network coding has been known as a spectrally efficient technique in wireless networks. However, when it is applied to a two-way relay channel (TWRC), it suffers from performance degradation caused by the asymmetric p...
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network coding has been known as a spectrally efficient technique in wireless networks. However, when it is applied to a two-way relay channel (TWRC), it suffers from performance degradation caused by the asymmetric position of the relay. In this paper, we suggest remedying this problem by using hierarchical modulation at the source node. We investigate how hierarchical modulation can be incorporated and optimized with network coding. Our results are encouraging in that hierarchically modulated network coding (HMNC) significantly improves end-to-end bit-error probability and spectral efficiency in asymmetric relay channels, as compared with direct transmission (DT), bidirectional network coding (BNC), and coded bidirectional relay (CBR).
For a wireless sensor network (WSN), we proposed a network coding scheme that adopts a neural network (NN) instead of methods based on Shannon theory, and this scheme can transmit specific data such as images of speci...
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For a wireless sensor network (WSN), we proposed a network coding scheme that adopts a neural network (NN) instead of methods based on Shannon theory, and this scheme can transmit specific data such as images of specific data sets. In further, it also can transmit universal data with the channel code. In the source, the specific data or the code word is sent out to a network performing network coding, where the universal data are encoded into the code word with Reed Solomon code and Polar code. In the intermediate nodes, the network coding based on Shannon theory is replaced with a multilayer perceptron, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). In the sink, we trained a NN with the received massage as the input and the original data as the label. The trained NN includes the sublayers, which are the subneural networks in every node. We test the new transmission scheme for some datasets and channel code such as Reed Solomon code in a WSN environment, and it is proven to be effective.
network coding is a promising technique to improve the throughput and robustness of wireless ad hoc networks. However, the packet-mixing nature of network coding also renders it more prone to pollution attacks. Most e...
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network coding is a promising technique to improve the throughput and robustness of wireless ad hoc networks. However, the packet-mixing nature of network coding also renders it more prone to pollution attacks. Most existing schemes to combat pollution attacks did not consider the defender's resource limit, nor the trade-off between defensive performance and other metrics such as delay and resource consumption. The authors investigate how to achieve such a trade-off optimally by proposing a two-player strategic game model between the attack and the defender. In this model, the utilities of both players are well defined, and thus the defender can obtain its best strategy by maximising its utility. To do so, a graph-based simulated annealing algorithm is proposed to derive the utility-maximising strategy. Finally, they conduct extensive experiments to evaluate their scheme from different aspects. The results show that their scheme can achieve better utility than existing schemes, and is more computationally efficient in the meanwhile. Moreover, their scheme can obtain a sub-optimal solution within a small number of iterations, which implies that it can be implemented in the short-session communication scenario where it is required to find a sufficiently good solution within a short time.
This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmiss...
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This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our design is rooted in the theory of network coding. Prior work on network coding is mainly theoretical and focuses on multicast traffic. This paper aims to bridge theory with practice;it addresses the common case of unicast traffic, dynamic and potentially bursty flows, and practical issues facing the integration of network coding in the current network stack. We evaluate our design on a 20-node wireless network, and discuss the results of the first testbed deployment of wireless network coding. The results show that using COPE at the forwarding layer, without modifying routing and higher layers, increases network throughput. The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol.
Short message noisy network coding (SNNC) differs from long message noisy network coding (LNNC) in that one transmits many short messages in blocks rather than using one long message with repetitive encoding. Two prop...
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Short message noisy network coding (SNNC) differs from long message noisy network coding (LNNC) in that one transmits many short messages in blocks rather than using one long message with repetitive encoding. Two properties of SNNC are developed. First, SNNC with backward decoding achieves the same rates as SNNC with offset encoding and sliding window decoding for memoryless networks where each node transmits a multicast message. The rates are the same as LNNC with joint decoding. Second, SNNC enables early decoding if the channel quality happens to be good. This leads to mixed strategies that unify the advantages of decode-forward and noisy network coding.
The coding capacity of a network is the supremum of ratios k/n, for which there exists a fractional (k, n) coding solution, where k is the source message dimension and n is the maximum edge dimension. The coding capac...
