The networks are evolving drastically since last few years in order to meet user requirements. For example, the 5G is offering most of the available spectrum under one umbrella. In this work, we will address the resource allocation problem in fifth-generation (5G) networks, to be exact in the Cloud Radio Access Networks (C-RANs). The radio access network mechanisms involve multiple network topologies that are isolated based on the spectrum bands and it should be enhanced with numerous access technology in the deployment of 5G network. The C-RAN is one of the optimal technique to combine all the available spectral bands. However, existing C-RAN mechanisms lacks the intelligence perspective on choosing the spectral bands. Thus, C-RAN mechanism requires an advanced tool to identify network topology to allocate the network resources for substantial traffic volumes. Therefore, there is a need to propose a framework that handles spectral resources based on user requirements and network behavior. In this work, we introduced a new C-RAN architecture modified as multitier Heterogeneous Cloud Radio Access Networks in a 5G environment. This architecture handles spectral resources efficiently. Based on the simulation analysis, the proposed multitier H-CRAN architecture with improved control unit in network management perspective enables augmented granularity, end-to-end optimization, and guaranteed quality of service by 15 percentages over the existing system.Trans Emerging Tel Tech. 2019;30:e3627.wileyonlinelibrary.com/journal/ett spectrum like WiFi. 2 The advanced antennas with multiple input and multiple output technologies can be adopted in the evolving 5G to provide higher data rate. 3,4 The colossal requirements of future 5G networks cannot be accomplished by the existing radio access networks (RANs), wherein the base band units (BBUs) and radio units are consolidated. 2 Meanwhile, the cloud-enabled services in 5G ecosystem cater heterogeneous communication framework utilizing advanced virtualization techniques. The network virtualization stimulates multiservice and multitenancy for efficient network operations and service provisions, which, in turn, offers new-fangled service-oriented and edge-cloud 5G architectures with enhanced Quality of Experience (QoE). 5 Thus, the current RAN architectures need to be further extended with various virtualization techniques to develop Cloud RAN (C-RAN). Cloud RANs abutments the correlation of Access Points (APs) to the pool of BBU via a troop of transport links in the Control Unit (CU), 6 which conquers the limitations in the traditional RAN. 7 However, C-RAN requires an intelligent technique to recognize the topology of the network in the mobility stage and to locate the resources, as the vast traffic volume created by the sampled radio signals are directly transported to the CU. 8 The next-generation networks are expected to learn consistently based on the varied user behavior and spectral stability that depends on both the user's environment and network behavior. 9 Thereby, sea...

An intricate network deployment for high demand users leads to simultaneous transmission in wireless mesh networks. Multiple radios are adapted to individual nodes for improving network performance and Quality of Service (QoS). However, whenever multiple radios are assigned to the same channel, co-located radio interference occurs, which poses a major drawback. This paper proposes a Radio aware Channel Assignment (Ra-CA) mechanism based on a direct graphical model for mitigation of interference in multi-radio multi-channel networks. Initially, the co-located radio interference is identified by classifying non-interfering links for simultaneous transmission in the network. Proposed channel assignment mechanism helps in allocating the minimal number of channels to the network that mitigate co-located radio interference. Performance analysis of the proposed Ra-CA strategy is carried out compared with other existing techniques, like BFS-CA and MaIS-CA, in multi-radio networks. Simulation results demonstrate that the proposed channel assignment scheme is more efficient compared to the existing ones, in terms of QoS parameters like, packet drop rate, packet delivery ratio, transmission delay and throughput.

No abstract

No abstract

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.