A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Noise resistant generalized parametric validity index of clustering for gene expression data

This article has been made available through the Brunel Open Access Publishing Fund.Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters α and β to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage of the proposed GPV index is its noise-resistance ability, which results from the flexibility of tuning the parameters. Several rules are set to guide the selection of parameter values. To illustrate the noise-resistance performance of the proposed index, we evaluate the GPV index for assessing five clustering algorithms in two gene expression data simulation models with different noise levels and compare the ability of determining the number of clusters with eight existing indices. We also test the GPV in three groups of real gene expression data sets. The experimental results suggest that the proposed GPV index has superior noise-resistance ability and provides fairly accurate judgements

Building a comprehensive mentoring academy for schools of health.

Formal mentoring programs are increasingly recognized as critical for faculty career development. We describe a mentoring academy (MA) developed for faculty across tracks (i.e., researchers, clinicians, educators) within a "school of health" encompassing schools of medicine and nursing. The program is anchored dually in a clinical and translational science center and a school of health. The structure includes the involvement of departmental and center mentoring directors to achieve widespread uptake and oversight. A fundamental resource provided by the MA includes providing workshops to enhance mentoring skills. Initiatives for junior faculty emphasize establishing and maintaining strong mentoring relationships and implementing individual development plans (IDPs) for career planning. We present self-report data on competency improvement from mentor workshops and data on resources and barriers identified by junior faculty (n = 222) in their IDPs. Mentors reported statistically significantly improved mentoring competency after workshop participation. Junior faculty most frequently identified mentors (61%) and collaborators (23%) as resources for goal attainment. Top barriers included insufficient time and time-management issues (57%), funding limitations (18%), work-life balance issues (18%), including inadequate time for self-care and career development activities. Our MA can serve as a model and roadmap for providing resources to faculty across traditional tracks within medical schools

A virtual MIMO dual-hop architecture based on hybrid spatial modulation

International audienceIn this paper, we propose a novel Virtual Multiple-Input-Multiple-Output (VMIMO) architecture based on the concept of Spatial Modulation (SM). Using a dual-hop and Decode-and-Forward protocol, we form a distributed system, called Dual-Hop Hybrid SM (DH-HSM). DH-HSM conveys information from a Source Node (SN) to a Destination Node (DN) via multiple Relay Nodes (RNs). The spatial position of the RNs is exploited for transferring information in addition to, or even without, a conventional symbol. In order to increase the performance of our architecture, while keeping the complexity of the RNs and DN low, we employ linear precoding using Channel State Information (CSI) at the SN. In this way, we form a Receive-Spatial Modulation (R-SM) pattern from the SN to the RNs, which is able to employ a centralized coordinated or a distributed uncoordinated detection algorithm at the RNs. In addition, we focus on the SN and propose two regularized linear precoding methods that employ realistic Imperfect Channel State Information at the Transmitter. The power of each precoder is analyzed theoretically. Using the Bit Error Rate (BER) metric, we evaluate our architecture against the following benchmark systems: 1) single relay; 2) best relay selection; 3) distributed Space Time Block Coding (STBC) VMIMO scheme; and 4) the direct communication link. We show that DH-HSM is able to achieve significant Signal-to-Noise Ratio (SNR) gains, which can be as high as 10.5 dB for a very large scale system setup. In order to verify our simulation results, we provide an analytical framework for the evaluation of the Average Bit Error Probability (ABEP)