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The highly anticipated 5G network is projected to be introduced in 2020. 5G stakeholders are unanimous that densification of mobile networks is the way forward. The densification will be realized by means of small cell technology, and it is capable of providing coverage with a high data capacity. The EU-funded H2020-MSCA project ''SECRET'' introduced covering the urban landscape with mobile small cells, since these take advantages of the dynamic network topology and optimizes network services in a cost-effective fashion. By taking advantage of the device-to-device communications technology, large amounts of data can be transmitted over multiple hops and, therefore, offload the general network. However, this introduction of mobile small cells presents various security and privacy challenges. Cryptographic security solutions are capable of solving these as long as they are supported by a key management scheme. It is assumed that the network infrastructure and mobile devices from network users are unable to act as a centralized trust anchor since these are vulnerable targets to malicious attacks. Security must, therefore, be guaranteed by means of a key management scheme that decentralizes trust. Therefore, this paper surveys the state-of-the-art key management schemes proposed for similar network architectures (e.g., mobile ad hoc networks and ad hoc device-to-device networks) that decentralize trust. Furthermore, these key management schemes are evaluated for adaptability in a network of mobile small cells. INDEX TERMS 5G, beyond 5G, decentralized systems, device-to-device communication, key management, mobile small cells, security, small cells, wireless ad hoc networks.

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Understanding and modeling the rainfall drop size distribution is important in a number of applications, in particular predicting and mitigating attenuation of satellite signals in the millimeter band. Various standard statistical distributions have been proposed as suitable models, the first widely accepted being the exponential distribution. Subsequently, gamma and lognormal distributions have been shown to provide better rainfall rate computations. Some empirical studies have revealed bimodal distributions under some circumstances. A natural question to ask therefore is how often gamma and lognormal distributions fit the empirical data. In this paper we fit lognormal and gamma distributions to 1 min slices of rainfall drop size distributions taken from 7 year data from the Chilbolton Observatory in southern England. The chi-square goodness of fit of the models against the data is calculated, and it is found that failure to fit is greater than would normally be expected. This failure to fit is broken down and examined against seasonal variations, different rain rates, atmospheric temperature, and wind speed. Possible reasons for the lack of fit are explored, and alternative fits using models based on Gaussian Mixture Models are developed and found to be an improvement. DSD Modeling Standard Statistical ModelsRaindrop size distribution, denoted N(D) and expressed in mm À1 m À3 , is defined as the number of raindrops per unit volume per unit diameter, centered on D (in mm). Thus, N(D)dD, expressed in m À3 , is the number of such drops per unit volume having diameters in the infinitesimal range (D À dD/2, D + dD/2) of size dD centered on D. Various standard classical statistical distributions have been proposed in literature as models EKERETE ET AL.MODELING RAINFALL DSD WITH A GMM 876 PUBLICATIONS

The fade slope of rain attenuation at Sparsholt, U.K. has been measured for a link with the ITALSAT F1 and F2 satellites at frequencies of 18.7, 39.6, and 49.5 GHz and compared with the corresponding ITU-R model. The model fits the data well visually with a tendency to be less accurate with increasing frequency due to the assumption of constant scaling of equiprobable attenuations being less accurate at higher frequencies. The probability distribution used in the model fails a chi-squared test for all confidence levels when compared with the data. Conditional statistics of fade slope on a diurnal and seasonal basis are also analyzed and reveal that the autumn season has higher fade slopes than any other, which can in part be attributed to the higher ratio of stratiform to convective rain in the autumn season. Fade slope was found to have slightly higher values during midday.