Secondary Users Access Research Articles

Secondary User Access Control in Cognitive Radio Networks: Concept, Design, and Analysis

Cognitive radio (CR), an important part of nextgeneration wireless communications networks, has been extensively studied to improve the spectrum utilization. The spectrum sensing and access functions are especially crucial for ensuring the secure and efficient spectrum sharing needs of CR networks. In this article, the secondary user access control (SUAC) based spectrum access technique is investigated to improve spectrum efficiency and network safety, by which a jamming signal is carefully designed to break down unauthorized secondary users (UA-SUs) sensing result and keep reliable sensing result from authorized secondary users (A-SUs). First, SUAC techniques are described, and a jamming cancellation method based on an oblique projection approach is proposed to guarantee the sensing result of authorized secondary users. Second, a prioritized SUAC approach is examined to ensure A-SUs with distinct spectrum access levels. Third, quadratic programming in massive multipleinput multiple-output based SUAC is presented to effectively design a jamming signal that combines minimizing jammer's influences on A-SUs and maximizing influences on UA-SUs. Finally, some open research areas and challenges in SUAC are discussed.

Secondary User Access Control (SUAC) via Quadratic Programming in Massive MIMO Cognitive Radio Networks

Secondary User Access Control (SUAC) via Quadratic Programming in Massive MIMO Cognitive Radio Networks

Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things

AbstractCognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was93.5%, the regression coefficient was0.98, and a reduced MSE of0.0013obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.

Maximizing Energy Efficiency in Hybrid Overlay-Underlay Cognitive Radio Networks Based on Energy Harvesting-Cooperative Spectrum Sensing

Spectrum demand has increased with the rapid growth of wireless devices and wireless service usage. The rapid development of 5G smart cities and the industrial Internet of Things makes the problem of spectrum resource shortage and increased energy consumption even more severe. To address the issues of high energy consumption for spectrum sensing and low user access rate in the cognitive radio networks (CRN) model powered entirely by energy harvesting, we propose a novel energy harvesting (EH)-distributed cooperative spectrum sensing (DCSS) architecture that allows SUs to acquire from the surrounding environment and radio frequency (RF) signals energy, and an improved distributed cooperative spectrum sensing scheme based on energy-correlation is proposed. First, we formulate an optimization problem to select a leader for each channel; then formulate another optimization problem to select the corresponding cooperative secondary users (SUs). Each channel has a fixed SUs cluster in each time slot to sense the main user state, which can reduce the energy consumption of SUs sensing and can reduce the sensing time, and the remaining time can be used for data transmission to improve throughput, and finally achieve the purpose of improving energy efficiency. Simulation results show that our proposed scheme significantly outperforms the centralized scheme in terms of SUs access capability and energy efficiency.

Fair and smart spectrum allocation scheme for IIoT based on blockchain

Compared to 5G mobile network with a peak rate of 10Gbps, 6G can provide over 100Gbps peak rate, which can better support Industrial Internet of Things (IIoT) popularity and application. Spectrum dynamic access is one of the basic techniques of 6G, and spectrum auction has been gradually applied to mobile communication to solve the problem of secondary users access. To cope with the risk of sudden demand increase of primary users after auction, several channel recall based schemes were proposed. However, the spectrum auction fairness between sellers and buyers as well as fairness among buyers during auction and channel recall process have not been considered in the existing spectrum auction algorithms. Utilizing the unique features of blockchain, a spectrum auction method based on blockchain for 6G mobile network is proposed. The fairness between sellers and buyers and fairness among buyers are guaranteed by smart contracts. The sudden increase of primary users’ traffic demand risk can also be well solved. In addition, in the proposed method, user privacy preservation is provided by blockchain. We deploy the algorithm on Ethereum test network, experimental results indicate that the proposed method is effective in fairness, with a high efficiency and preservation of user privacy.

Evaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation

As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT secondary users through utilizing spectrum holes resulting from the underutilization of frequency spectrum. A CR needs to frequently sense the spectrum to avoid interference with primary users (PUs). Compressive spectrum sensing techniques have been gaining increasing interest in wideband spectrum sensing, as they reduce the need for high-rate analog-to-digital converters, reducing the complexity and energy requirements of the CR. In order to enhance spectrum sensing performance, researchers proposed to incorporate PU spectrum usage information into the process of spectrum sensing. Spectrum usage information can be obtained through pilot signals, geo-locational databases or through evaluation of previous spectrum sensing results. In this paper, we are studying the effects of compressive sensing parameters namely compression ratio, sensing period, and sensing duration on the estimation of primary user behavior statistics. We achieved an accurate estimation of the primary user's behavior while saving 40% of the sampling rate by using compressive spectrum sensing compared to traditional spectrum sensing with Nyquist rate sampling.

Performance analysis of centralized dynamic spectrum access via channel reservation mechanism in cognitive radio networks

The two main goals in wireless networks are to provide channel allocation opportunities for incoming services and to assure the accomplishment of ongoing services. In this paper, a channel reservation scheme in cooperative cognitive radio networks (CRNs) based on dynamic spectrum access (DSA) is proposed. In this scheme, a centralized cognitive manager (CCM) provides the dynamic coordination access for primary users (PUs) and secondary users (SUs) through partitioning the spectrum into non-reserved channels (N-CRN) and reserved channels (R-CRN). N-CRN can be exploited by licensed PUs and unlicensed SUs, while R-CRN is dedicated for active SUs until the successful completion of their service. If a SU session in N-CRN is interrupted by the sudden arrival of PU, then a spectrum handover occurs to R-CRN to resume its session. The reserved channel number R is deduced upon the reduction of both forced termination and blocking probabilities for SUs while satisfying PUs’ traffic demands. Also, overall system cost and SU cost functions are investigated. Simulation results show significant reduction in blocking and forced termination probabilities and improvement in utility function and service completion rate of SUs while meeting PU’s quality of service (QoS) aims. For example, when the arrival traffic rates per channel λp=0.5, and for R=1,R=2 and R=3, the values of specific SU forced termination probabilities Pft are 0.1597, 0.116, 0.0799 respectively compared to 0.2106 for the case of no channel reservation. Furthermore, blocking probability and service completion rate of PU are evaluated to demonstrate the effect of channel reservation variation on the system performance.

Secure Beamforming in 5G-Based Cognitive Radio Network

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) is a significant system in the 5G wireless communication system. However, the system is an exceptional way for the cognitive users to secure a communication from the interferences in multiple-input multiple-output (MIMO)-NOMA-based cognitive radio network. In this article, a new beamforming technique is proposed to secure an information exchange within the same cells and neighboring cells from all intervened users. The interference is caused by an imperfect spectrum sensing of the secondary users (SUs). The SUs are intended to access the primary channels. At the same time, the primary user also returns to the channel before the SUs access ends. This similar way of accessing the primary channel will cause interference between the users. Thus, we predicted that the impact of interferences would be greatly reduced by the proposed technique, and that the proposed technique would maximize the entire secrecy rate in the 5G-based cognitive radio network. The simulation result provides better evidence for the performance of the proposed technique.

5G multi‐layer routing strategies for TV white space secondary user access

As mobile applications and services have developed, the dramatic growth in user data traffic has led to the legacy channels becoming ever more congested with the commensurate requirement for more spectrum. This has motivated both regulatory bodies and industry to investigate innovative strategies to increase the existing spectral efficiency. Prominent examples include both Long Term Evolution (LTE) which employs orthogonal frequency-division modulation technology to improve bandwidth efficiency, and heterogeneous networks, which facilitate the offloading of data traffic between technologies such as from LTE to Wi-Fi and vice versa. Furthermore, as 5G mobile technology and related standards mature, there is an impetus to address the issue of secondary user (SU) spectrum access in which TV White Space (TVWS) is the prime contender. Two nascent viewpoints have emerged as to how this will evolve: i) greater coverage, ii) increased throughput allied with lower latency. This paper presents a novel TVWS framework that successfully fulfils both criteria to ensure 5G services can both exploit TVWS spectrum and protect the benefits of SU access and quality-of-service provision by using a routing strategy on the Access Network Discovery and Selection Function server to dynamically determine the most suitable heterogeneous technology for the new framework.

