Por\'ownanie metod detekcji zaj\k{e}to\'sci widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez w\k{e}z{\l}a centralnego

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial intelligence are often and successfully used. To protect the privacy of user data and to reduce the amount of control data, an interesting approach is to use federated machine learning. This paper compares two approaches to system design using federated machine learning: with and without a central node.Comment: 4 pages, in Polish language, 3 figures, presented during conferenc

UNIFIED ANALYSIS OF TWO-HOP COOPERATIVE AMPLIFY-AND-FORWARD MULTI-RELAY NETWORKS

ABSTRACT This article develops an extremely simple and tight closed-form approximation for the moment generating function (MGF) of signal-to-noise ratio (SNR) for two-hop amplify-and-forward relayed paths over generalized fading environments. The resulting expression facilitates efficient analysis of twohop cooperative amplify-and-forward (CAF) multi-relay networks over a myriad of stochastic channel models (including mixed-fading scenarios where fading statistics of distinct links in the relayed path may be from different family of distributions). The efficacy of our proposed MGF expression for computing the average symbol error rate (ASER), outage probability, and the ergodic capacity (with limited channel side-information among cooperating nodes) is also studied. Numerical results indicate that the proposed MGF expression tightly approximates the exact MGF formulas and outperforms the existing MGF of lower and upper bounds of the half-harmonic mean (HM) SNR, while overcoming the difficulties associated in deriving a