Search Results (111)

Multiband spectrum sensing in a cooperative environment is a novel solution for efficient spectrum resource management under the cognitive radio networks (CRNs) paradigm. This paper presents a distinctive framework where a central entity collects power spectral density data from multiple geographically distributed secondary users and applies the Gramian angular field (GAF) summation method to transform the time-series data into image representations. A major contribution of this work is the integration of these GAF images with a convolutional neural network (CNN), enabling precise and real-time detection of primary user activity and spectrum occupancy. The proposed approach achieves 99.6% accuracy in determining spectrum occupancy, significantly outperforming traditional sensing techniques. The main contributions of this study are (i) the introduction of GAF-based image representations for cooperative spectrum sensing in CRNs; (ii) the development of a CNN-based classification framework for enhanced spectrum occupancy detection; and (iii) the demonstration of superior detection performance in dynamic, real-time environments. Full article

In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network’s communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs). Full article

Integrated Sensing and Communications (ISAC) has attracted increasing attention due to more efficient utilization of both radio spectrum and hardwares. However, ISAC can only relieve the shortage of the spectrum, especially in the situation of exponential growth of wireless terminals. Efficient spectrum utilization strategy is still an important direction for the continuous evolution of wireless communication technology. As such, spectrum sensing (SS) is discussed in ISAC scenarios, and a novel cooperative SS scheme is proposed by as an improved auto-encoder for more efficient spectrum utilization. More specifically, the parameters of each local spectrum spectrum-sensing network are encoded and sent to the central server, and the local network parameters are decoded, fused, and returned to each local node at the central server. The simulations are designed, and the experiment results demonstrate the effectiveness of the proposed scheme. Full article

As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance. Full article

Symbiotic radio (SR), which has recently been introduced as an effective solution for 5G wireless networks, stands out with system models that include hybrid devices that share the frequency spectrum and transmit information to the same receiver. However, the low bit rate and the small amount of energy harvested in SR, where backscatter communication systems are integrated, make the system vulnerable to eavesdropping. To ensure security, the secrecy rate is defined as the difference between the number of bits transmitted to the receiver over the information channel and the number of bits reaching the eavesdropper (ED) over the wiretap channel. This paper is the first work that aims to maximize the secrecy rate for friendly jammer-aided SR networks with EDs over time allocation and power reflection coefficient in the presence of sensing errors. The proposed model consists of a base station (BS), a hybrid transmitter (HT) in symbiotic relationship with the BS, a WiFi access point used by the HT for energy harvesting, a jammer cooperating with the HT and BS, an information receiver, and EDs trying to access the information of the HT and BS. The simulation results provide valuable insights into the impact of system parameters on secrecy rate performance. Although taking the sensing error into account degrades the system performance, the real-world applicability of the system with sensing error is more realistic. It is also observed that the proposed system has higher performance compared to the wireless powered communication networks in the literature, which only use the energy harvest-then-transmit protocol and the power reflection coefficient is assumed to be zero. Full article

