Cognitive radio

An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

This paper explores how the vital function of spectrum sensing should be executed in cognitive radio networks. The cognitive receiver is required to be able to evaluate the spectral environment properly so that it may not create additional interference to the primary users. The sensing accuracy and time are conflicting parameters, therefore, a trade-off is necessary for an optimal efficiency. We propose a dual-approach solution. The decision about the spectrum occupancy is made using the measured signal-to-noise ratio (SNR) and the received signal levels as inputs in a fuzzy logic algorithm. The result is then compared with the one acquired using the statistical method. Finally, an optimal balance between the sensing time and accuracy is obtained for the current environmental conditions using the derived closed form expression. The algorithm has been implemented using the USRP platform and GNU Radio. The analysis of the results shows the efficiency of our proposal.

Cognitive radio is one of the most promising techniques of wireless communications, due to its many applications. Cognitive networks have the capability to congregate different cognitive users via cooperative spectrum sensing. Examples of cognitive networks can be found in important and different applications, such as digital television and wireless sensor networks. The objective of this paper is to analyze how signal processing techniques are used to provide reliable performance in such networks. Applications of signal processing in cognitive networks are presented and detailed.

Cognitive Radio (CR) is extending the applications of wireless communications worldwide. Cognitive radio verifies the electromagnetic spectrum availability and permits the modification of the transmission parameters using the interaction with the environment. The goal is to opportunistically occupy spectral bands with minimum interference to other users or applications. Cognitive radio for Vehicular Ad hoc Networks (CRVs or CR-VANETs) is a new trend in the automotive market. Recent and future vehicles will offer functionalities for the transmission of intra-vehicular commands and dynamic access to wireless services, while the car is in transit. This paper describes the cognitive radio technology and its signal processing perspectives for the automotive market.

The Internet of Things (IoT) is a fundamental technology for emerging 5G networks, enabling data exchange among interconnected devices. However, the rapid expansion of IoT-based smart cities increases data traffic and collision probability. This paper presents a novel adaptive IEEE 802.11ah MAC protocol that integrates artificial intelligence (AI) to mitigate data collisions. Two mathematical models are derived to analyze collision probability in the Restricted Access Window (RAW) and Periodic RAW (PRAW) access mechanisms. Moreover, six machine learning (ML) algorithms are applied to classify IoT traffic patterns and map them to appropriate access channels. The proposed protocol achieves an 80-90% reduction in collision probability, with the decision tree (DT) algorithm attaining 99.45% testing accuracy and 1.066 s processing time.

Efficient spectrum sensing in Cognitive Radio Networks (CRNs) is challenged by sparse spectrum recovery under dynamic and interference-prone conditions. This paper presents an enhanced framework for Cooperative Compressed Sensing (CCS), motivated by the need to improve detection probability while reducing false alarms and reconstruction error in realworld deployments. We evaluate conventional fusion strategies-arithmetic mean, geometric mean, weighted averaging, and voting-and introduce two novel methods designed to overcome their limitations. The first method employs blind Signal-to-Noise Ratio (SNR) estimation via normalized correlation between geometric and arithmetic mean spectra to guide adaptive weighting. The second introduces a subband-aware overlapping fusion strategy, selectively combining high-detection subbands to suppress false alarms while preserving spectral activity. Experimental validation is conducted using a real-time Software-Defined Radio (SDR) testbed with multiple USRP receivers and a central fusion center. Results show that the correlation-based fusion achieves near-optimal detection probability with a low false alarm rate and minimal RMSE, while the overlapping-band method further improves spectral precision. Both methods consistently outperform conventional rules across network sizes and SNR regimes. In addition, a complexity analysis confirms their computational feasibility. The findings demonstrate the motivation and effectiveness of adaptive, correlation-driven CCS for dynamic spectrum access, laying the groundwork for future integration with machine learning-based reconstruction in 5G/6G systems.

