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Abstract-In this paper, we present a new resource allocation scheme for cell-edge active users to achieve improved performance in terms of a higher system capacity and better quality-of-service (QoS) guarantee of the users, where we utilize the two-dimensional resource allocation flexibility of orthogonal frequency division multiple access (OFDMA) networks. Here, the mobile stations (MSs) at the cell-edge can maintain parallel connections with more than one base station (BS) when it is in their coverage area. A MS, before handoff to a new BS, seeks to utilize additional resources from the other BSs if the BS through which its current session is registered is not able to satisfy its requirements. The handoff procedure is termed as split handoff. The BSs participate in split handoff operation while guaranteeing that they are able to maintain QoS of the existing connections associated with them. In this study, first, we present the proposed shared resource allocation architecture and protocol functionalities in split handoff, and give a theoretical proof of concept of system capacity gain associated with the shared resource allocation approach. Then, we provide a differentiated QoS provisioning approach that accounts for the MS speed, its channel quality, as well as the loads at different BSs. Via extensive simulations in Qualnet, the benefits of the proposed class-based split handoff approach is demonstrated. The results also indicate traffic load balancing property of the proposed scheme in heavy traffic conditions.

To determine the critical component in an industry is one of the most important tasks performed by maintenance personnel to choose the best maintenance policy. Therefore, the purpose of the current paper is to develop a methodology based on integrated cloud model and extended preference ranking organization method for enrichment evaluation (PROMETHEE) method for finding the most critical component of the framework by ranking the failure causes of the system from multiple decision maker perspective. For this purpose, ranking of failure causes is performed by taking into account five factors namely chances of occurrence of failure (F0), non-detection probability (Nd), downtime duration (Dd), spare part criticality (Spc) and safety risk (Sr). In this paper, first the primary and secondary weight of decision makers are calculated based on the uncertainty degree and divergence degree, respectively, to determine overall weight using cloud model theory by converting the uncertain linguistic evaluation matrix into interval cloud matrix, and then ranking of the steam handling subunit of paper making unit in a paper mill using extended PROMETHEE. The effectiveness of the proposed methodology is explained by considering steam handling subunit of paper making unit to find the critical component.

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