Joint Fixed Power Allocation and Partial Relay Selection Schemes for Cooperative NOMA

 In the future wireless systems, non-orthogonal multiple-access (NOMA) with partial relay selection scheme is considered as developing research topic. In this paper, dual-hop relaying systems is deployed for NOMA, in which the signal is transfered with the assistance of decode-and-forward (DF) scheme. This paper presents exact expressions for outage probability over independent Rayleigh fading channels, and two partial relay selection schemes are provided. Using matching analytical result and Monte-Carlo method, we introduce forwarding strategy selection for fixed user allocation and exactness of derived formula is checked. The presented simulations confirm the the advantage of such considered NOMA, and the effectiveness of the proposed forwarding strategy

A computational algorithm for the CPP/M/c retrial queue

This paper introduces the retrial CPP/M/c queue, which is the general- ization of the M/M/c retrial queue. The arrival process of jobs into the queue follows the Compound Poisson Process (CPP). We present an efficient and numerically stable computational algorithm for the steady state probabilities

New Algorithms for Balancing Energy Consumption and Performance in Computational Clusters

In this paper, we propose new real-time measurement-based scheduling algorithms to achieve a trade-off between the energy efficiency and the performance capability of computational clusters. An investigation is performed using a specific scenario of computing clusters with realistic parameters. Numerical results show that a trade-off between the performance and the energy efficiency can be controlled by the proposed algorithms

Regression models to predict the resource usage of MapReduce application

MapReduce applications are used to process big data. Therefore, the prediction of the resource usage of MapReduce applications is crucially needed. In this paper, we construct multiple linear regression models to predict the resource usage parameters of MapReduce applications

Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications

The forecasting of the resource usage of MapReduce applications plays an important role in the operation of cloud infrastructure. In this paper, we apply long short-term memory recurrent neural networks to predict the resource usage of three representative MapReduce applications. The Results show that the Long Short-term Memory Recurrent Neural Networks models perform higher prediction accuracy than persistence ones. Predictions of other usage parameters show similar accuracy with persistence one. The improper configuration parameters of Long Short-term Memory Recurrent Neural Networks possibly result in few of worse prediction

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

AbstractA quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+$$\epsilon$$ ϵ )-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well