Cognitive Radio Networks (CRN) are using Compressive Spectrum Sensing (CSS) for Dynamic Spectrum Access (DSA) via secondary users (SUs). Contributions are in one of the following fields: Sparsity, Measurements, Reconstruction, and Detection. Many reconstruction techniques exist. In the present work, we focus on the reconstruction process made by Linear programming and greedy algorithms. After the comparison, we performed a detection on the recovered spectrum to classify the sub-bands occupancy. Comparisons and performance analysis are made in terms of detection probability (P d), false alarm rate (P f a), mean square error (MSE). Receiver Operating Characteristics (ROC) curves also are investigated. Convex optimization ensures accurate recovery for fewer measurements at the expense of time, while greedy algorithms are faster but accurate for higher measurements number. The greedy reconstruction achieves the same P d as for the original spectrum for both algorithms Fast matching Pursuit (FMP) and Orthogonal Matching Pursuit (OMP). P f a has been improved (from 0.8% to less than 0.2% for FMP and 0 for OMP) due to the non-estimation of smaller amplitudes in the reconstruction process in addition to the noise randomness weakened by averaging. The most important achievement with this configuration in the spectral efficiency improvement (5 times) leading to energy, memory, hardware and frequency band saving.

This paper studies the role of ex-ante information asymmetries in second price, common value auctions. Motivated by information structures that arise commonly in applications such as online advertising, we seek to understand what types of information asymmetries lead to substantial reductions in revenue for the auctioneer. One application of our results concerns online advertising auctions in the presence of "cookies," which allow individual advertisers to recognize advertising opportunities for users who, for example, are customers of their websites. Cookies create substantial information asymmetries both ex ante and at the interim stage, when advertisers form their beliefs. The paper proceeds by first introducing a new refinement, which we call "tremble robust equilibrium" (TRE), which overcomes the problem of multiplicity of equilibria in many domains of interest. Second, we consider a special information structure, where only one bidder has access to superior information, and show that the seller's revenue in the unique TRE is equal to the expected value of the object conditional on the lowest possible signal, no matter how unlikely it is that this signal is realized. In the third part of the paper, we study the case where multiple bidders may be informed, providing additional characterizations of the impact of information structure on revenue. Finally, we consider revenue maximization in a richer setting with a private component to valuations.

Cognitive radio (CR) enables dynamic spectrum access, addressing spectrum scarcity. When integrated with wireless sensor networks (WSNs), the resulting system is known as a wireless cognitive radio sensor network (WCRSN). This study investigates the energy-efficiency and throughput of WCRSNs under sensing (S) channels affected by noise and Log-normal shadowing (LNS) fading. Each cognitive radio sensor (CRS) receives an unknown licensed signal from the primary user (PU) and uses energy detection (ED) to make a local one-bit binary decision. These decisions are sent via control (C) channels to a control center (CC), where a hard-decision combining technique (HDCT) determines the PU's activity state. A novel mathematical expression for detection probability is derived, accounting for noise and LNS fading. This expression is validated through Monte Carlo Simulations. Analytical models are then developed to evaluate energy-efficiency and throughput under varying network and channel conditions. The effects of erroneous S and Cchannels (with channel error probability q) are analyzed. Key parameters such as number of CRSs (K), detection threshold (λ), average SNR (γ), and LNS fading parameter (σ) are examined. The study achieves up to 92.54% energy-efficiency and optimized throughput across various scenarios.

The main objective of the cognitive radio sensor network (CRSN) is to minimize the energy consumption in channel sensing and switching when sensor nodes sense and switch to a licensed channel for improving the energy efficiency for 6G applications. The drawback in CRSN is that the sensor network

The global shift towards sustainable energy solutions has brought smart grid technology to the forefront of modern electrical engineering. Smart grids represent a revolutionary advancement in power distribution, integrating digital communication and advanced monitoring systems to improve the efficiency, reliability, and flexibility of electricity networks. Traditional power grids, characterized by one-way energy flow, are being
replaced by smart grids that enable two-way communication between consumers and utility providers, thereby optimizing energy distribution and consumption. This transition is driven by the increasing demand for
renewable energy sources like solar and wind, which require dynamic grid management to handle their intermittent nature. Smart grids enhance energy efficiency by utilizing advanced metering infrastructure (AMI)
to provide real-time data on energy usage, allowing for better load management and reduced energy wastage. Furthermore, these grids incorporate distributed energy resources (DERs), such as residential solar panels and electric vehicle (EV) charging stations, into the broader grid network, facilitating decentralized energy generation. With the rise of electric vehicles and renewable energy integration, smart grids play a pivotal role in minimizing power outages, improving energy security, and reducing greenhouse gas emissions. Despite their potential, smart grids face challenges, including cybersecurity risks and the high costs of infrastructure upgrades. However, ongoing research and development aim to address these issues and further enhance grid functionality. This article delves into the technological advancements of smart grids, their benefits, and the obstacles that must be overcome for their widespread adoption in the power sector.