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The coding capacity of a network is the supremum of ratios k/n, for which there exists a fractional (k, n) coding solution, where k is the source message dimension and n is the maximum edge dimension. The coding capacity is referred to as routing capacity in the case when only routing is allowed. A network is said to achieve its capacity if there is some fractional (k, n) solution for which k/n equals the capacity. The routing capacity is known to be achievable for arbitrary networks. We give an example of a network whose coding capacity (which is 1) cannot be achieved by a network code. We do this by constructing two networks, one of which is solvable if and only if the alphabet size is odd, and the other of which is solvable if and only if the alphabet size is a power of 2. No linearity assumptions are made.
In this paper, we consider the problem of building a secure network against node conspiracy attack that based on network segmentation. As we know, network coding has demonstrated its great application prospects in wir...
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In this paper, we consider the problem of building a secure network against node conspiracy attack that based on network segmentation. As we know, network coding has demonstrated its great application prospects in wireless sensor network (WSN) transmission. At the same time, it is facing a variety of security threats, especially conspiracy attack. In existing research, secure coding design strategies are much more than secure topological structure. In this background, a weakly secure scheme is proposed from the perspective of topology and network segmentation. Based on the network segmentation and topology design, the network coding transmission is weakly secure. We conduct a simulation to show that the proposed scheme can efficiently prevent conspiracy attack.
It is very difficult to deal with the problem of error correction in random network coding, especially when the number of errors is more than the min-cut of the network. We combine a small field with rank-metric codes...
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It is very difficult to deal with the problem of error correction in random network coding, especially when the number of errors is more than the min-cut of the network. We combine a small field with rank-metric codes to solve this problem in this paper. With a small finite field, original errors are compressed to propagated errors, and their number is smaller than the min-cut. Rank-metric codes are introduced to correct the propagated errors, while the minimum rank distance of the rank-metric code is hardly influenced by the small field. It is the first time to correct errors more than the min-cut in network coding with our method using a small field. This new error-correcting algorithm is very useful for the environment such as a wireless sensor network where network coding can be applied.
network coding is a promising generalization of routing which allows a node to generate output messages by encoding its received messages. A typical scenario where network coding offers unique advantages is a multicas...
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network coding is a promising generalization of routing which allows a node to generate output messages by encoding its received messages. A typical scenario where network coding offers unique advantages is a multicast network where a source node generates messages and multiple receivers collect the messages. In a multicast network, linear network codes are preferred due to its sufficiency and simplicity. In this paper, we propose an approach to transforming the linear coding problem into a graph theory problem. By utilizing hypergraphs, we model the linear codes by constructing a pseudodual graph of the multicast network. Then, a valid linear code is equivalent to a cover in the pseudodual graph satisfying some constraints. By iterative refinements, an eligible cover can be found in polynomial time. Moreover, we propose several preprocessing algorithms to further reduce the computation time required by the iterative refinements by reducing the graph size before transformation. An important contribution of this work is that the proposed approach can be readily extended to solve many minimal network coding problems. By assigning different weights to edges, minimal network coding problems are reduced to the shortest path problem in the pseudodual graph. Our simulation results show that the proposed preprocessing algorithms can reduce the computation time by about 40-50 percent in a medium size multicast network compared to the scheme without preprocessing algorithms, and the throughput of the system with network coding is 25 percent higher than that with the traditional approach of multiple multicast trees.
This article studies the scalable broadcast scheme realized with the joint application of layered source coding,unequal error protection （UEP） and random network coding from the theoretical point of *** success prob...
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This article studies the scalable broadcast scheme realized with the joint application of layered source coding,unequal error protection （UEP） and random network coding from the theoretical point of *** success probability for any non-source node in a heterogeneous network to recover the most important layers of the source data is *** probability proves that in this broadcast scheme every non-source node with enough capacity can always recover the source data partially or entirely as long as the finite field size is sufficiently ***,a special construction for the local encoding kernel at the source node is *** this special construction,an increased success probability for partial decoding at any non-source node is achieved,i.e.,the partial decodability offered by the scalable broadcast scheme is improved.
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