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Personal exposure meters for assessing exposure to RF electric or magnetic fields are subject to errors associated with perturbations of the fields by the presence of the human body. Although these alterations are complex they are not completely unpredictable. This article concludes that this error in a common worst-case scenario could reach up to 30 dB and therefore is of concern for exposure assessment. We present several guidelines to address this issue and a useful insight into the overall problem based on finite-difference time-domain simulations and experimental verification.

5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.