One promising approach to achieving high precision spectrum sensing in a cognitive radio (CR) system is the energy detector based on Wavelet Packet Transform (WPT). However, energy detector spectrum sensing is affected by the Signal to Noise Ratio (SNR). When the SNR is lower than 5 dB, an energy detector may falsely determine some unoccupied sub-channels as occupied . In this paper, a new noise immune algorithm for spectrum sensing is introduced. This algorithm combines two powerful tools: the wavelet packet analysis and Higher-Order-Statistics (HOS). The use of the proposed technique makes spectrum sensing possible in very low SNR conditions. This allows better utilization of the unoccupied spectrum and high spectrum efficiency usage.

This paper is concerned with an optimal design of the precoders and receive filters for cognitive radio (CR) networks in which multiple secondary users (SUs) share the same frequency band with multiple primary users (PUs). To cope with interference and to achieve fairness among users, we develop an interference alignment (IA) scheme by minimizing the maximum mean squared error (Min-Max MSE) of the received signals. Since the Min-Max MSE design problems are nonconvex in the design matrix variables of the precoders and receive filters, we develop an alternating optimization algorithm with provable convergence to iteratively find the optimal solutions. In each iteration, the precoder design problems can be recast as second order cone program (SOCP) while the optimal receive filters can be derived in closed-form solutions. Finally, numerical results are provided to demonstrate the superiority of the proposed method as compared to previous work in terms of the information rate and bit error rate.

Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capacities of the cognitive radio, will provide an ultimate spectrumaware communication paradigm in wireless communications. Specifically, in cognitive radio ad hoc networks (CRAHNs), the distributed multihop architecture, the dynamic network topology, and the time and location varying spectrum availability are some of the key distinguishing factors. In this paper, intrinsic properties and current research challenges of the CRAHNs are presented. A particular emphasis is given to distributed coordination between CR users through the establishment of a common control channel. Lastly, a new commission called the park model is explained, where CRAHN users may independently determine their own performance based on pre-decided spectrum. The performance is comparable to MANET routing protocols In this system implementation through real time systems with Specialized ANDROID BASED OPERATING SYSTEMS.

We outline an innovative approach to the development of a prototype of a neighborhood notification system (NNS). The NNS application residing on smart phones will use software defined radio and cognitive radio components to interface with radio frequency transceivers. Mesh networking is proposed for emergency notification and disaster response coordination using NNS. Our focus has been on the IEEE 802.15.4 and the very recent IEEE 802.15.5 mesh networking standard for low data rate connectivity among low power nodes (or nodes whose power consumption needs to be low). The innovation stems from bringing together different hardware and software componentssome of which, like our Software Defined Radio (SDR) platform, are themselves still evolving and others, like the meshing platform, are very newto propose an adaptive, reconfigurable, infrastructure-less ad hoc wireless solution to emergency communications in the unlicensed ISM RF band.

Machine to Machine (M2M) communications have been recently introduced as a viable paradigm for allowing low cost and efficient communications among devices mainly in an autonomous manner. Even if M2M protocols need dedicated resources, a new paradigm, called Cognitive M2M (CM2M) communications, has been recently considered exploiting cognitive/opportunistic radio communications. After having introduced the problem of applying cognitive techniques in M2M scenarios, the authors focus their attention on the Medium Access Control (MAC) protocols for CM2M scenarios, with a particular attention on the OFDMA-based primary systems. Among other approaches, the authors focus on a data-aided approach for the access of the secondary devices aiming to reduce interference toward the primary system.

André Oliveira Tekever, Portugal, [email protected]
 Pedro Sinogas Tekever, Portugal, [email protected] Stefan Taing Munich Innovation Group, Germany, [email protected] Jens Elsner Munich Innovation Group, Germany, [email protected] Simon Watts Avanti Communications Group plc, United Kingdom, [email protected] Valentine Boissinot Avanti Communications Group plc, United Kingdom, [email protected] Henrique M. Salgado INESC TEC and Faculty of Engineering, University of Porto, Portugal, [email protected] João Canas Ferreira INESC TEC and Faculty of Engineering, University of Porto, Portugal, [email protected] Luis Pessoa INESC TEC, Portugal, [email protected] José Machado da Silva INESC TEC and Faculty of Engineering, University of Porto, Portugal, [email protected]

