The paper focuses on the optimization of IEEE 802.11ax dense networks. The results were obtained with the use of the NS-3 simulator. Various network topologies were analyzed and compared. The advantage of using MSDU and MPDU aggregations in a dense network environment was shown. The process of improving the network performance for changes in the transmitter power value, CCA Threshold, and antenna gain was presented. The positive influence of BSS coloring mechanism on overal network efficiency was revealed. The influence of receiver sensitivity on network performance was determined.

The paper presents an analysis of BSS coloring scheme defined in IEEE 802.11ax standard. The efficiency of dense networks for different scenarios was analyzed and compared. This analysis covers various topologies and work configurations through the use of multiple parameters of the PHY and MAC layers. A positive impact of the coloring mechanism on the QoS was observed. The study also analyzed the impact of the RTS/CTS mechanism on the obtained network performance and adequate prioritization of various traffic classes. It was shown that the proper selection of the coloring mechanism parameters in the IEEE 802.11ax standard has a strong impact on QoS and the performance of dense networks.

Brain-computer interface (BCI) is a device which allows paralyzed people to navigate a robot, prosthesis or wheelchair using only their own brains reactions. By creating a direct communication pathway between the human brain and a machine, without muscles contractions or activity from within the peripheral nervous system, BCI makes mapping persons intentions onto directive signals possible. One of the most commonly utilized phenomena in BCI is steady-state visually evoked potentials (SSVEP). If subject focuses attention on the flashing stimulus (with specified frequency) presented on the computer screen, a signal of the same frequency will appear in his or hers visual cortex and from there it can be measured. When there is more than one stimulus on the screen (each flashing with a different frequency) then based on the outcomes of the signal analysis we can predict at which of these objects (e.g., rectangles) subject was/is looking at that particular moment. Proper preprocessing steps have taken place in order to obtain maximally accurate stimuli recognition (as the specific frequency). In the current article, we compared various preprocessing and processing methods for BCI purposes. Combinations of spatial and temporal filtration methods and the proceeding blind source separation (BSS) were evaluated in terms of the resulting decoding accuracy. Canonical-correlation analysis (CCA) to signals classification was used.