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.

This paper investigates the influence of employing diverse operating algorithms in Base Stations (BSs) to distribute network traffic. The study focuses on a wireless system where half of the BSs are powered by Renewable Energy Sources (RES) such as Photovoltaic Panels (PVPs) and Wind Turbines (WTs). The aim is to diminish the energy demand from conventional infrastructure reliant on Conventional Energy Sources (CES) like fossil fuels. The research evaluates the Energy Efficiency (EE) factors of the entire wireless system using two User Association (UA) approaches. The first approach, the RES-aware Association Algorithm (RAA), prioritizes connecting mobile users togreenBSs. The second approach, the SNR-based (SNR – Signal-to-Noise Ratio) Association Algorithm (SAA), assigns users to BSs based on radio signal distribution. Results indicate a reduction in conventional energy usage and renewable energy loss, ultimately enhancing the EE of the examined Radio Access Network (RAN).

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 this paper, we examine the distribution of radio signal propagation within the city of Poznan (Poland) to determine optimal locations for deploying Reconfigurable Intelligent Surfaces (RIS). The study focuses on designing a 5G/6G Radio Access Network (RAN), incorporating eight Base Stations (BSs) that utilize either Single Input Single Output (SISO), or Multiple Input Multiple Output (MIMO) antenna technologies, depending on the network cell configuration. Through detailed simulations and analyses, we explore various propagation scenarios in both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, considering the complex urban landscape characterized by high-rise buildings. The results demonstrate the potential of using RISs in mobile networks to enhance radio signal quality in urban environments through strategic placements. Our findings suggest that RISs can significantly mitigate Path Loss (PL) and improve signal coverage in challenging urban environments, particularly in areas where traditional base station deployment alone would be insufficient. Furthermore, the study highlights the role of RISs in reducing the need for additional base stations, thereby optimizing network costs and infrastructure while maintaining high-quality service delivery. The insights gained from this research provide valuable guidelines for network planners and engineers seeking to implement RIS technology in future 5G and beyond networks, ensuring more efficient and robust urban communication systems.

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.

Reconfigurable Intelligent Surfaces (RIS) have gained significant attention for some time. Thanks to the possibility of individual steering of each re- flecting element of the boards, they are envisaged to impact the propagation environment significantly. In this work, we concentrate on the practical verification of this concept. We present the results of detailed measurements of the reflection characteristics of the RIS boards, which have been conducted intentionally in the real environment. Various potential impacting factors have been considered (impact of azimuth and elevation angle, polarization, number of RIS boards, and distance). Achieved measurement results consti- tuted the basis for conceptual analysis on the practical possibility of creating a codebook (consisting of RIS patterns - codewords) for some applications.

In this paper, we conduct an in-depth analysis of radio signal propagation characteristics within the urban environment of Poznan (Poland). The study specifically addresses the deployment of a 5th generation (5G NR - New Radio) Radio Access Network (RAN), which comprises 8 strategically positioned Base Stations (BSs). These base stations are configured with either Single Input Single Output (SISO) or Multiple Input Multiple Output (MIMO) antenna technologies, contingent upon the specific requirements of the network cells they serve. A key focus of our research is the integration of 15 reflecting arrays, known as Reconfigurable Intelligent Surfaces (RISs), which were installed throughout the study area. These RISs were deployed at various suspension heights to evaluate their impact on radio signal propagation and coverage. By exploring the influence of these RIS matrices, our research sheds light on their potential to significantly enhance signal quality, particularly in urban environments.

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.

In this work, the impact of using Renewable Energy Source (RES) generators in next-generation (5G) cellular systems on total power consumption (PC) has been investigated. The paper highlights the gain related to the use of photovoltaic (PV) panels and wind turbines (WTs) in the form of two factors - the average extension of battery lifetime (AEBL) powering a single network cell and the average reduction in energy consumption (AREC) within the whole network. The examination has been conducted for four different seasons of the year and various configurations of available power sources. Provided system scenario was based on real data on weather conditions, buildings placement, and implemented mobile networks for the city of Poznan in Poland. Used RES generators were designed in accordance with the specifications of real devices.