The massification of mobile access and services has increased the demand for faster, reliable and ubiquitous networks, which has been leading to additional pressure on satellite service providers to provide larger throughput. This inherently raises the challenge of bandwidth management. Regulatory activities have led to frequency allocation charts that are growing more complex and harder to manage. Such problem needs therefore to be addressed in order to achieve a more efficient use of resources and cope with the escalating traffic in satellite communications in the sub 5GHz bands. The H2020 SCREEN project is addressing this challenge by resorting to cognitive radio (CR) technology at S band. SCREEN is working towards maturing several CR enabling technologies up to TRL4/5, considering two reference scenarios: Satcom-enabled UAV constellations and Inter-Satellite Links for satellite networks. This paper focuses on the design, development, simulation and implementation of the proposed...

U svakodnevnoj upotrebi, informacije se mogu definisati kao podaci koji proširuju naše razumevanje stvarnosti a mere se stepenom do kojeg pomažu primaocu da ostvari svoje praktične ciljeve. Dobre in formacije su praktično korisne, loše su beskorisne. Na tom mestu uobičajenom shvatanju kvantitativnih informacija, pridružuju se semantički, prostorni i vremenski faktori. Ali pitanje je kako proste informacije, koje dolaze iz okruženja, postaju nosioci tih svojstava?
Fokus ovog rada prvenstveno je usmeren na pitanja u vezi sa nastankom svojstava perspektivnog vremena (tensed time) koje se, za razliku od globalnog vremena fizike (detensed time), izražava u terminima prošlosti, sadašnjosti i budućnosti. Moj argument temelji se na artikulisanju perspektivnog vremena kao posledice manipulisanja lokalnim informativnim obrascima.
Nakon uvoda u razliku između kvantitativnog (Šenonovog) i semantičkog (Floridijevog) koncepta informacija, pokušaću ću da pokažem sledeće: (1) Semantički sadržaj nije van dosega kvantitativnog razumevanja informacija, već se može izvesti iz njega putem prepoznavanja obrazaca i redukcije redundanse; (2) kvantitativni koncept
informacija dovoljan je da objasni perspektivno vreme kao emergentni aspekt lokalnog informativnog sistema čvrsto povezanog sa novogenerisanim semantičkim sadržajem; (3) perspektivno vreme je redefinisano kao lokalno i semantičko, sa implikacijama na metafiziku perspektivnog vremena a potencijalno i na informativne sisteme.

Traditionally, the frequency spectrum is licensed to users by government agencies in a fixed manner where the licensee has exclusive right to access the allocated band. However, with increasing demand for the spectrum and scarcity of vacant bands, a spectrum policy reform seems inevitable. Meanwhile, recent measurements suggest the possibility of sharing spectrum among different parties subject to interference-protection constraints. In order to enable access to an unused licensed spectrum, a secondary user has to monitor licensed bands and opportunistically transmit whenever no primary signal is detected. Spectrum-sharing between a primary licensee and a group of secondary users has been studied. The structure of an asymptotically optimum detector based on the measurements of all secondary users is derived and the effect of the quantisation error in such a system is evaluated. Also, it is shown that by using the proposed detector in a sequential detection structure, it is possible to shorten the decision time needed by the detector. The results show the superiority of the proposed detector to other schemes.

Motivated by the deployment of hybrid cooperative cognitive radio (HCCR) based relays in cellular environments, this paper proposes the hybrid cognitive Gaussian two-way relay channel (HCGTWRC) model. Recently, the combination of licensed and cognitive radios with cooperative systems has gained popularity. These combined techniques have the potential to outperform primary network and pure cognitive network by utilizing the complementary nature of licensed and cognitive radios. In the proposed HCGTWRC model, a relay jointly utilizes both the licensed as well as the unlicensed/cognitive radio resources (RRs). Unlike conventional two-way relay channel, in this proposed model, source uses licensed resources for transmission and relay forwards this information using cognitive RRs. These licensed and unlicensed resources are not affected by total resource constraints of source and relay. Here, unlicensed RRs are not only described by bandwidth and power but also by reliability/availability parameter because of its opportunistic nature. In this paper, the proposed HCGTWRC model is studied by taking information theoretic aspect. In this respect, firstly, upper and lower bounds of performance metrics such as capacity, spectral efficiency (SE), and energy efficiency (EE) are analytically derived for the proposed HCGTWRC model. Then the optimal resource (such as power and bandwidth) allocation is derived with respect to upper and lower bounds of each performance metrics. Further, the performance comparison of the proposed HCGTWRC model is presented with the existing one. Numerical results are also presented for the optimal resource allocation to validate the analytical ones.

