Shortage Of Spectrum Resources Research Articles

W-band photonic-aided mm-wave ISAC system enabled by a shared OFDM signal waveform and a two-stage carrier frequency recovery algorithm.

Integrated sensing and communication (ISAC) systems will play a potential role in the upcoming six-generation (6G) networks. The reason is that ISAC systems can effectively solve the spectrum resource shortage problem and decrease the cost of deploying hardware equipment for simultaneously realizing communication and sensing. In this Letter, we successfully realize a W-band photonic-aided millimeter-wave (mm-wave) ISAC system enabled by a shared orthogonal-frequency-division-multiplexing (OFDM) signal waveform and two-stage carrier frequency recovery (CFR) algorithm. The digital-signal-processing (DSP)-based two-stage CFR algorithm includes the first-stage fractional frequency offset (FFO) estimation and compensation as well as the second-stage integer frequency offset (IFO) estimation and compensation. We experimentally analyze and verify that, with the introduction of the two-stage CFR algorithm, the system robustness to carrier frequency offset can be significantly enhanced. We also experimentally demonstrate, based on the ISAC system and our receiver DSP, a net data rate of up to 47.54 Gbit/s with a total signal bandwidth of 16 GHz over a 5.2 m wireless link at 94.5 GHz. Moreover, the sensing results indicate that the ISAC system can realize the detection of one target 2 m away from the transmitter with a ranging resolution of 0.98 cm and a ranging error of 4 mm. The system is anticipated to facilitate diverse potential applications in future 6G networks.

Dynamic Credible Spectrum Sharing Based on Smart Contract in Vehicular Networks

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system’s response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios.

113Gbps rainbow visible light laser communication system based on 10λ laser WDM emitting module in fiber-free space-fiber link.

With the advent of the sixth-generation mobile communication standard (6 G), the visible light communication (VLC) technology based on wavelength division multiplexing (WDM) technology can effectively solve the problem of shortage of spectrum resources and insufficient channel capacity. This paper introduces one of our technical achievements, namely the construction of a near-real-time visible light laser communication (VLLC) system based on WDM, which includes a self-designed 10-λ fully-packaged visible light laser emission module, 1 m multimode fiber - 0.175 m free space - 1 m multimode fiber optical transmission link, and receiver array. In the transmitter system, we adopt adaptive discrete multitone (DMT) modulation technique combined with Quadrature Amplitude Modulation (QAM) modulation scheme to obtain maximum spectral efficiency (SE). In the receiving system, we utilize the sparse-structured reservoir computing post-equalization algorithm to achieve superior equalization performance on the basis of the traditional post-equalization algorithm. The experimental results indicate that this quasi-real-time communication system has achieved a signal transmission rate of 113.175Gbps. To the best of our knowledge, this work has set a record in the field of high-speed visible light laser communication. Therefore, the laser communication system constructed by this work, with its flexibility in deployment and high-speed performance, demonstrates the significant potential application of visible light laser communication in data center interconnection and high-speed indoor access networks.

Secure STBC-Aided NOMA in Cognitive IIoT Networks

The Industrial Internet of Things (IIoT) has been recognised as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges including computation, storages, latency and interoperability. In this paper, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with non-orthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIOT devices with high spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA) based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology.

Given the shortage of spectrum resources and the demand for communication systems of diminutive size, multi-function, and adaptive characteristics, this paper proposes an L-slot frequency reconfigurable antenna based on the MEMS switch. The antenna size is 4.07 × 5.27 mm2 and is suitable for the U-band. The antenna structure consists of two RF MEMS switches, a Rogers RT5880 dielectric substrate, an L-slot patch, and a full-coverage ground. The switch is of a series contact structure and is arranged at the corner of an L-slot. By controlling the on and off state of the switch, the antenna can switch between four states of 42.36, 47.65, 53.13, and 56.72 GHz. According to the simulation results in CST STUDIO SUITE 2018, the maximum gain of the antenna is 7.90 dB, the impedance bandwidth of each state is above 1 GHz, and the direction is mainly consistent. The antenna can meet the demand for multi-frequency millimeter wave communication.