Multi-Scale Hierarchical Resource Management for Wireless Network Virtualization

To deal with the conflict between limited radio resources and dramatic growth of wireless traffic, wireless network virtualization (WNV) is proposed as an efficient network sharing solution in 5G era. In this paper, we propose a multi-time-scale hierarchical model for WNV, which consists of primary users (PUs) with low latency requirement, secondary users (SUs) with high data rate requirement and tertiary users (TUs) without strict QoS requirement, such as machine-to-machine communications. The resource allocation scheme is decomposed into inter-slice subchannels pre-assignment in large time period and intra-slice subchannels and power scheduling in small time slot. In large time period, the subchannels pre-assignment problem is formulated as an integer optimization problem considering the requirement of PUs, SUs, and TUs. In small time slot, the subchannels and power scheduling of PUs is formulated as a mixed optimization problem with integer variables, for which we adopt Lyapunov drift-plus-penalty function with tradeoff factor ${V}$ to transform the problem into a Lyapunov optimization problem and propose a two-step algorithm consisting of a heuristic subchannel assignment procedure and a fast barrier-based power allocation procedure. SUs access spectrum in a static manner and TUs access spectrum in a cognitive way. Simulation results show that our proposed algorithm achieves great performance in terms of users’ performance with almost linear computation complexity.

Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks

Fifth-generation (5G) heterogeneous network deployment poses new challenges for 5G-based cognitive radio networks (5G-CRNs) as the primary user (PU) is required to be more active because of the small cells, random user arrival, and spectrum handoff. Interweave CRNs (I-CRNs) improve spectrum utilization by allowing opportunistic spectrum access (OSA) for secondary users (SUs). The sum utilization of spectrum, i.e., joint utilization of spectrum by the SU and PU, depends on the spatial and temporal variations of PU activities, sensing outcomes, transmitting conditions, and spectrum handoff. In this study, we formulate and analyze the sum utilization of spectrum with different sets of channels under different PU and SU co-existing network topologies. We consider realistic multi-channel scenarios for the SU, with each channel licensed to a PU. The SU, aided by spectrum handoff, is authorized to utilize the channels on the basis of sensing outcomes and PU interruptions. The numerical evaluation of the proposed work is presented under different network and sensing parameters. Moreover, the sum utilization gain is investigated to analyze the sensitivities of different sensing parameters. It is demonstrated that different sets of channels, PU activities, and sensing outcomes have a significant impact on the sum utilization of spectrum associated with a specific network topology.

On the performance of cognitive internet-of-vehicles with unlicensed user-mobility and licensed user-activity

Internet of Vehicles (IoV) is regarded as an emerging concept for intelligent transportation cyber-physical systems. Due to the limited number of channels available in IEEE 802.11p DSRC/WAVE standard for vehicular communications, time-critical emergency messages in IoV could suffer from delays. Thus, cognitive radio enabled IoV has potential to overcome this problem, where vehicles (i.e., unlicensed vehicular users aka unlicensed secondary users) access RF spectrum other than 802.11p spectrum in an opportunistic manner by sensing and identifying the idle channels licensed to primary users without causing any harmful interference to primary users. In cognitive IoV, unlicensed vehicular users rely on spectrum sensing to find idle bands for opportunistic IoV communications. Existing spectrum sensing algorithms either consider low mobility or stationary (no mobility) of unlicensed vehicular users. Furthermore, joint impact of unlicensed vehicular user mobility and primary user activity have not considered while evaluating the performance of spectrum sensing for opportunistic communications. In this paper, we analyze and evaluate the combined impact of unlicensed vehicular user mobility and licensed user activity for cognitive IoV where each vehicle is assumed to be equipped with a wireless device capable of communication and spectrum sensing for wide-band spectrum regime including IEEE 802.11p. We formally study the performance of the proposed approach for cognitive IoV using mathematical analysis by considering a speed of the vehicles, activities of the primary users and distance between licensed and unlicensed users. Then, numerical results obtained from simulations are used to evaluate the performance of the proposed approach. Results show that the unlicensed vehicular user mobility and primary user activity have a higher impact on misdetection probability than that on false alarm probability. Furthermore, communication range, speed, travel direction and distance between unlicensed vehicular users and licensed primary users affect the overlapping time period for spectrum sensing to find idle channels for opportunistic communications. We also study the dynamic spectrum access for opportunistic communications in terms of expected transmission time, achievable per-user rate and expected transmission count for successful communications.