In 5th Generation (5G) systems, the current Machine-to-Machine (M2M) communications using Wi-Fi or Bluetooth provide a good opportunity to dramatically increase the overall performance. Converged mobile networks can provide M2M communications with significant performance improvement from sharing unlicensed spectrum bands in cellular networks, such as Long Term Evolution-Advanced (LTE-A), by using cognitive radio technology. Thus, the converged mobile network will become one of the most popular future research topics because mobile multimedia content services have been generally accepted among mobile device users. In this article, we provide an overview of converged mobile networks, investigating different types of converged mobile network, different types of convergence, and the current problems and solutions. This survey article also proposes potential research topics in converged mobile networks.

Dynamic Spectrum Allocation (DSA) is one of the latest technologies serving to cater to rising demand of spectra. Since a decade, cognitive radios (CR) are looked upon as a solution to increase spectral efficiency. However, to make CR a reality, development of air interface is a big challenge. In this paper, we have proposed a CR architecture followed by its equivalent mathematical model. We consider a centralized network where the master/controller of this ad-hoc network coordinates for spectrum allocation with the surrounding CR in the network. This CR ad-hoc network is assumed to coexist with the network of licensed users where the controller of licensed users is updated with CR coordinating engine. The mathematical model is formed of two Markovian distributed queues. One of these queues is modeled as M/M/1 and the second as M/G/S/N. Mathematical analysis of our proposal is focused to evaluate bandwidth access latency of an unlicensed user.

This chapter addresses the issues in air interface designs for Cognitive Radios. Fuzzy logic system is used as one of the soft computing techniques to learn to sub optimality and vagueness. Many good, simple, and quick fuzzy based solutions have been developed since few decades in many diverse domains. Through this chapter, the authors first discuss the significance and need of soft computing techniques in designing such solutions and then present fuzzy based solutions for spectrum access, mobility, and management. Hierarchical fuzzy systems have been used to get over to the problem of curse of dimensionality. The proposed solutions consider an architecture, similar to the one proposed by IEEE 802.22 working group, for spectrum sharing and management. Models have been designed using fuzzy logic toolbox in MATLAB, and the system performance is checked using SIMULINK.

This letter presents the design, analysis, and implementation of an electrically compact high-gain multilayer Vivaldi antenna. The multilayer antenna comprises gradient metasurface superstrate layers arranged on the top and bottom of a central electrically compact Vivaldi antenna via low-loss polyethylene foam spacers. The overall electrical dimensions of the antenna are 0.36λ 0 × 0.29λ 0 × 0.06λ 0 , covering a bandwidth from 450 MHz to 10 GHz with an average measured forward gain of more than 15 dBi (λ 0 : free-space wavelength at 450 MHz). The unit pixel dimensions of the gradient metasurface layer have been optimized to achieve a high-flat gain profile within the desired bandwidth of the antenna. The metasurface layers are printed on thin Kapton polyimide boards to minimize the system's weight. The prototyped antenna is extremely low-profile and weighs 182 g, making it ideally suited for lightweight ultrawideband (UWB) applications needing a very high forward gain.

Most of the existing studies on energy harvesting (EH) cooperative relaying networks are conducted for the outdoor environments which are mainly characterized by Rayleigh fading channels. However, there are not as many studies that consider the indoor environments whereas the state-of-the-art internet of things (IoT) and smart city applications are built upon. Thus, in this paper, we analyze a namely hybrid time-power splitting relaying (HTPSR) protocol in a full-duplex (FD) decode-and-forward (DF) battery energized relaying network in indoor scenarios modelled by the unpopular log-normal fading channels. Firstly, we formulate the analytical expression of the outage probability (OP) then the system throughput. Accordingly, we simulate the derived expressions with the Monte Carlo method. It is worth mentioning that in our work, the simulation and the theory agree well with each other. From the simulation results, we know how to compromise either the power splitting (PS) or the time splitting (TS) factors for optimizing the system performance.