This paper looks into the extension of the pymcdm library, focusing on reference point-based techniques. It introduces the implementations of methods such as the Reference Ideal Method (RIM), Preference Ranking On the Basis of Ideal-average Distance (PROBID), and Election based on Relative Value Distances (ERVD). This update is intended to meet the increasing demand for solutions tailored to decision makers’ expertise and experience. Furthermore, it introduces techniques related to sensitivity analysis and weight comparison factors of criteria. By expanding this library, the range of MCDA/MCDM tools is broadened and further progress is encouraged in the use of expert knowledge and the implementation of compromise methods.
The proliferation of sixth-generation (6G) networks and the massive Internet of Things (IoT) demand wireless communication technologies that are ultra-low-power, secure, and covert. Noise-based communication has emerged as a transformative paradigm that meets these demands by encoding information directly into the statistical properties of noise, rather than using traditional deterministic carriers. This survey provides a comprehensive synthesis of this field, systematically exploring its fundamental principles and key methodologies, including thermal noise modulation (TherMod), noise modulation (NoiseMod) and its variants, and the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange. We address critical practical challenges such as channel estimation and hardware implementation, and highlight emerging applications in simultaneous wireless information and power transfer (SWIPT) and non-orthogonal multiple access (NOMA). Our analysis confirms that noise-based systems offer unparalleled advantages in energy efficiency and covertness, and we conclude by outlining future research directions to realize their potential for enabling the next generation of autonomous and secure wireless networks.
Multipole expansion methods have been primarily used for analyzing the electromagnetic scattering from non-magnetic isotropic dielectric scatterers, and studies about the scattering from magnetic objects seem to be lacking. In this work, we used the multipolar expansion framework for decomposing the electromagnetic scattering by dielectric particles with magnetic properties. Magnetization current contributions were explicitly accounted for by using the vector spherical harmonics to compute the electric and magnetic multipole contributions of arbitrary order. The exact analytical expressions for the corresponding spherical multipole coefficients were employed, with the scattering efficiencies being used to distinguish the dielectric and magnetic contributions of each multipole. This enables the analysis of scattering from arbitrarily shaped, anisotropic, and inhomogeneous magnetic scatterers. It also provides a tool for studying non-reciprocal devices that exploit magnetic resonances in magnetic-dielectric materials. Calculations were made for an experimentally feasible system, namely for ferrite-based scatterers operating in the microwave regime. These materials are of interest in radio frequency (RF) applications due to their magnetic activity. We demonstrated analytically that the magnetic circular dichroism in a magnetic-dielectric scatterer in the Faraday geometry can be decomposed into individual multipole contributions. The analytical results indicate that multipole resonances associated with magnetization currents can be even stronger than multipole contributions from conventional dielectric currents. It is worth noting that these analytical results were verified through comparison with numerical results from finite element method (FEM) simulations in COMSOL Multiphysics.
Selecting the Modulation and Coding Scheme (MCS) from the measured Signal-to-Noise Ratio (SNR) is commonly implemented via precomputed Look-Up Tables (LUTs) derived from analytical models, link-level simulations under idealized assumptions, or empirical Bit Error Rate (BER)/Block Error Rate (BLER) thresholds. In operational Software-Defined Radio (SDR) platforms, however, non-ideal channel estimation, pilot-grid interpolation, equalizer noise enhancement, decoder non-idealities, and Radio Frequency (RF) front-end impairments distort this mapping, causing conventional tables to miss the expected goodput. This paper proposes a measurement-driven Adaptive Modulation and Coding (AMC) strategy based on Contextual Bandit (CB), which learns a stationary SNR→MCS policy directly from link measurements by maximizing downlink Medium Access Control (MAC)-layer goodput. The target application is a TV White Space (TVWS) backhaul in the Ultra High Frequency (UHF) band, designed to provide Internet connectivity to remote areas: both the Base Station (BS) and the remote terminal are fixed, typically under Line-of-Sight (LoS), yielding quasi-stationary channel conditions over many transmission intervals. We implement and evaluate the proposed approach on an SDR-based radio access system in two representative deployments: (i) an RF wireless link that exercises the full Physical Layer (PHY) stack under regulatory transmit-power constraints, and (ii) a Radio over Fiber (RoF) extension enabling tens-of-kilometers reach. The context is the measured SNR (discretized into bins), the action is the MCS index, and the reward is the delivered goodput; transmission errors and retransmissions are naturally captured through reduced goodput. For controlled experimentation and reproducible operating regimes, we constrain the effective SNR within a desired interval by programmatically adjusting the transmit-chain gain. As baselines, we consider as references the link adaptation based on an Inner Loop Link Adaptation (ILLA) obtained from laboratory calibration, an Outer Loop Link Adaptation (OLLA) that periodically adjusts the selected MCS using a BER target, and a supervised Histogram-Based Gradient Boosting Regressor (HGBR) baseline, trained offline from measured samples to approximate the goodput-maximizing SNR→MCS mapping. Experimental results show that the learned CB policy improves the area under the goodput–SNR curve and the mean Spectral Efficiency (SE) over both the baselines within the operational SNR range, while satisfying the BER target. In addition, by directly learning the optimal SNR→MCS mapping from interaction with the real environment, the proposed approach avoids the approximation stage required by the offline HGBR model and eliminates the transient convergence period required by the BER-driven OLLA to adapt from the initial ILLA table toward its steady-state operating point, thereby enabling immediate near-optimal performance in quasi-stationary wireless backhaul links.
The Weighted Similarity (WS) coefficient is an asymmetric measure increasingly applied for comparing rankings, particularly valuable in multi-criteria decision analysis (MCDA). Despite its widespread use, previous WS applications have lacked a formal statistical approach to assess significance. This paper addresses this gap by proposing a nonparametric significance test, called the SSD test, that generates empirical p values through random ranking comparisons under minimal assumptions. We formally analyze the statistical properties of the p-value estimator, including consistency, unbiasedness, variance, and asymptotic normality. To enhance computational efficiency, we also introduce an analytical approximation of the WS null distribution based on the Beta distribution. Simulations demonstrate that the Beta approximation remains reliable for moderate to large ranking lengths (n0 >= 6). Furthermore, we highlight that the Beta parameters, a and /i, exhibit dependence with ranking length, suggesting potential for analytical modeling in future research. Our findings establish a robust and statistically sound SSD test for WS-based inference, significantly improving the interpretability of ranking comparisons in complex decision-making problems.
