Spectrum occupancy detection is a key issue in enabling dynamic spectrum access. Nowadays, to improve detection effectiveness, the application of machine learning (ML) solutions is popular, and the issue of federated learning (FL) deserves special attention. A key challenge in spectrum occupancy detection is the limited access to labeled training data at the sensors. The sensor network studied in this work comprises a few sensors. These sensors have only partial access to labeled data during the ML model training process. The approach presented in this work uses the exchange of ML model coefficients to overcome the issue of limited training data at individual sensors. Both centralized and distributed FL variants were considered. The results of a hardware experiment involving the detection of the DVB-T signal by several sensors are discussed. In the presence of the sensor without access to labeled data probability of detection in the system increases from 52.06 % to 68.96 % in distributed variant, and to 78.87 % in centralized one. Moreover, the influence of neural network size on additional data transfer was analyzed due to the ML model coefficients exchange.

In recent years, Reconfigurable Intelligent Surfaces (RISs) have gained attention as a technology for manipulating wireless signal propagation in real-time. Beyond traditional communication enhancements, RISs offer new opportunities for precise user localization. This paper presents a modular, software-defined RIS steering system designed to dynamically configure reflection patterns for user position estimation. Using controlled RIS-induced variations in the wireless environment, the system enables fine-grained localization without requiring infrastructure. The proposed approach is validated through a series of localization experiments that demonstrate enhanced positioning accuracy by exploiting RIS-configured signal behavior.

In recent years, Reconfigurable Intelligent Surfaces (RISs) have emerged as a dynamic research area, enabling real-time control over signal propagation. However, deploying multiple RIS boards in practice requires fast and efficient remote steering of their parameters. This paper introduces a modular, software-defined RIS steering system that allows real-time adjustment of the reflection characteristics of deployed RISs. The system is validated through a spectrum sensing use case, demonstrating improved detection performance through signal enhancement.

The concept of open radio access networks (RAN) creates numerous opportunities for developing new technology and economy branches. At the same time, a flexible and modular approach in the disaggregated RAN entails the need for careful design of the overall RAN architecture and the implementation and deployment process of new applications. It is assumed that dedicated and specialized software companies may deliver the latter. A joint effort must be guaranteed among different sectors (industry, academia, and standardization bodies) to make the whole process efficient, safe, and reliable. Here, one of the critical driving forces origins from the open-source community that often stimulates the development of a specific technology. In this paper, we address the challenges that have to be faced by third-party application developers in the context of Open RAN. Based on many implemented applications (called xApps or rApps), we compare various available solutions. We pose the most critical issues that must be tackled in the near future to stimulate the progress in open RAN development further. In particular, we compare available open platforms for xApp development and testing. We present the details of implementing four selected applications describing the problems encountered. The paper is split into two logical parts - first, we identify the key ambiguities related to the development of new xApps, which address more complicated use cases like beam management. In the second part, we present the challenges associated with detailed software implementation in existing open platforms. In the first case, we show that dedicated beam mobility management xApp can reduce beam switches and keep beam failures low. However, it requires access to detailed localization information. Similarly, the signaling storm detection xApp provides expected performance under the assumption that there is access to detailed information on, e.g., time advance resolution parameter. We conclude here that several aspects still need to be well-defined to allow smooth software implementation; these include the rules for data reporting in time, parameters available in service models, and localization features. Concerning the second logical part, related to low-level implementation, we compare the numerical results of the traffic steering and quality-of-service-based resource allocation xApps and draw conclusions related to implementation and testing. In particular, we point out problems associated with the simulator, the software, and conflicts inside. Finally, we identify the key challenges which should be treated as incentives for joint academia-industry cooperation in the field of Open RAN. Thus, the paper presents the lesson learned during the first years of xApp development.

Dynamic spectrum access (DSA) has shown its benefits in various configurations of coexisting wireless networks. In this work, we discuss the ways for effective DSA between the terrestrial and non-terrestrial (satellite) networks. It is shown that the usage of historical statistical data leads to efficient resource sharing between these two kinds of networks at a limited interference cost.