Camellia oleifera (Camellia oleifera Abel.) is a key woody oilseed tree. In recent years, China’s Camellia oleifera industry has shifted from extensive to refined management, with an action plan launched to boost productivity and efficiency. This study utilized remote sensing technology to diagnose crop nutrient levels. Focusing on 240 Camellia oleifera trees from four varieties at the Dechang Cooperative in Shucheng County, Anhui Province, the study collected full-spectrum canopy reflectance data (350–2500 nm) across five growing stages: spring shoot, summer shoot, fruit expanding, fruit ripening, and full blooming. First-order derivative (FD) and second-order derivative (SD) transformations were used to preprocess the spectral data and analyze the relationships between leaf potassium concentration (LKC) and the raw spectra (R), FD, and SD. The VCPA-IRIV strategy was then applied to identify sensitive wavelengths and artificial neural network algorithms were used to construct LKC estimation models. The main conclusions are as follows. (1) In the spring shoot stage, LKC ranged from 1.93 to 8.06 g/kg, with an average of 3.70 g/kg; in the summer shoot stage, LKC ranged from 2.01 to 8.82 g/kg, with an average of 4.96 g/kg; in the fruit expanding stage, LKC ranged from 1.40 to 18.27 g/kg, with an average of 4.90 g/kg; in the fruit ripening stage, LKC ranged from 1.45 to 8.90 g/kg, with an average of 3.71 g/kg.; and in the full blooming stage, LKC ranged from 2.38 to 9.57 g/kg, with an average of 5.79 g/kg. Across the five growth stages, the LKC content of Camellia oleifera showed a pattern of initially increasing, then decreasing, and subsequently increasing again. (2) The optimal LKC model for the spring shoot stage was FD-[7,6,2], with ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.6559, RMSEC = 0.1906 in the calibration set, ${\mathrm{R}}_{\mathrm{T}}^2$ = 0.4531, RMSET = 0.2009 in the test set. The optimal LKC model for the summer shoot stage was FD-[6,5,4], with ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.7419, RMSEC = 0.2489 in the calibration set, and ${\mathrm{R}}_{\mathrm{T}}^2$ = 0.7536, RMSET = 0.2259 in the test set; the optimal LKC model for the fruit expanding stage was SD-[7,6,2], with ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.3036, RMSEC = 0.2113 in the calibration set, and ${\mathrm{R}}_{\mathrm{T}}^2$ = 0.3314, RMSET = 0.1800 in the test set; the optimal LKC model for the fruit ripening stage was FD-[10,3,2], with ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.4197, RMSEC = 0.2375 in the calibration set, and ${\mathrm{R}}_{\mathrm{T}}^2$ = 0.5649, RMSET = 0.1772 in the test set, and the optimal LKC model for the full blooming stage was SD-[10,3,2], with ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.7013, RMSEC = 0.2322 in the calibration set, and ${\mathrm{R}}_{\mathrm{T}}^2$ = 0.5621, RMSET = 0.2507 in the test set. Full article

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%. Full article

In order to explore more spectrum resources to support sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensor paradigms is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attacks from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attacks, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on the sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. The numerical simulation results demonstrate that, compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities. Full article

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature. Full article

To address the shortage of wireless spectrum resources caused by the rapid development of the Internet of Vehicles, spectrum sensing technology in cognitive radio is employed to tackle this issue. In pursuit of superior outcomes, a double-threshold cooperative spectrum sensing algorithm is introduced. This algorithm enhances traditional energy detection technology to mitigate the high sensitivity to noise interference in the Internet of Vehicles environment. A double-threshold judgment mechanism can be established based on the uncertainty of noise. Varying fusion rules are implemented in the collaborative spectrum sensing scheme according to the density of vehicles and the spectrum resource demand. Simulation results demonstrate that the performance of the double-threshold cooperative spectrum sensing algorithm surpasses that of the traditional single-threshold energy detection scheme, particularly evident under lower Signal-to-Noise Ratio (SNR) conditions. Moreover, the proposed algorithm exhibits superior sensing performance in environments characterized by higher noise uncertainty. Full article

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds. Full article

This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity. Full article

Recent advancements in wireless communication technology have brought about the pressing issue of increasing spectrum scarcity. This challenge in spectrum allocation arises from ongoing research in the field of wireless communication. Unfortunately, a significant portion of the spectrum remains underutilized within wireless networks. Cognitive radio (CR) presents an innovative solution to this problem by enabling unlicensed secondary users to coexist with licensed primary users within allocated spectrum bands without causing interference to the primary users’ communications. This paper promises to address the spectrum redundancy challenges and substantially improve the spectrum utilization efficiency. Cognitive radio networks (CRNs), alternatively known as dynamic spectrum access networks, are comprised of multiple CR nodes and are frequently referred to as next generation (XG) communication networks. These XG communication networks are expected to offer high-speed data transmission capabilities to adaptable users through a variety of wireless architectures and dynamic access protocols. Since CRNs share similarities with traditional wireless networks but operate in an external wireless medium, they are more susceptible to various types of attacks compared to their wired counterparts. This vulnerability stems from the fact that wireless media can be intercepted or exploited, potentially leading to channel congestion or data interception. This paper presents two key approaches: the node evaluation and selection (NES) algorithm and the secure spectrum sensing mechanism, which incorporate the user’s interaction history and connection distance, that are recorded in a public ledger and managed by a blockchain management system. The proposed algorithm facilitates the central aggregation point for selecting nodes with outstanding performance for cooperative sensing, thus enhancing the network’s security against malicious node attacks. Full article

Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time. Full article

In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions. Full article