A hybrid cloud radio access network (H-CRAN) architecture has been proposed to alleviate the midhaul capacity limitation in C-RAN. In this architecture, functional splitting is utilized to distribute the processing functions between a central cloud and edge clouds. The flexibility of selecting specific split point enables the H-CRAN designer to reduce midhaul bandwidth, or reduce latency, or save energy, or distribute the computation task depending on equipment availability. Meanwhile, techniques for caching are proposed to reduce content delivery latency and the required bandwidth. However, caching imposes new constraints on functional splitting. In this study, considering H-CRAN, a constraint programming problem is formulated to minimize the overall power consumption by selecting the optimal functional split point and content placement, taking into account the content access delay constraint. We also investigate the trade-off between the overall power consumption and occupied midhaul bandwidth in the network. Our results demonstrate that functional splitting together with enabling caching at edge clouds reduces not only content access delays but also fronthaul bandwidth consumption but at the expense of higher power consumption.
The decomposition of signals into their primitive or fundamental constituents play a vital role in removing noise or unwanted signals, thereby improving the quality and utility of the signals. There are various decomposition techniques, among which the linear wavelet technique and the Variational Mode Decomposition (VMD) are the most recent and widely used ones. This paper presents a comparative study of the decomposition of spatially inhomogeneous test functions namely Doppler and Bumps used by statisticians. An effort is made in this article to compare the efficiency of the noise removal in the resulting decompositions at various approximation levels using wavelets and by varying the number of reconstruction modes in VMD. Surprisingly it is found that the VMD technique yields better results with more accuracy for a specific set of parameters irrespective of the spatial character of the function.
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