Resource Scheduling of Time-Sensitive Services for B5G/6G Connected Automated Vehicles

Due to the shortage of spectrum resources in the Internet of vehicles, the diversified time-sensitive services for B5G/6G connected automated vehicles cannot be effectively transmitted and the existing researches lack efficient and intelligent resource scheduling methods. Therefore, this paper proposes a spectrum resource scheduling model for time-sensitive services, and designs a two-tier joint resource scheduling method based on the age of information and bandwidth requirements under macro base station (MBS) and Road Side Unit (RSU). In the first layer, we propose a Vickery-Clarke-Groves (VCG) enabled auction model to solve the time-sensitive services resource scheduling problem, in order to guarantee the authenticity of vehicle users (VUEs) bidding. In addition, the lagrange relaxation algorithm is utilized to obtain the optimal solution of spectrum resource allocation to ensure the VUEs service delay and achieve low complexity resource scheduling. In the second layer, we design a federated learning based MBS auxiliary communication method to alleviate RSU communication pressure. The communication links of differentiated time-sensitive services are predicted to improve the quality of perception services. Simulation results verify that the proposed algorithms can enhance the VUEs experience effectively compared with the conventional methods, in terms of the delay, throughput and packet loss ratio.

Adaptive Channel Detection for Full-Duplex IAB Systems on Unlicensed Bands

The integration of integrated access and backhaul (IAB) and full-duplex communications on unlicensed bands can improve the spectrum efficiency and mitigate the spectrum resource shortage. To guarantee fair coexistence on unlicensed spectrum, the listen-before-talk mechanism needs to be executed at the IAB nodes, where the clear channel assessment (CCA) is applied before transmission. However, the residual self-interference (RSI) brought by the full-duplex communication may degrade the CCA detection performance tremendously. Therefore, in this paper we propose an adaptive channel detection scheme for the full-duplex IAB network to minimize the impact of RSI on the CCA detection, which can enhance the spectrum efficiency. In particular, the closed-form expressions of false alarm probability and detection probability are derived. Accordingly, the energy detection (ED) threshold applied in CCA is adjusted adaptively to improve the system throughput. Then, a novel timing sequence with respect to the self-interference cancellation and CCA detection is proposed. The corresponding optimal initial threshold is also derived to realize the adaptive ED threshold. Furthermore, an enhanced request to send/clear to send mechanism is proposed to solve the hidden terminal problem. Simulation results demonstrate that the proposed schemes can effectively reduce the impact of RSI on the CCA detection accuracy and significantly improve the system throughput.

CPSS using Dynamic Spectrum Accessibility on the Blockchain over Non-Real-Time Data

Abstract: Due to being dependent on wireless transmission through various devices for big data sharing in Cyber-PhysicalSocial Systems (CPSSs), radio spectrum resources are seriously in limited availability. Despite the fact that license-free access to the spectrum offers a great deal of promise to reduce the developing shortage of spectrum resources, competition for spectrum is more fierce due to the laxer access regulations. A dynamic cycle of "competition-verification-synchronization-competition" may be introduced by a blockchain technology to address this problem of competition. In this study, we offer a general architecture for managing license-free spectrum resources in CPSSs using blockchain and smart contracts. We precisely partition the spectrum of a local cell into numerous channels, each of which is equivalent to a blockchain. Next, we propose a blockchain-KM protocol that could expedite transaction processing while maintaining a generic blockchain's core features. Users of the Blockchain-KM protocol must mine or lease wireless spectrum, and the entire private chain is transformed into a multi-ring blockchain. In contrast to the conventional mining method, the prize in our mining process includes both a spectrum access licence and virtual currency. Once a miner has secured a permit for spectrum access, it will use the permit to send messages through wireless networks. If a miner decides not to send messages, it may sell its permit at auction. During the auction, we provide a virtual currency called Xcoin for trading spectrums or other items.

Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks.

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system's performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU's spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3% and the proposed method makes the SUs allocation more fair.

