Channel Measurement and Noise Estimation in FBMC/OQAM-Based IoT Networks

Channel measurement plays an important role in the emerging 5G-enabled Internet of Things (IoT) networks, which reflects the channel quality and link reliability. In this paper, we address the channel measurement for link reliability evaluation in filter-bank multicarrier with offset quadrature amplitude modulation- (FBMC/OQAM-) based IoT network, which is considered as a promising technique for future wireless communications. However, resulting from the imaginary interference and the noise correlation among subcarriers in FBMC/OQAM, the existing frequency correlation method cannot be directly applied in the FBMC/OQAM-based IoT network. In this study, the concept of the block repetition is applied in FBMC/OQAM. It is demonstrated that the noises among subcarriers are independent by the block repetition and linear combination, instead of correlated. On this basis, the classical frequency correlation method can be applied to achieve the channel measurement. Then, we also propose an advanced frequency correlation method to improve the accuracy of the channel measurement, by assuming channel frequency responses to be quasi-invariant for several successive subcarriers. Simulations are conducted to validate the proposed schemes.

On the node reliability of Harary graphs: an analysis through real-world topologies

The literature on network reliability shows that Harary networks are designed so that the link reliability is maximum in many cases. The following question: ``what are the best topology networks in maximizing node reliability?'' is still open. In this paper, we performed computational experiments with eleven real-world networks and their corresponding Harary graphs. The node reliability of both sets of networks was computed. Computational results point out that the Harary network has a topology with high node reliability if compared to the real-world networks studied.

On the node reliability of Harary graphs: an analysis through real-world topologies

The literature on network reliability shows that Harary networks are designed so that the link reliability is maximum in many cases. The following question: ``what are the best topology networks in maximizing node reliability?'' is still open. In this paper, we performed computational experiments with eleven real-world networks and their corresponding Harary graphs. The node reliability of both sets of networks was computed. Computational results point out that the Harary network has a topology with high node reliability if compared to the real-world networks studied.

Reliable Communications for Vehicular Networks

Vehicular communications, referring to information exchange among vehicles, and infrastructures. It has attracted a lot of attentions recently due to its great potential to support intelligent transportation, various safety applications, and on-road infotainment. The aim of technologies such as Vehicle-to-Vehicl (V2V) and Vehicle to-Every-thibg (V2X) Vehicle-to very-thing is to include models of connectivity that can be used in various application contexts by vehicles. However, the routing reliability of these ever-changing networks needs to be paid special attention. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road.

All-terminal network reliability estimation using convolutional neural networks

Estimating the all-terminal network reliability by using artificial neural networks (ANNs) has emerged as a promissory alternative to classical exact NP-hard algorithms. Approaches based on traditional ANNs have usually considered the network reliability upper bound as part of the inputs, which implies additional time-consuming calculations during both training and testing phases. This paper proposes the use of Convolutional Neural Networks (CNNs), without the reliability upper-bound as an input, to address the all-terminal network reliability estimation problem. The present study introduces a multidimensional matrix format to embed the topological and link reliability information of networks. The unique contribution of this article is the method to capture the topology of a network in terms of its adjacency matrix, link reliability, and topological attributes providing a novel use of CNN beyond image classification. Since CNNs have been successful for image classification, appropriate modifications are needed and introduced to use them in the estimation of network reliability. A regression output layer is proposed, preceded by a sigmoid layer to achieve predictions within the range of reliability characteristic, a feature that some previous ANN-based works lack. Several training parameters together with a filter multiplier (CNN architecture parameter) were investigated. The actual values and the ones predicted with the best trained CNN were compared in the light of RMSE (0.04406) and p-value (0.3) showing non-significant difference. This study provides evidence supporting the hypothesis that the network reliability can be estimated by CNNs from its topology and link reliability information, embedded as an image-like multidimensional matrix. Another important result of the proposed approach is the significant reduction in computational time. An average of 1.18 ms/network was achieved by the CNN, whereas backtracking exact algorithm took around 500 s/network.