An Analytical Framework for Effective Joint Scheduling Over TDD-Based Mobile Networks

A mathematical framework for effective routing over low power and lossy networks

“…Although the EIRPL holistically enables sorting deployments according to their effective performance, capturing an attained performance level requires additional information about the KIs. Besides the EIRPL, an analytical scheme is adopted that comprises the Percentage of Satisfaction (PoS) that equals the percentage of KIs that attain a rank of Satisfactory and above . For instance, a PoS that is greater than 90 % is ranked as High.…”

“…The analysis within the EIRPL framework employs standard statistical formulations that include the geometric mean and the percentage of indicators that at least achieve the rank of Satisfactory according to Tables and . The use of mean in analysis is, by its very nature, a summary measure that is sensitive to outliers with extreme values, to this end, aggregate scores and their corresponding classifications and can be largely affected and deviated . The employed PoS, as combined with the EIRPL indicator, lessens or omits the effect of outliers by calculating the number of satisfactory indicators.…”

“…Damaj and Kasbah do not present classification schemes or evaluation charts. Moreover, a framework for joint scheduling of Time Division Duplexing‐Orthogonal Frequency‐Division Multiple Access (TDD‐OFDMA) networks is presented in Damaj et al The framework includes a rich set of indicators of importance within mobile networks, namely, complexity, communication interference, fairness, and reliability. In addition, a similar framework that proposes the optimization fitness indicator with application on hardware and software implementations of particle swarm optimization .…”

“…The proposed formulation ranks and classifies algorithm deployments according to their effectiveness; to that end, the framework can be adopted within a decision making process. Indeed, the framework is comparable to MCDM . Our proposed framework and MCDM adopt a similar approach that comprise identifying criteria, collecting information, developing a decision tree of alternate options, and evaluate and rank the identified options.…”

“…Furthermore, the proposed framework adopts the geometric mean in the calculation of its CMIs with the main outcome of ranking and classification; such a use is limitedly reported in MCDM. For example, Analytic Hierarchy Process, a famous MCDM technique, uses the geometric mean for a different purpose that focuses on enabling the consideration of fuzzy variables using linguistic terms . As far as we know, the use of MCDM in the context of mobile communication systems has been limited .…”