Regional autonomous security cooperative spectrum sensing method based on trust value

AbstractIn order to solve the problems of spectrum resource shortage, low spectrum utilization and shadow effect, a regional autonomous security cooperative spectrum sensing (CSS) method based on trust value is proposed to address the vulnerability of CSS technology in cognitive radio to malicious node attacks. Considering the difference in node position, the vulnerability of CSS to malicious node attacks, the large amount of centralized sensing data processing, and the large energy consumption of distributed sensing, based on the trust value update mechanism of ‘slow growth and fast recovery’, the perception area is divided and managed, and the final perception result is determined by majority voting based on the centralized sensing. The convergence value of the time difference of the perceptual process trust value is used to detect the anomaly of the node, and the cumulative difference in trust values between the nodes can detect and locate the malicious node. Experimental simulation results show that the proposed model is superior to traditional algorithms in spectral perception accuracy and malicious node detection.

Collaborative and Incremental Learning for Modulation Classification With Heterogeneous Local Dataset in Cognitive IoT

Among 6G high-density connection applications, the cognitive Internet of Things (IoT) is considered to be an effective means to cope with the problem of spectrum resource shortage and reduce energy consumption. The modulation classification with the functions of primary user detection and signal recognition has become the core technology in the cognitive IoT. However, the remarkable surge in the generated data makes it intractable to store and analyze such a large amount of data in a centralized manner. Meanwhile, due to the heterogeneity of IoT devices, the collected data in distributed devices may be non-independent-identical distribution, resulting in heterogeneous local datasets problem. Therefore, we introduce the federated learning (FL) and propose federated incremental learning (FIL) method to address the problem of heterogeneous local datasets in modulation classification. Specifically, we regard the private classes of a particular local device as incremental classes and incremental learning is adopted to learn the classification knowledge of private classes. In order to avoid excessive dispersion between the global model parameters and local model parameters, we utilize knowledge distillation in the local model training stage for imposing similar constrain of parameters on different local models. Simulation results have proven the effectiveness of our proposed method.

Radio resource management scheme in radar and communication spectral coexistence platform

In vehicular environments, the cooperation of radar and communications has been recognized as a promising solution to alleviate the shortage of spectrum resources. In this paper, we investigate the joint radar sensing and communication (JSC) system based on the cooperative spectrum sharing strategy. Via the full cooperation between both radar sensing and wireless communication functionalities, we can avoid the under-utilization of spectral resource while improving system efficiency. To implement our JSC control scheme, we focus on coalitional bargaining solutions according to the cooperative game theory. Especially, the coalitional Nash bargaining solution (CNBS) and coalitional Kalai-Smorodinsky bargaining (CKSBS) are employed to get a mutually desirable solution in the radar-communication coexistence platform. The main ides of CNBS is adopted to distribute the spectrum resource among roadside units, and both radar and communication functions in each individual vehicle. The concept of CKSBS is applied to allocate the spectrum resource for vehicles in order to maximize their payoffs. In the proposed scheme, the CNBS and CKSBS work together to share the surplus of a coalition with the fairness and efficiency properties. The main contribution of our proposed approach is the reciprocal combination of the CNBS and CKSBS to obtain the synergy effect. Finally, numerical results are provided to verify the effectiveness of the proposed scheme for the JSC system operations.

Multiple Orbital Angular Momentum Beams with High‐Purity of Transmission‐Coding Metasurface

AbstractVortex electromagnetic wave carrying orbital angular momentum (OAM) is expected to break through the shortage of spectrum resource because of its multiple independent modes. Nowadays, there are various methods to generate OAM vortex beam. Based on the concepts of digital metasurface and propagation phase, a broadband 3‐bit coding transmission metasurface is designed and illustrated by simulation and measurement. The elements exhibit the different phase by changing the size of metal patch, and the focusing phase is introduced to reduce the divergence of OAM beams. Based on the principle of phase superposition, the wide‐band, high‐purity, and multi‐beam OAM vortex waves can be generated by the transmission‐coding metasurface (TCMS). The experimental results verify that TCMS realizes the multiple OAM beams along different directions simultaneously from 9 to 10.5 GHz, which are consistent with the theoretical results. The high purity more than 90% of OAM mode is demonstrated by both the simulated and measured results. The TCMS provides a potential and effective scheme for OAM multiplexing and multi‐target positioning in wireless systems.

Integrated Cooperative Spectrum Sensing and Access Control for Cognitive Industrial Internet of Things

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT.

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay–underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.

A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning.