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, we concentrate on modeling and simulation aspects of the Open Radio Access Networks (O-RAN). In detail, we present the solution in the form of an xApp considering the Quality-of-Service (QoS) Based Resource Optimization Use Case (QBRO-UC) within the O-RAN system. The xApp has been developed for the μONOS RAN Intelligent Controller (RIC) contained in the SD-RAN software platform designed by the Open Networking Foundation (ONF). The paper focuses on the mechanisms of Radio Resource Management (RRM) controlled by O-RAN-compliant policies. The paper highlights the capabilities of the proposed Physical Resource Block (PRB) allocation and discusses the limitations of the whole modeling framework.

The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.

We formulate a drone base stations (DBSs) localization problem that improves the users’ received signal-to-interference-plus-noise ratio (SINR) and the fairness between users in terms of receiving sufficient channel quality. In contrast to other works, our algorithm adapts to the actual users distribution on the ground without knowing their locations but rather their channel measurements history. We also leverage the fact that moving a DBS results in reduced aerodynamic energy consumption and illustrate that moving DBSs intelligently at a certain speed actually reduces energy consumption. We propose a multi-agent Q-learning formulation to solve this problem which requires less computation than its single agent counterpart and show by extensive simulations the improvements in terms of system fairness and link reliability relative to the benchmark solutions while leveraging a realistic users mobility model.

Platoon-based driving is an idea that vehicles follow each other at a close distance, in order to increase road throughput and fuel savings. This requires reliable wireless communications to adjust the speeds of vehicles. Although there is a dedicated frequency band for vehicle-to-vehicle (V2V) communications, studies have shown that it is too congested to provide reliable transmission for the platoons. Additional spectrum resources, i.e., secondary spectrum channels, can be utilized when these are not occupied by other users. Characteristics of interference in these channels are usually location-dependent and can be stored in the so-called Radio Environment Maps (REMs). This paper aims to design REM, in order to support the selection of secondary spectrum channel for intra-platoon communications. We propose to assess the channel’s quality in terms of outage probability computed, with the use of estimated interference distributions stored in REM. A frequency selection algorithm that minimizes the number of channel switches along the planned platoon route is proposed. Additionally, the REM creation procedure is shown that reduces the number of database entries using (Density-Based Spatial Clustering of Applications with Noise) DBSCAN algorithm. The proposals are tested using real IQ samples captured on a real road. Application of the DBSCAN clustering to the constructed REM provided 7% reduction in its size. Utilization of the proposed channel selection algorithm resulted in a 35 times reduction of channel switches concerning channel assignment performed independently in every location.

In this paper, we consider the use of radio environment maps (REMs) in vehicular dynamic spectrum access (VDSA) for vehicle platooning applications. We propose an algorithm that dynamically allocates the frequency bands and transmission power in the so-called TV white spaces (TVWS) for intra-platoon messaging, intending to maximize the reliability of the communications, simultaneously keeping the interference to the primary system below the required threshold. The proposed solution is evaluated in simulations, with the results indicating a significant increase in communications reliability with VDSA.

Heterogeneous radio access networks require efficient traffic steering methods to reach near-optimal results in order to maximize network capacity. This paper aims to propose a novel traffic steering algorithm for usage in HetNets, which utilizes a reinforcement learning algorithm in combination with an artificial neural network to maximize total user satisfaction in the simulated cellular network. The novel algorithm was compared with two reference algorithms using network simulation results. The results prove that the novel algorithm provides noticeably better efficiency in comparison with reference algorithms, especially in terms of the number of served users with limited frequency resources of the radio access network.

The information and communication technology, ICT, a domain of the global economy, consumes more and more energy, what is the effect of continuously increasing wireless data traffic. Thus, there is a need for more energy-efficient and sustainable network and resource management, and such a goal can be achieved by the utilization of renewable energy sources. This paper proposes a novel user allocation scheme applicable to the heterogeneous radio access networks supplied with renewable energy sources. Presented results proved that high-energy efficiency can be accomplished by reallocating some traffic to the green wireless base station.

