Alexander M. Wyglinski

Martha Cash, Charlotte Fowler, Alexander M. Wyglinski

This paper analyzes the role of time-series clustering in traffic matrix (TM) prediction. Traffic flows within a TM often exhibit heterogeneous behavior, which can reduce the effectiveness of global forecasting models that predict all flows jointly. To address this, we propose a clustering-based prediction framework that groups flows into smaller subsets and trains separate predictors for each group. Four traffic-flow representations for clustering are explored, namely, histogram, autocorrelation function (ACF), power spectral density (PSD), and naïve partitioning, and how the representation choice and the number of clusters affect prediction performance. Experiments using the publicly available Abilene and GÉANT datasets show that clustering consistently improves over global forecasting baselines, while remaining substantially less costly than local prediction. The results further show that most of the performance gain is achieved at moderate values of K, with diminishing returns as the number of clusters increases. Although different clustering representations produce different partitions of the traffic flows, they often achieve similar root mean squared error (RMSE). This suggests that the main benefit of clustering lies in decomposing the TM prediction task into smaller subproblems, while the exact cluster structure plays a more limited role in determining overall prediction accuracy.

Kuldeep S. Gill, Son Nguyen, Myo M. Thein et al.

In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform localization using a deep neural network. Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in facilitating dynamic spectrum access (DSA) in beyond-5G and 6G communication technologies. Localization, wireless signal detection, and spectrum policy-making are several of the applications where distributed spectrum sensing will play a significant role. Detection and positioning of wireless emitters is a very challenging task in a large spectral and spatial area. In order to construct a smooth RFMap database, a large number of measurements are required which can be very expensive and time consuming. One approach to help realize these systems is to collect finite localized measurements across a given area and then interpolate the measurement values to construct the database. Current methods in the literature employ channel modeling to construct the radio frequency map, which lacks the granularity for accurate localization whereas our proposed approach reconstructs a new generalized RFMap. Localization results are presented and compared with conventional channel models.