Godność w prawie pracy

Udostępnienie publikacji Wydawnictwa Uniwersytetu Łódzkiego finansowane w ramach projektu „Doskonałość naukowa kluczem do doskonałości kształcenia”. Projekt realizowany jest ze środków Europejskiego Funduszu Społecznego w ramach Programu Operacyjnego Wiedza Edukacja Rozwój; nr umowy: POWER.03.05.00-00-Z092/17-00. Publikacja dofinansowana przez Uniwersytet Kardynała Stefana Wyszyńskieg

Waveform flexibility in database-oriented cognitive wireless systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we discuss the idea of waveform flexibility in future wireless networks utilizing cognitive radio functionality. Mainly, we consider the possibility to adjust the shape of the waveform based on the information about the surrounding environment stored in a dedicated context-information database. In our approach, the cognitive terminal has an option to select one of four available waveforms to adapt itself in the best way to the constraints delivered by the database. In this paper we present the key concept of waveform flexibility, the proposed algorithm for waveform selection and the achieved simulation results.Peer ReviewedPostprint (author's final draft

DVB-T signal detection for indoor environments in low-SNR regime

The problem of coexistence between the primary (licensed) and secondary (non-licensed) users can be solved in various ways. One of them assumes the application of the detailed Radio Environment Maps being a kind of database, where some crucial information about the licensed transmission can be stored. In this paper we propose the new methods for signal detection in low signal-to-noise regime and compare it through hardware experiment with other known techniques used for spectrum sensing.Peer ReviewedPostprint (author’s final draft

Signaling Storm Detection in IIoT Network based on the Open RAN Architecture

The Industrial Internet of Things devices due to their low cost and complexity are exposed to being hacked and utilized to attack the network infrastructure causing a so-called Signaling Storm. In this paper, we propose to utilize the Open Radio Access Network (O-RAN) architecture, to monitor the control plane messages in order to detect the activity of adversaries at its early stage

Beam Management Driven by Radio Environment Maps in O-RAN Architecture

The Massive Multiple-Input Multiple-Output (M-MIMO) is considered as one of the key technologies in 5G, and future 6G networks. From the perspective of, e.g., channel estimation, especially for high-speed users it is easier to implement an M-MIMO network exploiting a static set of beams, i.e., Grid of Beams (GoB). While considering GoB it is important to properly assign users to the beams, i.e., to perform Beam Management (BM). BM can be enhanced by taking into account historical knowledge about the radio environment, e.g., to avoid radio link failures. The aim of this paper is to propose such a BM algorithm, that utilizes location-dependent data stored in a Radio Environment Map (REM). It utilizes received power maps, and user mobility patterns to optimize the BM process in terms of Reinforcement Learning (RL) by using the Policy Iteration method under different goal functions, e.g., maximization of received power or minimization of beam reselections while avoiding radio link failures. The proposed solution is compliant with the Open Radio Access Network (O-RAN) architecture, enabling its practical implementation. Simulation studies have shown that the proposed BM algorithm can significantly reduce the number of beam reselections or radio link failures compared to the baseline algorithm

Federated Learning-Based Interference Modeling for Vehicular Dynamic Spectrum Access

A platoon-based driving is a technology allowing vehicles to follow each other at close distances to, e.g., save fuel. However, it requires reliable wireless communications to adjust their speeds. Recent studies have shown that the frequency band dedicated for vehicle-to-vehicle communications can be too busy for intra-platoon communications. Thus it is reasonable to use additional spectrum resources, of low occupancy, i.e., secondary spectrum channels. The challenge is to model the interference in those channels to enable proper channel selection. In this paper, we propose a two-layered Radio Environment Map (REM) that aims at providing platoons with accurate location-dependent interference models by using the Federated Learning approach. Each platoon is equipped with a Local REM that is updated on the basis of raw interference samples and previous interference model stored in the Global REM. The model in global REM is obtained by merging models reported by platoons. The nodes exchange only parameters of interference models, reducing the required control channel capacity. Moreover, in the proposed architecture platoon can utilize Local REM to predict channel occupancy, even when the connection to the Global REM is temporarily unavailable. The proposed system is validated via computer simulations considering non-trivial interference patterns

