributed Gaussian signals, followed by public communication among themselves. Exploiting an inherent connection between secrecy generation and lossy data compression, two main contributions are made. The first is a characterization of strong secret key capacity, and entails a converse proof technique that is valid for real-valued (and not necessarily Gaussian) as well as finite-valued signals. The capacity formula acquires a simple form when the terminals observe "symmetrically correlated" jointly Gaussian signals. For the latter setup with two terminals, considering schemes that involve quantization at one terminal, the best rate of an achievable secret key is characterized as a function of quantization rate;secret key capacity is attained as the quantization rate tends to infinity. Structured codes are shown to attain the optimum tradeoff between secret key rate and quantization rate, constituting our second main contribution.

关键词： Linear code multiterminal Gaussian source model nested lattice code public communication quantization secret key capacity strong secrecy

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Random Linear Network Coding over Compute-and-Forward in Multi-Source Multi-Relay Networks 13

Random Linear Network Coding over Compute-and-Forward in Mul...

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13th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Ngeth, Rithea Kurkoski, Brian M. Lim, Yuto Tan, Yasuo Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa Japan

ISBN: (纸本)9781509043729

This paper proposes a transmission scheme which applies random linear network coding (RLNC) over compute-and-forward (CF), called RLNC/CF, in multi-source multi-relay networks. Instead of solving the full rank failure at relays, this paper compensates for this overhead to increase the possibility of successfully decoding computed messages at the destination. The concept of the overlapped generations is applied with a proposed computing and storing strategy. This paper provides a compensation based on the estimation of the channel state information (CSI) of the previous generation and a compensation based on the learning data of CSI. By comparing to an orthogonal channel transmission scheme, a performance trade-off is considered. An expression for estimated performances of RLNC/CF in function of the probabilities of the parameters related to CSI is provided to help for the decision of selecting transmission scheme. From the numerical result, RLNC/CF scheme works better than a conventional CF transmission scheme in reducing the transmission latency.

关键词： random linear network coding compute-and-forward nested lattice code multi-source multi-relay rank deficiency

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Factor Graph-based Deep Reinforcement Learning for Path Selection Scheme in Full-duplex Wireless Multihop Networks 19

Factor Graph-based Deep Reinforcement Learning for Path Sele...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Cui, Zhihan Khun, Aung Thura Phyo Lim, Yuto Tan, Yasuo Japan Adv Inst Sci & Technol JAIST Sch Informat Sci 1-1 Asahidai Nomi Ishikawa 9231292 Japan

ISBN: (纸本)9798350333398

Wireless Multihop Network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any message via relaying nodes by multihop fashion. In WMN, there are still some issues that need to be resolved, like due to the uncertainty of source node choosing a path to send the message and the nature of multihop fashion, the performance of network capacity can degrade drastically. To solve these problems, in this research we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from source node to destination node with highest end-to-end throughput. Factor graph (FG) representation is used to reduce the computation time. Our simulation results reveal that both NLPS and INLPS can achieve high network capacity and moderate computation time. Meanwhile, nested lattice code (NLC) is used in compute-and-forward strategy to reduce the time slots. As a result, the network capacity can increase more.

关键词： Deep Reinforcement Learning Factor Graph nested lattice code Learning Path Selection Algorithm Wireless Multihop Networks

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Interference Truncation for Compute-and-forward Relaying in Multiple Access Channels 14

Interference Truncation for Compute-and-forward Relaying in ...

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IEEE International Conference on Computer and Information Technology (CIT 2014)

作者： Zhao, Zhongyuan Peng, Mugen Zhou, Zheng Wang, Zhao Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Wireless Signal Proc & Network Lab Beijing 100876 Peoples R China

ISBN: (纸本)9781479962396

nested lattice code is a practical technique to approach the Shannon capacity upper bound. However, the performance of compute-and-forward (CF) relaying is blocked by the channel output residuals that the decoding process of nested lattice codeword cannot remove completely. In this paper, an interference truncation scheme is designed for CF relaying in multiple access channels to minimize the channel residual caused by lattice decoding, where the scalars and coefficient vectors are configured to optimize the computation rate. The key idea is to improve the performance by introducing a recovered linear combination of codewords into the decoding process at the destination with proper scaling. The proposal harnesses the received message to reduce the impact of effective noise, which in traditional CF is determined by that equations are computed out directly from received messages. The analysis and numerical results demonstrate that the achievable computation rate of the proposed scheme outperforms the traditional scheme, and the impacts of asymmetric transmit power settings are studied as well.

关键词： Terms-Interference truncation nested lattice code network coding computation rate

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Factor graph-based deep reinforcement learning for path selection scheme in full-duplex wireless multihop networks

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AD HOC NETWORKS 2024年 161卷

作者： Cui, Zhihan Lim, Yuto Tan, Yasuo Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol 1-1 Asahidai Nomi Ishikawa 9231292 Japan

A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.

关键词： Deep reinforcement learning Factor graph nested lattice code Learning path selection algorithm Wireless multihop networks

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A Design of Overlapped Chunked code over Compute-and-Forward for Multi-Source Multi-Relay Networks

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SENSORS 2018年第10期18卷 3225-3225页

作者： Ngeth, Rithea Kurkoski, Brian M. Lim, Yuto Tan, Yasuo Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa 9231292 Japan

This paper investigates the design of overlapped chunked codes (OCC) for multi-source multi-relay networks where a physical-layer network coding approach, compute-and-forward (CF) based on nested lattice codes (NLC), is applied for the simultaneous transmissions from the sources to the relays. This code is called OCC/CF. In this paper, OCC is applied before NLC before transmitting for each source. Random linear network coding is applied within each chunk. A decodability condition to design OCC/CF is provided. In addition, an OCC with a contiguously overlapping, but non-rounded-end fashion is employed for the design, which is done by using the probability distributions of the number of innovative codeword combinations and the probability distribution of the participation factor of each source to the codeword combinations received for a chunk transmission. An estimation is done to select an allocation, i.e., the number of innovative blocks per chunk and the number of blocks taken from the previous chunk for all sources, that is expected to provide the desired performance. From the numerical results, the design overhead of OCC/CF is low when the probability distribution of the participation factor of each source is dense at the chunk size for each source.

关键词： overlapped chunked code compute-and-forward nested lattice code multi-source multi-relay empirical rank distribution decodability

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