In this paper, we investigate the concept of database supported Vehicular Dynamic Spectrum Access (VDSA) for platooning. As various researchers show that the 5.9 GHz band, devoted for Intelligent Transportation Systems, may suffer from congestion of the channel, we propose to offload part of this traffic to white-spaces with the guidance of the active database system. In our work, we describe our measurement campaign which delivered data for population of the dedicated radio environment map. Once the map is created, it was used in three proposed algorithms for VDSA: an optimal and two pragmatic approaches.

In this paper, we propose a distributed Vehicular Dynamic Spectrum Access (VDSA) framework for vehicles operating in platoon formations. Given the potential for significant congestion in licensed frequency bands for vehicular applications such as 5.9 GHz. Our approach proposes to offload part of the intra-platoon data traffic to spectral white-spaces in order to enhance vehicular connectivity in support of on-road operations. To enable VDSA, a Bumblebee-based decision making process is employed which is based on the behavioral models of animals, is employed to provide a means of distributed transmission band selection. Simulation results show the distributed VDSA framework improves the leader packets reception ratio by 5%, thus indicating its potential to increase in reliability of intra-platoon communications.

The introduction of large antenna arrays facilitating massive multiple-input-multiple output (M-MIMO), and the addition of a tier of pico and femto base stations (BS), implementing an heterogeneous network, provides means to improve network throughput and capacity in 5G networks. However, the addition of antennas and BSs implies additional hardware and is associated with higher energy consumption. Improving the energy efficiency (EE) while reducing the power consumption of such heterogeneous M-MIMO dense networks can be performed by switching off base stations that have few users to serve and redistribute those users among the active neighboring base stations. One promising solution to intelligently map user spatial distribution to the optimal set of active BSs is by utilizing radio service maps (RSM). In this paper we propose a novel approach that effectively switches off base stations by combining reinforcement learning with RSM data. The proposed approach is evaluated through computer simulations using a 3D ray tracing model. The simulation results show the benefits of RSM and machine learning use for the improvements in EE of the considered heterogeneous M-MIMO networks.

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.

In this paper, we deal with the small-scale spectrum aggregation, where a set of even very narrow and disjoint frequency bands closely located on the frequency axis can be utilized simultaneously. Contrarily to our previous work on that subject, we concentrate on the multiuser scenario in which the 5G system tries to share the spectrum interleaved by multiple narrowband signals realized in the protected cellular systems, e.g., 2G/3G systems. We first present the idea of multiuser small scale sharing based on non-contiguous multicarrier (NC-MC) techniques. In our optimization problem we rely on the detailed interference model that takes into account limitations of both transmitter and receiver frequency selectivity, and which has been practically validated. Presented results confirm that proposed solution can provide high throughput even when the 5G system operates in a dense heterogeneous network.

The article presents an analysis of correlation-based clustering procedure in cooperative spectrum sensing. The motivation is to assess if it is more beneficial in terms of energy efficiency to group nodes according to the received signal-to-noise ratio (SNR) on the link between Primary User and node or according to the distance between nodes. To this end, a merged clustering measure is introduced. Then a number of network topologies are compared via computer simulations.

This paper evaluates the capacity of weighted circularly convolved FBMC/OQAM systems. A rigorous mathematical model is derived to calculate the increase of capacity that can be obtained thanks to the lattice structure of the modulation and the exploitation of the intrinsic interference. The numerical results reveal that a signal to noise ratio gain of 2dB is obtained in one resource block of 12 subcarriers, translating into a capacity increase of 11% with respect to OFDM, which nuances some previous results on this topic in the literature.

