Structural unit: S.M. Nikol’skii Mathematical Institute.
The Center conducts research aimed at improving and developing the network infrastructure. The research is carried out on real equipment, which makes it possible to more accurately assess the effectiveness and applicability of the results in real conditions.Resources
The laboratory base allows to carry out:
- Functional emulation of wireless communication networks on model equipment
- Radio frequency analysis of communication channels
- Modeling of applications at different levels of the protocol stack and the device interaction scenario
Equipment fleet
- Radio access network, which consists of software-configurable radio systems (allowing software measurement of the physical characteristics of the radio channel) SDR NI USRP 2954R. It is possible to deploy LTE, New Radio sub6, LoRa, NB-IoT, etc. networks on this network.
- Converged transport network, which is the link between the network core and the RAN radio access network. It is based on the software-configurable SDN network approach from B4N, which allows flexible and fast network management.
- The core of the network, which includes a virtualized platform organized in the form of a “sandbox”. It contains elements of the mobile and backbone network core. Solutions based on the OpenAirInterFace, srsLTE, AMAISOFRT, and openEPC software packages are used for this purpose.
The center team develops full-fledged and functional wireless networks of current generations. These networks cover all aspects, from the radio channel and terminal devices such as smartphones, to data processing and network management systems.
Prospective result
- The creation of algorithms for modeling and detecting signal blockages during data transmission over terahertz channels, which will ensure more accurate and efficient identification of possible obstacles and make 6G more reliable and stable. To do this, work is underway on a method for the preventive detection of signal collisions.
- The development of a model for the integration of edge computing systems (MEC) in the IMT-2030 communication network for various applications, as well as an algorithm for the placement of MEC, taking into account the parameters of quality of service (QoS), quality of perception (QoE) of services.
Partners
- St. Petersburg State University of Telecommunications named after M. A. Bonch-Bruevich
- Moscow Technical University of Communications and Informatics
- V. A. Trapeznikov Institute of Management Problems of the Russian Academy of Sciences
- National Research Tomsk State University
- National Research University “Higher School of Economics”
- A. A. Kharkevich Institute of Information Transmission Problems of the Russian Academy of Sciences
- Research of new technologies for wireless networks
- Creation of a test stand to verify theoretical results
- Development of models of communication channels in wireless networks
- Development of network resource management systems
A new intelligent network application management platform, REx (resource exchange), has been developed for smart application management, which will reduce the average application response delay and maintain high download speeds at minimal cost for carrier-grade equipment. This means that users will be able to watch the same broadcast in good quality, despite the increase in the number of views by reducing the load on the cloud service.
An emulator of wireless network channels has been developed, which allows operators to simulate the configuration of base stations in advance, taking into account the Doppler effect (when users are moving), which is especially important for highways or along railway tracks.
Two inventions related to the network interaction of wireless devices and the distribution of virtual resources of telecom operators have been patented:
- “A method of network interaction between a wireless device and a remote application server”
- “A method for distributing virtual resources of a telecom operator”.
Two inventions optimize the resources of telecom network operators. This allows to reduce the cost of maintaining transport networks, improve the quality of service provision for user applications, and increase the energy efficiency of sensor networks by reducing the consumption of nodes in this network.