BIOMONITORING OF ECOLOGICAL STATE OF THE ENVIRONMENT IN THE ZONE OF INFLUENCE OF THE “CHERVONOGRADSKA” MINE OF THE LVIV-VOLYN COALFIELD

Coal mining has a very negative impact on the environment and it requires monitoring studies to assess the degree of environmental pollution

Characterization of Coded Random Access with Compressive Sensing based Multi-User Detection

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.Comment: Submitted to Globecom 201

Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks

We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achieve consensus on the estimates over the whole network. Multiple kernels are incorporated to enhance the approximation of functions with several high and low frequency components common in practical scenarios. We provide a thorough convergence analysis of the proposed scheme based on the metric of the Cartesian product of multiple reproducing kernel Hilbert spaces. To this end, we introduce a modified consensus matrix considering this specific metric and prove its equivalence to the ordinary consensus matrix. Besides, the use of hyperslabs enables a significant reduction of the computational demand with only a minor loss in the performance. Numerical evaluations with synthetic and real data are conducted showing the efficacy of the proposed algorithm compared to the state of the art schemes.Comment: Double-column 15 pages, 10 figures, submitted to IEEE Trans. Signal Processin

Nonreciprocal Bloch Oscillations in Magneto-Optic Waveguide Arrays

We show that nonreciprocal optical Bloch-like oscillations can emerge in transversely magnetized waveguide arrays in the presence of an effective index step between the waveguides. Normal modes of the system are shown to acquire different wavenumbers in opposite propagation directions. Significant differences in phase coherence and decoherence between these normal modes are presented and discussed. Non-reciprocity is established by imposing unequal vertical refractive index gradients at the substrate/core, and core/cover interfaces in the presence of transverse magnetization.Comment: 12 pages, 2 figure

On the Importance of Exploration for Real Life Learned Algorithms

The quality of data driven learning algorithms scales significantly with the quality of data available. One of the most straight-forward ways to generate good data is to sample or explore the data source intelligently. Smart sampling can reduce the cost of gaining samples, reduce computation cost in learning, and enable the learning algorithm to adapt to unforeseen events. In this paper, we teach three Deep Q-Networks (DQN) with different exploration strategies to solve a problem of puncturing ongoing transmissions for URLLC messages. We demonstrate the efficiency of two adaptive exploration candidates, variance-based and Maximum Entropy-based exploration, compared to the standard, simple epsilon-greedy exploration approach

Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient

Following the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning, i.e., semantics, of a message instead of its exact version, allowing for information rate savings. In this work, we apply the Stochastic Policy Gradient (SPG) to design a semantic communication system by reinforcement learning, separating transmitter and receiver, and not requiring a known or differentiable channel model -- a crucial step towards deployment in practice. Further, we derive the use of SPG for both classic and semantic communication from the maximization of the mutual information between received and target variables. Numerical results show that our approach achieves comparable performance to a model-aware approach based on the reparametrization trick, albeit with a decreased convergence rate.Comment: Accepted for publication in IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Source Code: https://github.com/ant-uni-bremen/SINFON

Downlink beamforming concepts in UTRA FDD

This article gives a comparison of beamforming concepts. Adaptive beamforming and fixed beam switching in WCDMA-FDD-mode are compared from a system level perspective, ordinary sectorization (three 120° sectors) serves as a basis for comparison. Pilot channels P-CPICH (Primary Common Pilot Channel) and S-CPICH (Secondary CPICH) are considered as additional interference. For adaptive beamforming channel estimation has to be based on the pilot bit sequence on DPCCH (Dedicated Physical Control Channel) which leads to degradation especially for high mobile velocities and large angular dispersions of the multipath channel

Стратегия установления выводимости формул в структурных функциональных моделях

Рассматривается исчисление рекурсивных предложений для теории структурных функциональных моделей. Исследуются вопросы разрешимости и полноты исчисления. Предлагаются стратегия и алгоритм установления выводимости формул исчисления, показывается корректность алгоритма и определяется оценка его эффективности