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Smart windows can selectively regulate excess solar radiation to reduce heating and cooling energy consumption in the built environment. However, the inevitable dissipation of ultraviolet and near‐infrared into waste heat results in inefficient solar utilization. Herein, a dual‐band selective solar harvesting (SSH) window is developed to realize full‐spectrum utilization. A transparent photovoltaic, converting ultraviolet into electricity, and a transparent solar absorber, converting near‐infrared into thermal energy, are integrated and coupled with a ventilation system to extract heat for indoor use. Compared with common transparent photovoltaics, the SSH window increases solar harvesting efficiency up to threefold while maintaining a considerable visible transmittance. Simulations suggest that the SSH window, besides generating electricity, delivers energy savings by over 30% higher than common smart windows. This is the first integration of transparent photovoltaic and transparent solar absorber into a window, which may open up a new avenue for the development of energy‐efficient buildings. A selective solar harvesting window is designed as a photovoltaic‐thermal collecting system coupled with a ventilation system to extract heat for indoor use. The integration of the transparent photovoltaic and transparent solar absorber enables a dual‐band selectivity for full‐spectrum solar utilization, opening up a new avenue for the development of energy‐efficient buildings.

Cognitive Radio (CR) is a new concept in wireless communication systems that aims at enabling dynamic access to the frequency spectrum for unlicensed secondary users (SU) through the reuse of licensed frequencies. Specifically, CR systems are expected to detect idle licensed frequencies (spectrum holes) by applying a spectrum sensing procedure, to take advantage of them for providing service to the SU, and to release them to the licensed primary users (PU) upon request. The duration of the spectrum sensing procedure is an overhead factor that affects the actual utilisation of the spectrum by the CR system and whose adaptation may enhance spectrum utilisation: the less time the CR spends for spectrum sensing and identifying spectrum holes, the more time the spectrum is available for secondary use by the CR. In this paper, we define a formal measure for spectrum hole utilisation in dynamic scenarios and propose a new method for adapting the duration of energy-based spectrum sensing that improves the detection of PU signals as well as the utilisation of spectrum holes by the SU. The proposed method is illustrated with numerical results obtained from simulations which confirm its effectiveness.

The television white space (TVWS) spectrum presents a promising opportunity to extend wireless broadband access, particularly in rural, underserved, and hard-to-reach communities. To leverage this potential, low-power radio communication equipment must efficiently utilise the TVWS spectrum on a secondary basis while ensuring strict compliance with regulatory requirements to prevent harmful interference to primary services. This paper presents a comparative performance analysis of TVWS radio equipment from three original equipment manufacturers (OEMs). The equipment under test was identified to reflect each OEM, as follows: OEM 1 and OEM 2 from South Korea and OEM 3 from the USA. We evaluated their performance in two real-world field scenarios, namely outdoor short-distance and outdoor long-distance. The evaluation was based on the following key metrics: (i) spectrum utilisation efficiency (SUE), (ii) received signal strength (RSS), (iii) downlink throughput, and (iv) connectivity to the Geo-Location Spectrum Database (GLSD) in compliance with the South African TVWS regulatory framework. The overall preliminary experimental results indicate that in both scenarios, white space devices (WSDs) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11af Standard demonstrated better performance than those based on the 3rd Generation Partnership Project Long-Term Evolution-Advanced (3GPP LTE-A) Standard in terms of the SUE, downlink throughput, and RSS metrics. All WSDs under test demonstrated sufficient compliance with the regulatory requirement metric.

Vehicular Internet of Things (V-IoT) networks, sustained by a high-density deployment of roadside units and sensor-equipped vehicles, are currently at the edge of next-generation intelligent transportation system evolution. However, offering stable, low-latency, and energy-efficient communication in such heterogeneous and delay-prone environments is challenging due to limited spectral resources and diverse quality of service (QoS) requirements. This paper presents a Priority-Aware Spectrum Management (PASM) scheme for IoT-based vehicular networks. This dynamic spectrum access scheme integrates interweave, underlay, and coexistence modes to optimize spectrum utilization, energy efficiency, and throughput while minimizing blocking and interruption probabilities. The algorithm manages resources efficiently and gives proper attention to each device based on its priority, so all IoT devices, from high to low priority, receive continuous and reliable service. A Continuous-Time Markov Chain (CTMC) model is derived to analyze the proposed algorithm for various network loads. Simulation results indicate improved spectral efficiency, throughput, delay, and overall QoS compliance over conventional access methods. These findings establish that the proposed algorithm is a scalable solution for dynamic V-IoT environments.

Unmanned aerial vehicle (UAV)-empowered communications have gained significant attention in recent years due to the promise of agile coverage provision for a large number of various mobile nodes on the ground and in three-dimensional (3D) space. Consequently, there is a need for efficient spectrum utilization in these dense aerial networks, which is characterized through radio environment maps (REMs), the construction of which is an important research area. Nevertheless, due to the difficult collection of radio frequency (RF) data, there are limited works that are based on real-world measurement campaigns. This paper presents a novel experimental setup that includes a constellation of three UAVs, the communication signals of which are measured by a software-defined radio (SDR) mounted on a separate UAV. It follows a trajectory that defines the REM’s two-dimensional (2D) area on a plane, executed at four altitudes, to extend the REM to 3D. The measurements are then processed and their features (received mean power level, average difference of the mean power, percentage of meaningful correlations) are analyzed in the temporal, spatial, and frequency domains to determine the utilization of a 20 MHz band in the 2.4 GHz spectrum, as well as their variation with altitude. This analysis provides a base for research in reducing the amount of measurements (by identifying the regions of low and of high interest) and spectrum occupancy prediction for UAV-based communication coexistence.