New spectrum bands together with flexible spectrum management are treated as one of the key technical enablers for achievement of the so-called key-performance indicators defined for 5G wireless networks. In this paper, we deal with the small-scale spectrum aggregation and sharing, where a set of even very narrow and disjoint frequency bands closely located on the frequency axis can be utilized simultaneously. We first discuss how such a scheme can be applied to various multicarrier systems, focusing on the non-contiguous orthogonal frequency division multiplexing and non-contiguous filter-bank multicarrier technique. We propose an interference model that takes into account the limitations of both transmitter and receiver frequency selectivity, and apply it to our 5G link-optimization framework, what differentiates our work from other standard approaches to link adaptation. We present the results of hardware experiments to validate assumed theoretical interference models. Finally, we solve the optimization problem subject to the constraints of maximum interference induced to the protected legacy systems (GSM and UMTS). Results confirm that small-scale spectrum aggregation can provide high throughput even when the 5G system operates in a dense heterogeneous network.

Spectrum sensing is a fundamental problem in cognitive radio systems. Its main objective is to reliably detect signals from licensed primary users to avoid harmful interference. As a first step toward building a large-scale cognitive radio network testbed, we propose to investigate experimentally the performance of three blind spectrum sensing algorithms. Using random matrix theory to the covariance matrix of signals received at the secondary users, the first two sensing algorithms base their decision statistics on the maximum to minimum eigenvalue ratio and the sum of the eigenvalues to minimum eigenvalue ratio, respectively. However, the third algorithm is based on cyclostationary feature detection and it uses the symmetry property of cyclic autocorrelation function as a decision policy. These spectrum sensing algorithms are blind in the sense that no knowledge of the received signals is available. Moreover, they are robust against noise uncertainty. In this paper, we implement spectrum sensing in real environment and the performance of these three algorithms is conducted using the GNU-Radio framework and the universal software radio peripheral (USRP) platforms. The results of the evaluation reveal that cyclostationary feature detector is effective in finite sample-size settings, and the gain in terms of the SNR with respect to eigenvalues-based detectors to achieve Pfa (probability of false alarm) = 0.08 is at least 4 dB.

The paper tackles the problem of efficient reduction of the out-of-band (OOB) radiation in multi-carrier systems when considering the presence of a nonlinear power amplification. It is important to provide “green” transmitters producing less electromagnetic “pollution” out of its nominal frequency band and obtaining at the same time high energy efficiency of the used power amplifier. One of the known methods assumes appropriate modulation of dedicated subcarriers by specially calculated symbols reducing OOB radiation. The solution proposed in this paper combines two approaches, among which the first, called cancellation carriers, is used traditionally for subcarrier sidelobes minimization, and the second, called tone reservation, for peak to average power ratio reduction. The novelty of this work is in joint minimization of both aforementioned metrics in the near optimal way while keeping low computational complexity. The result is seen as a decreased power of the OOB radiation observed after signal passing through the power amplifier. The obtained results confirm that the proposed algorithm outperforms the approaches in which those metrics are minimized separately. Additionally, a number of simulations has been carried out to investigate the influence of the proposed algorithm parameters on the resultant out-of-band radiation performance.

In this paper we consider the coexistence of a single-input single-output (SISO) primary link with a multiple-input single-output (MISO) secondary user pair that has non-causal knowledge of the primary message. We study an achievable rate region that exploits this knowledge by combining selfless relaying to maintain the rate supported by the primary link with dirty paper coding to pre-cancel the interference at the secondary receiver. We find the optimal choice of power allocation between these operating modes at the secondary transmitter as well as the optimal beamforming vectors. Moreover, we address the robustness of the solution to uncertainties in the channel knowledge. Finally, we show by numerical evaluation the gains obtained due to the additional knowledge of the primary message.

Bit-Power Loading (BPL) algorithms are proposed based on Effective SINR Mapping (ESM) techniques. The ESM techniques used are the Exponential ESM (EESM) as well as the Mean Mutual Information per Bit (MMIB). The algorithms are based on proper usage and modifications of algorithms already proposed in the literature, applied to ESM compatible link-level performance models. The performance gain when using the proposed algorithms is assessed for an IEEE 802.16 OFDM system.

In this paper, a novel pragmatic bit and power allocation algorithm for Non Orthogonal Frequency Division Multiplexing (NOFDM) systems is presented. It is based on the well-known Campello approach and takes into account the lack of orthogonality between adjacent waveforms. Simulation results in a typical NOFDM wireless applicative scenario show the effectiveness of the proposed algorithms.