Joint resource optimization for DF relaying SWIPT based cognitive sensor networks

Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.

Dynamic Spectrum Reservation for CR Networks in the Presence of Channel Failures: Channel Allocation and Reliability Analysis

Providing channel access opportunities for new service requests and guaranteeing continuous connections for ongoing flows until service completion are two challenges for service provisioning in wireless networks. Channel failures, which are typically caused by hardware and software failures or/and by intrinsic instability in radio transmissions, can easily result in network performance degradation. In cognitive radio networks (CRNs), secondary transmissions are inherently vulnerable to connection breaks due to licensed users’ arrivals as well as channel failures. To explore the advantages of channel reservation on performance improvement in error-prone channels, we propose and analyze a dynamic channel reservation (DCR) algorithm and a dynamic spectrum access (DSA) scheme with three access privilege variations. The key idea of the DCR algorithm is to reserve a dynamically adjustable number of channels for the interrupted services to maintain service retainability for ongoing users or to enhance channel availability for new users. Furthermore, the DCR algorithm is embedded in the DSA scheme enabling spectrum access of primary and secondary users with different access privileges based on access flexibility for licensed shared access. The performance of such a CRN in the presence of homogeneous and heterogeneous channel failures is investigated considering different channel failure and repair rates.

Towards Secure and Energy-Efficient CRNs Via Embracing Interference: A Stochastic Geometry Approach

In a cognitive radio network (CRN), the interference caused by secondary users (SUs) is conventionally regarded as an obstacle to the throughput of the primary network. However, when considering security, the interference brings potential benefits to the secrecy throughput of the primary network. For a stand-alone primary network, the secrecy guard zone, including the cooperative mode and non-cooperative mode, has been shown as an efficient method to enhance the network security. The cooperative mode is traditionally thought to outperform the non-cooperative mode due to the jamming effect of the artificial noise generated by primary transmitters on eavesdroppers. However, allowing SUs to access the licensed spectrum, the resulting interference can be used as a source of the artificial noise instead of that generated on purpose by primary transmitters, which in return benefits the energy efficiency of the primary network. Inspired by the two benefits brought by SUs, this paper considers a random underlay CRN with eavesdroppers overhearing primary transmissions. To enhance both the security and energy efficiency of the primary network, we apply a secrecy guard zone around each primary transmitter, and adopt the non-cooperative mode. We exploit the stochastic geometry to model such a random CRN and analyze the connection/secrecy probability of primary links. We propose a criterion for guaranteeing the performance of secure primary network. The main idea is that compared with the primary network with the cooperative mode, the access of SUs should not reduce the secrecy throughput and energy efficiency of the primary network with the non-cooperative mode. Based on the obtained analytical results, we design the optimal secondary link scheduling schemes under the criterion. Both analytical and numerical results show that the interference from SUs can be exploited toward a secure and energy-efficient primary network and provide SUs with extra transmission opportunities.