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

Research and Application of Computer Collaborative Spectrum Sensing Guided by Radio Technology

Currently, due to the proliferation of smart appliances and the rapid development of wireless communication technologies, there is a huge shortage of spectrum resources. Cognitive radio utilizes technologies that dramatically increase the utilization of free frequency bands, detecting the environment in real time and maximizing the reallocation of spectrum resources by sensing and reallocating the licensed free frequency bands. The frequency and accuracy required to determine the free bands is an important basis for determining the spectrum awareness in all aspects of a sensing radio system. Therefore, for the more complex network environment in reality and the intrusion of malicious users, this paper uses Fast K-medoids and Mean-shift methods for information fusion and an iterative approach to overcome malicious use to fuse data and finally obtain realistic feature statistics. The results show that the collaborative identification strategy proposed in this paper has good results.

Security Outage Probability Analysis of Cognitive Networks With Multiple Eavesdroppers for Industrial Internet of Things

The Industrial Internet of Things (IIoT) has been recognised as having the potential to benefit a range of industrial sectors substantially. However, widespread development and deployment of IIoT systems are limited for some reasons, the most significant of which are a shortage of spectrum resources and network security issues. Given the heterogeneity of IIoT devices, typical cryptographic security techniques are insufficient since they can suffer from challenges including computation, storage, latency, and interoperability. This paper presents a physical layer security analysis of the underlying cognitive radio networks for IIoT. Through consideration of the spectrum, IIoT devices can opportunistically utilise the primary spectrum, thereby improving spectrum efficiency and allowing access by an increased number of devices. Specifically, we propose two cognitive relay transmission (CRT) schemes, optimal single CRT (O-SCRT) and multiple CRT (MCRT), to improve transmission reliability further. Since it is challenging to obtain channel state information in the wiretap link, we provide a sub-optimal single CRT scheme and derive closed-form expressions of security outage probability by invoking both selection combination and maximal ratio combination techniques at the eavesdropper. To provide a benchmark, the round-robin single CRT scheme is also analyzed. Simulation results are provided to verify our analysis and show that O-SCRT provides the best system security outage performance.

Dynamic spectrum access based on deep reinforcement learning for multiple access in cognitive radio

With the increasing shortage of spectrum resources, dynamic spectrum access (DSA) technology is proposed to maximize the spectrum resources utilization. Traditional DSA solutions can no longer meet the requirements of high throughput and low interference in large-scale access scenarios of cognitive radio (CR). Therefore, in this paper, we propose a DSA scheme based on deep reinforcement learning (DRL) combined with multiple access methods to maximize the system throughput. In the DSA network, the access strategy adopted by the secondary user (SU) will directly affect the performance of the entire system, so we introduce DRL to help the SU learn the best access strategy in a dynamic environment. The trained SU can intelligently access the appropriate channel to avoid interference to the primary user (PU) and other SUs. By combining deep Q-network (DQN) into two multiple access methods: Frequency Division Multiple Access (FDMA) and Non-orthogonal Multiple Access (NOMA), DQN-based FDMA scheme and DQN-based NOMA scheme are designed, respectively, which can find the best DSA strategy to avoid collisions with PU or other SU and improve system throughput. Simulation results show that the DQN-based NOMA scheme has better performance than the DQN-based FDMA scheme.

Multipath Stability Routing in Cognitive UAV Swarm for Emergency Communications: A Hypergraph Matching Approach

With the rapid development of unmanned aerial vehicles (UAVs), it has been considered as an effective solution for emergency communications. In order to solve the contradiction between the rapid growth of user equipments and the shortage of spectrum resources as well as the problem that a single UAV cannot meet the multimission requirements, we integrate the cognitive radio spectrum access and UAVs to form a cognitive UAV swarm. Since most of the base stations cannot work properly in disaster scenarios, cognitive UAV swarm transmits the collected information through multihop routing in the form of a decentralized distributed ad hoc network. In order to solve the problems of low robustness of single paths and unstable links, we explore a multipath stability optimization problem and then propose a multipath stable routing algorithm based on the multidimensional hypergraph matching method. The results show that compared with the greedy algorithm, the proposed algorithm can obtain higher path stability while obtaining multiple node disjoint paths. Finally, this paper provides a reference value for establishing multipath routing of UAV ad hoc networks in emergency communication scenarios.