Spectrum Occupancy Detection Supported by Federated Learning

Dynamic spectrum access is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is effective spectrum occupancy detection. In many cases, machine learning algorithms improve detection effectiveness. Because of the recent trend of using federated learning, a federated learning algorithm is presented in the context of distributed spectrum occupancy detection. The results of the work presented in the paper are based on actual signal samples collected in the laboratory. The proposed algorithm is effective, especially in the context of a set of sensors with faulty sensors.

Utilization of Reconfigurable Intelligent Surfaces with Context Information: Use Cases

In terms of complex radio environments especially in dense urban areas, a very interesting topic is considered - the utilization of reconfigurable intelligent surfaces. Basically, based on simple controls of the angle of reflection of the signal from the surface, it is possible to achieve different effects in a radio communication system. Maximizing or minimizing the received power at specific locations near the reflecting surface is the most important effect. Thanks to this, it is possible to: receive a signal in a place where it was not possible, detect spectrum occupancy in a place where the sensor could not make a correct detection, or minimize interference in a specific receiver. In this paper, all three concepts are presented, and, using a simple ray tracing simulation, the potential profit in each scenario is shown. In addition, a scenario was analyzed in which several of the aforementioned situations are combined.

Wykorzystanie rekonfigurowalnych matryc antenowych wraz z informacją kontekstową

Reconfigurable intelligent surfaces can be successfully used to control the radio environment. Simple control of the reflection angle of the signal from the surface allows maximization or minimization of the received power in specific places. The paper presents simulations where it is possible to receive a signal in a place where it was not possible, to detect the occupancy of the spectrum in a place where the sensor was unable to make correct detection or to minimize interference in a specific receiver.

Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks

The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Open Radio Access Networks (O-RAN) architecture. In this article, we tackle the issue of dynamic resource allocation using a data-driven approach by employing Machine Learning (ML). We present an xApp-based implementation for the proposed ML algorithm. The core aim of this work is to optimise resource allocation and fulfil Service Level Specifications (SLS). This is accomplished by dynamically adjusting the allocation of Physical Resource Blocks (PRBs) based on traffic demand and Quality of Service (QoS) requirements. The proposed ML model effectively selects the best allocation policy for each base station and enhances the performance of scheduler functionality in O-RAN - Distributed Unit (O-DU). We show that an xApp implementing the Random Forest Classifier can yield high (85\%) performance accuracy for optimal policy selection. This can be attained using the O-RAN instance state input parameters over a short training duration.

Porównanie metod detekcji zajętości widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez węzła 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.