In this paper, the power consumption model of a wireless transceiver is proposed. It is based on measurements of a number of off-the-shelf WiFi cards. The power consumption was divided into an idle, associated, and transmission states. Then, the eight-coefficients power consumption model depending on the rate and path-loss has been proposed and the values of coefficients have been determined. While there are significant differences in the power consumption among considered devices, the proposed model is well aligned with the measurements for each of them. Moreover, this paper shows that models based on a single device characterization can result in significant errors if employed to assess network power consumption.

This work presents the concept of a dense wireless network inspired by the human nervous system. To assess the impact of the proposed solutions, primarily on the network reliability and the mean time to failure, the percolation theory is used. The authors present simulation results for networks with random arrangement of network devices and additional devices inspired by microglia cell operation.

We discuss the idea of waveform flexibility and resource allocation in future wireless networks as a promising tool for network slicing implementation down to the lowest layers of the OSI (Open Systems Interconnection) models. In particular, we consider the possibility of cognitively adjusting the shape of the waveform to the requirements associated with various network slices. Moreover, such an adjustment of waveform shape is realised jointly with the selection and allocation of the appropriate frequency bands to each slice. In our approach, the definition of the waveform, as well as the assignment of resources, is done based on the information about the surrounding environment and each slice requirement stored in a dedicated context-information database. In this paper, we present the key concept of waveform flexibility for network slicing, the proposed algorithm for waveform selection and resource allocation among slices, and the achieved simulation results.

Dynamic spectrum access and management is one key enabler to constitute the foundation for a multi- service architecture with a high level of flexibility. In this work, we design and implement the spectrum management application (SMA) as an efficient tool to manage and process different policies and rules defined by various stakeholders such as national regulatory authorities, operators and licensed shared access. The SMA is an open- source and clean-slate replacement for legacy platform-dependent spectrum management solutions and can provide custom control programmability and agile resource utilization. We also elaborate on the design details of the SMA and show how it can dynamically select the optimal spectrum offers based on different applied rules in time-series. Finally, we demonstrate two specific use cases via integrating the implemented SMA prototype on top of the Mosaic5G and OpenAirInterface platforms.

This paper deals with the presentation of the idea of brain-inspired wireless communications in dense networks. It is built on two pillars; first, it draws on the current outcomes of the cognitive radio technology and the solutions considered in wireless sensor and ad-hoc networks, and second, it applies selected brain-functioning principles. In this paper we apply this concept to point-to-point communications, where the source node sends information to the destination node with the purpose of minimizing the overall energy consumption. To achieve this goal, we consider usage of neuron learning and astrocyte abilities to control neurons. These biological processes additionally introduce network self-repairing. However, the idea of brain-inspired networks can be extended to more advanced cases.

In this short paper we present the recent results on fault-tolerance in brain-inspired wireless communications, where the density of deployed transmission points is high. We follow the analogy to human brain and add dedicated functionality of microglia - a glial cell acting as the immune defense in the central nervous system.

Dynamic spectrum sharing can provide many benefits to wireless networks operators. However, its efficiency requires sophisticated control mechanisms. The more context information is used by it, the higher performance of networks is expected. A facility for collecting this information, processing it, and controlling base stations managed by various network operators is a so‐called Radio Environment Map (REM) subsystem. This paper proposes REM‐based schemes for the allocation of base stations power levels in 4G/5G networks, while considering interference generated to a licensed network. It is assumed that both networks have different profiles of served users, e.g., area of their positions and movement, which opens opportunities for spectrum sharing. The proposed schemes have been evaluated by means of extensive system‐level simulations and compared with two widely adopted policy‐based spectrum sharing reference schemes. Simulation results show that dynamic schemes utilizing rich context information outperforms static, policy‐based spectrum sharing schemes.