Prioritized Secondary User Access Control in Cognitive Radio Networks

In cognitive radio networks, a secondary user access control (SUAC) technique has been utilized to improve network management and system security, in which a jamming signal is injected to degrade the spectrum sensing performance of unauthorized secondary users. In the meantime, it ensures reliable spectrum sensing performance for authorized secondary users (A-SUs). In order to introduce spectrum access priorities among A-SUs, a prioritized SUAC (P-SUAC) technique is investigated in this paper. A projection-based jamming cancellation method is considered, where the singular value decomposition operation is applied in computing prioritized projection operators. An orthogonal frequency-division multiplexing technique is considered as a transmission model, and the energy detection method is used for secondary user spectrum sensing. Simulation results illustrate the effectiveness of the proposed P-SUAC method in providing the A-SUs with different access priorities.

Optimal Spectrum Access and Energy Supply for Cognitive Radio Systems With Opportunistic RF Energy Harvesting

We consider an energy-harvesting cognitive radio (CR) system coexisting with an unslotted primary user (PU) system. The secondary user (SU) either harvests energy from the PU transmissions or transmits when the PU is sensed to be idle. In this paper, we optimize the sensing intervals to balance between energy harvesting and spectrum access, considering the PU traffic statistics that affects both the energy arrival and consumption in addition to the opportunistic spectrum access. We propose to use different sensing intervals if the SU decides to transmit or harvest energy. The harvesting and transmission durations can be used to optimize the tradeoff between maximizing the utilization of PU spectrum opportunities and harvesting sufficient energy to transmit. We determine a closed-form expression for the probability of SU being active as a function of the PU traffic statistics and SU's access and harvest durations. Optimal transmission and harvesting durations have been found to maximize the CR throughput while protecting the PU. Furthermore, we enhance the system capability by considering that the SU can choose to purchase energy from the PU during the discovered PU idle periods in addition to the energy harvested opportunistically. It can further exploit the difference in the PU busy and idle durations as per its energy needs. The average energy cost spent in a time slot is minimized to achieve a given SU transmission rate while adhering to the PU collision constraint.

Optimal spectrum access and power control of secondary users in cognitive radio networks

In future 5G communication system, radio resources can be effectively reused by cognitive radio networks (CRNs), where a lot of secondary users (SUs) are able to access the spectrum of primary users (PUs). In this paper, we analyze the optimal spectrum access and power control of SUs on multiple bands with the target of maximizing the average sum rate (ASR) of SUs. Specifically, based on the stochastic geometry, the random distributions of PUs and SUs are modeled by Poisson point processes (PPPs), based on which we derive out the closed-form outage probabilities and obtain the ASR of SUs. Then, we formulate the maximization problem of ASR on multiple bands under the constraints of outage probabilities. With the help of convex optimization, the optimal density of SUs is obtained in closed-form when the power of SUs is fixed. The convexity of ASR is also verified, and we evaluate the optimal power of SUs when the density of SUs is fixed. Based on these two obtained results, a spectrum access and power control algorithm is further proposed to maximize the ASR of SUs on multiple bands. Simulation results demonstrate that the proposed algorithm can achieve a higher maximum ASR of SUs over the average power allocation algorithm, and the density and power boundary of SUs are constrained by PUs as well as the interference in the networks.

History-Assisted Energy-Efficient Spectrum Sensing for Infrastructure-Based Cognitive Radio Networks

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art.

Spectrum-Availability Based Routing for Cognitive Sensor Networks

With the occurrence of Internet of Things (IoT) era, the proliferation of sensors coupled with the increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensors transmit data packets over the licensed spectrum bands as well as the free ISM bands. The dynamic spectrum access technology enables secondary users (SUs) access wireless channel bands that are originally licensed to primary users. Due to the high dynamic of spectrum availability, it is challenging to design an efficient routing approach for SUs in cognitive sensor networks. We estimate the spectrum availability and spectrum quality from the view of both the global statistical spectrum usage and the local instant spectrum status, and then introduce novel routing metrics to consider the estimation. In our novel routing metrics, one retransmission is allowed to restrict the number of rerouting and then increase the routing performance. Then, the related two routing algorithms according to the proposed routing metrics are designed. Finally, our routing algorithms in extensive simulations are implemented to evaluate the routing performance, and we find that the proposed algorithms achieve a significant performance improvement compared with the reference algorithm.