Cooperative spectrum sensing (CSS) plays a vital role in cognitive radio networks (CRNs). CSS enables efficient spectrum utilization and improves communication reliability through dynamic detection of underutilized frequency bands. However, the methods which evolved earlier face limitations like reduced detection accuracy and high false alarm rates in low Signal-to-Noise Ratio (SNR) conditions. Efficient allocation of spectrum resources in cognitive radio networks is affected by unreliable sensing at low signal-to-noise ratios, which leads to missed detections and false alarms. To overcome these challenges a novel optimized deep learning model is presented in this research work which provides reliable spectrum detection under noisy environments. The proposed Optimized Multi-Scale Graph Neural Network with Attention Mechanism (OMSGNNA) utilizes graph-based data representation and provides multi-scale feature extraction. The attention mechanism effectively fuses spatial and temporal information which enhances the performances. Additionally, an Adaptive Butterfly Optimization with Lévy Flights (ABO-LF) is incorporated to fine tune the parameters of proposed model. The proposed model experiments which are conducted using benchmark RadioML2016.10b dataset includes I/Q samples from 11 modulation schemes across SNR levels ranging from − 20 dB to 18 dB. The performance evaluation exhibits better performance of proposed OMSGNNA in terms of 98% accuracy at high SNR with better precision, recall, and F1-score. Comparative analysis reveals that the proposed OMSGNNA outperforms traditional deep learning models even at low SNR conditions.

Different devices and applications in wireless networks share spectrum resources reasonably. However, there are still issues such as channel overlap and adjacent interference in spectrum allocation and utilization, making the process of data dynamic resource allocation more complex. Therefore, a new data dynamic resource allocation technique for wireless networks is proposed. An elastic optical wireless network is formed by combining elastic optical network and wireless network. A global constrained resource allocation optimization model is designed based on the threshold of the maximum frequency gap number occupied on the fiber core at the end of allocation. Then, by using the global optimization genetic algorithm, the optimal dynamic resource allocation results of the elastic optical wireless network are obtained. Experimental results show that the spectrum utilization obtained by this technology is higher when the number of cores is 12, and the spectrum utilization is significantly improved by employing the proposed method.

In the rapidly evolving landscape of wireless communications, optimizing spectrum utilization has become paramount. Cognitive radio (CR) technology offers a promising solution by enabling unlicensed secondary users (SUs) to intelligently access and exploit underutilized spectrum bands. The citizens broadband radio service (CBRS) framework provides a structured approach to shared spectrum access, making it ideal for CR systems implementation. However, efficient spectrum sensing, especially within CBRS, is a major challenge due to environmental variations, interference, and the need for timely detection of primary users (PUs). This paper addresses the issue of suboptimal spectrum sensing efficiency in CBRS-based CR systems and proposes innovative approaches to improve cooperative spectrum sensing. We explore a spectrum sensing paradigm that encourages collaboration among secondary users and utilizes their collective intelligence to achieve better spectrum sensing performance. Our goal is to improve spectrum utilization within the CBRS ecosystem and enable more efficient and harmonious sharing of this valuable resource.

The channel bonding proposed in IEEE 802.11ac has the potential to multiply the data rate. However, the backward compatibility with legacy 802.11 stations (STAs) and the overlapping basic service set (OBSS) problem make the available frequency resource severely under-utilised. The downlink (DL) approach (where the access point (AP) uses the available non-primary channels to transmit DL data to other 802.11ac STAs) and the relay (RL) approach (where the AP instructs some 802.11ac OBSS STAs to use the available non-primary channels to transmit uplink data to a pre-designated RL) are proposed to improve the performance of 802.11ac by efficiently using the under-utilised spectrum. The simulation results show that the proposed scheme can mitigate the OBSS problem and obtain significantly better performance than the current media access control (MAC) protocol in the 801.11ac draft. In addition, the proposed approaches can work seamlessly with the 802.11ac protocol.

In this article, a cognitive radio (CR) integrated antenna system, which has 1 sensing and 24 communication antennas, is proposed for better spectrum utilization efficiency. In the 24 communication antennas, 3 different operating band antennas are realized with an 8-element MIMO configuration. The sensing antenna linked to port 1 is able to sense the spectrum that ranges from 2 to 12 GHz, whereas the communication 8-element MIMO antennas linked with ports 2 to 9, ports 10 to 17 and ports 18 to 25 perform operations in the 2.17–4.74 GHz, 4.57–8.62 GHz and 8.62–12 GHz bands, respectively. Mutual coupling is found to be less than −12 dB between the antenna elements. Peak gain and radiation efficiency of the sensing antenna are better than 2.25 dBi and 82%, respectively, whereas the peak gains and radiation efficiencies of all communication antennas are more than 2.5 dBi and 90%, respectively. Moreover, diversity characteristics of the MIMO antenna are assessed by parameters such as DG, ECC and CCL. It is found that ECC and CCL are less than 0.42 and 0.46 bits/s/Hz, respectively, and also DG is more than 9.1 dB.