DVB-T channels power measurements in indoor/outdoor cases

In this paper the analysis of the spectrum occupancy in the TV band is provided based on the indoor and outdoor measurements campaigns carried out in Poznan, Poland, and Barcelona, Spain, in 2013. The goal of this work is to discuss the stability and other important features of the observed spectrum occupancy in the context of indoor/outdoor Radio Environment Maps database deployment. Reliable deployment of these databases seems to be one of the critical points in practical utilization of the TV White Spaces for cognitive purposes inside buildings and in densely populated cities.Postprint (published version

Path Loss and Shadowing Modeling for Vehicle-to-Vehicle Communications in Terrestrial TV Band

Vehicle platooning is considered as one of the key use cases for vehicle-to-vehicle (V2V) communications. However, its benefits can be realized only with highly reliable wireless transmission. As the 5.9GHz frequency band used for V2V suffers from high congestion, in this paper, we consider the use of the terrestrial TV frequencies for intra-platoon communications. In order to be able to evaluate the potential of the new bands fully, propagation models for V2V communications at such frequencies are needed. Therefore, this paper reports new V2V propagation measurements and their modeling results. Particularly, we propose a Double Slope Double Shadowing model as the most accurate one, based on a comparison of various models using the Bayesian Information Criteria. We also investigate the space-time autocorrelation properties of the shadowing, which turned out to be dependent on the speed of vehicles. The proposed path loss and shadowing model differs from the ones proposed for the 5.9GHz band. Mostly, in favor of the TV band, as shown by, e.g., no statistically significant impact of a blocking car

A Systems Approach for Solving Inter-Policy Gaps in Dynamic Spectrum Access-Based Wireless Rural Broadband Networks

In this paper, we articulate the challenge of multiple intersecting policies for the realization of rural broadband networks employing dynamic spectrum access (DSA). Broadband connectivity has been identified as a critical component of economic development, especially during the COVID-19 pandemic, and rural communities have been significantly (and negatively) affected by the lack of this important resource. Although technologies exist that can deliver broadband connectivity, such as 4G LTE and 5G cellular networks, the challenges associated with efficiently deploying this infrastructure within a rural environment are multi-dimensional in terms of the different dependent policy decisions that need to be considered. To resolve this issue, we describe how systems engineering tools can be used for representing these intersecting policies such that system configurations can be optimized for efficient infrastructure deployment and operations. One technology requiring increased attention is DSA, where licensed and emerging wireless services can coexist together via spectrum sharing. However, implementation of this technology is challenging, where highly efficient Radio Access Technology (RAT), available spectrum, and user requirements need to be precisely aligned. All these elements to be configured are typically described by independent policies. While DSA is more complicated than previously used spectrum allocation schemes, inter-policy gaps occur that ultimately decrease the network\u27s efficiency. Consequently, a systems engineering framework has the potential to obtain the optimal solutions although the systems and wireless communities conceptualize and scope problems differently, which can impede collaboration. We present the use case where 4G LTE RAT technology employing DSA applied to digital terrestrial television (DTT) frequency bands can yield spectral efficiency loss when the different policy dimensions are not sufficiently accounted for within the use case. Computer simulations have shown that in an example rural scenario the availability of rural broadband can increase from 1% to 21% of locations if the inter-policy gaps are removed

Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online evolving Spiking Neural Network for Unsupervised Anomaly Detection algorithm (OeSNN-UAD), which, unlike OeSNN, works in an unsupervised way and does not separate output neurons into disjoint decision classes. OeSNN-UAD uses our proposed new two-step anomaly detection method. Also, we derive new theoretical properties of neuronal model and input layer encoding of OeSNN, which enable more effective and efficient detection of anomalies in our OeSNN-UAD approach. The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories. Our approach outperforms the other solutions provided in the literature in the case of data streams from the Numenta Anomaly Benchmark repository. Also, in the case of real data files of the Yahoo Anomaly Benchmark repository, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.P. Maciąg acknowledges financial Support of the Faculty of the Electronics and Information Technology of the Warsaw University of Technology, Poland (Grant No. II/2019/GD/1). J.L. Lobo and J. Del Ser would like to thank the Basque Government, Spain for their support through the ELKARTEK and EMAITEK funding programs. J. Del Ser also acknowledges funding support from the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education of the Basque Governmen