This paper considers the power-efficient resource allocation problem in a cloud radio access network (CRAN). The C-RAN architecture consists of a set of base-band units (BBUs) which are connected to a set of radio remote heads (RRHs) equipped with massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. We formulate the power-efficient optimization problem in C-RANs as a joint resource allocation problem in order to jointly allocate the RRH and transmit power to each user, and fronthaul link and BBU assign to active RRHs while satisfying the minimum required rate of each user. To solve this non-convex optimization problem we suggest iterative algorithm with two-step based on the complementary geometric programming (CGP) and the successive convex approximation (SCA). The simulation results indicate that our proposed scheme can significantly reduce the total transmission power by switching off the under-utilized RRHs.

This paper questions some aspects of attributeacceptance sampling in light of the original concepts ofhypothesis testing from Neyman and Pearson (NP). Attributeacceptance sampling in industry, as developed byDodge and Romig (DR), generally follows the internationalstandards of ISO 2859, and similarly the Brazilian standardsNBR 5425 to NBR 5427 and the United StatesStandards ANSI/ASQC Z1. 4. The paper evaluates andextends the area of acceptance sampling in two directions. First, by suggesting the use of the hypergeometric distributionto calculate the parameters of sampling plansavoiding the unnecessary use of approximations such as thebinomial or Poisson distributions. We show that, underusual conditions, discrepancies can be large. The conclusionis that the hypergeometric distribution, ubiquitouslyavailable in commonly used software, is more appropriatethan other distributions for acceptance sampling. Second, and more importantly, we elaborate the theory of acceptancesampling in terms of hypothesis testing rigorouslyfollowing the original concepts of NP. By offering acommon theoretical structure, hypothesis testing from NPcan produce a better understanding of applications evenbeyond the usual areas of industry and commerce such aspublic health and political polling. With the new procedures, both sample size and sample error can be reduced. What is unclear in traditional acceptance sampling is thenecessity of linking the acceptable quality limit (AQL)exclusively to the producer and the lot quality percentdefective (LTPD) exclusively to the consumer. In reality, the consumer should also be preoccupied with a value ofAQL, as should the producer with LTPD. Furthermore, wecan also question why type I error is always uniquelyassociated with the producer as producer risk, and likewise, the same question arises with consumer risk which isnecessarily associated with type II error. The resolution ofthese questions is new to the literature. The article presentsR code throughout.