Behavior Of Primary Users Research Articles

An adaptive approach for integrating primary user behavior in compressive spectrum sensing

Compressive sensing (CS) has been widely used to sense the wideband spectrum with fewer measurements by taking advantage of radio spectrum underutilization. As new smart devices, such as IoT devices, smart home devices, and wearables, use batteries and have limited memory, more research is needed to reduce the overuse of cognitive radio (CR) resources through spectrum sensing. To reduce the number of compressive measurements required for spectrum recovery, researchers proposed approaches like weighted and sequential compressive sensing. In this paper, we estimate the primary user’s (PU) behavior statistics and use the estimated information in a novel weighted sequential compressive spectrum sensing approach. Our proposed approach can reduce and adapt the number of measurements and the sensing time to the changing number of active channels in a dynamically changing wideband spectrum.

Online Double Auction for Wireless Spectrum Allocation With General Conflict Graph

The spectrum usage often comes in an online fashion. Considering the selfish behaviors of both primary users(PUs) and sencond users(SUs), we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> double spectrum allocation methods. We propose a truthful online double auction for spectrum allocation. Preempting existing spectrum usage is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> allowed. We design a strategyproof mechanism for both the primary user side and the SU side. In previous studies, users who do not interfere with each other have not been reasonably allocated to use channels. There is one and only one user on the used spectrum channel at a certain time, which will result in discarding many user requests. Our model allows multiple users to use a spectrum channel at the same time. Aiming at the shortcomings of previous research, we propose the concept of grouping for online auctions, so that all SUs that do not interfere with each other can use spectrum channels at the same time. This will greatly increase the number of users who use the spectrum at the same time. We thus increase the utilization rate of the user market, and a large number of users will not be idle and abandoned.In the design of our experiments, the grouping model proposed in this paper has obtained at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$43{\%}$</tex-math></inline-formula> utilization rate on the set channel, and our experiments have obtained good generality for different interference radius.

Spectrum Sensing Utilizing Power Threshold and Artificial Intelligence in Cognitive Radio

The purpose of this paper was to take a preventative approach to predict the transmission status of the primary user (PU) on the white spectrum throughout the duration of the simulation. We intend to reduce the computational simulation budget to eliminate user interference by precise spectrum sensing handoff. We develop PU behaviors to use a time waiting estimate methodology that gives band occupancy time intervals. The proposed strategy may also lessen risks associated with flexible clients and adapt to changing channels. Real-time applications will use ANN range sharing, which reduces transmission delay, while high-throughput applications will use PTHD range sharing. Since noise as well as obscuring effects after each transmission plan, their proximity in the channel can influence range detection. This finding is in line with published investigations. Most range detection systems compare frequency test results with the operating channel's control scope thickness. The paper met design objectives by developing a noise-independent sensing methodology with proactive time estimation. The trial is accomplished with reduced cost and latency.

A hybrid spectrum access approach for efficient channel allocation and power control in cognitive radio networks

SummaryRadio spectrum is the backbone for wireless communication. Many recent studies show that most of the available spectrum has already been allocated. Cognitive radio (CR) provides a new dimension in the field of wireless communication and is considered as a life line for the spectrum scarcity problem as seen all around the world nowadays. CR enables the use of dynamic spectrum access, where secondary users (SUs) or unlicensed users are allowed to opportunistically use the spectrum when it is not being used by the primary users (PUs) or licensed users. Medium Access Control (MAC) protocols use the sensing capability and the primary users behavior to generate the transmission opportunities for SUs. This article proposes two Markov based hybrid access methods namely Hybrid‐P1 and Hybrid‐P2, where a secondary user (SU) dynamically decides it's transmission mode between overlay and underlay depending upon the primary users's (PU's) past and present behavior. Analytical results based on the Markov chain concept are very encouraging and verified through OMNeT++ simulator. This work also analyses the performance of overlay only and underlay only methods, which are well known data transmission techniques in the cognitive radio networks (CRNs). Simulation results show the 47% and 59% performance improvement in Hybrid‐P1 access method, when compared to the overlay only and underlay only access methods respectively. Furthermore simulation results also show the 54% and 65% performance improvement in Hybrid‐P2 access method, when compared to the overlay only and underlay only access methods respectively.

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.

An Intelligent Resource Allocation Scheme in Energy Harvesting Cognitive Wireless Sensor Networks

The energy harvesting cognitive wireless sensor network (EHCWSN) introduces energy harvesting technology and cognitive radio technology into the traditional wireless sensor network (WSN), which significantly prolongs the working life of the sensor node and effectively alleviates the congestion problem of the unlicensed spectrum. Due to the uncertainty of the energy harvesting process and the behavior of the primary user (PU), how to allocate and manage limited network resources is a crucial problem in the EHCWSN. In this work, a new Q-learning-based channel selection method is proposed for the energy harvesting process and the randomness of the PU's behavior in the sensor network. By continuously interacting and learning with the environment, the method guides the secondary user (SU) to select the channel with better channel quality. Moreover, we also propose a resource management and allocation mechanism with guaranteed QoS requirements for node traffic based on the framework of Lyapunov optimization theory. We design a low-complex online algorithm based on the optimization framework, which is then validated through extensive simulations. The results demonstrate that our design achieves higher accuracy with the QoS guarantee.

Distributed Artificial Intelligence Based Cluster Head Power Allocation in Cognitive Radio Sensor Networks

This proposed work addresses the problem of dynamic and real-time power allocation to the cluster head (CH) in a cognitive radio sensor network (CRSN). The work is based on spectrum sensing outputs by these secondary users (SUs) for a nonergodic system. In general, a cooperative sensing network consists of multiple nodes communicating with each other about their respective spectrum sensing output, to perform the desired work. Therefore, the SU nodes of each cluster sense the spectrum all the time, which results in a continuous power consumption in CRSN. Now, the total amount of energy, which is used to allocate all the CHs at a particular place for spectrum sensing is being avoided and saved. This is performed, by the use of autocorrelation error for predicting the real-time behavior of primary users extended by the implementation of vector quantization to identify nearby active SU nodes and based on distributed artificial intelligence for power allocation. The simulation results and the mathematical derivation validate the proposed method.

A Switching-Based and Delay-Aware Scheduling Algorithm for Cognitive Radio Networks

In this article, an opportunistic inter-frame spectrum scheduler that maximizes the throughput of cognitive radio networks over a span of multiple time-slots is proposed. An optimization problem is formulated to find the optimum inter-frame scheduler while taking into account the switching delay, the primary user (PU) activity, historical information on the PU behavior, the channel quality as well as the secondary user (SU) status. Simulation results show that the proposed inter-frame scheduler significantly improved the overall aggregate throughput and average switching delay of the cognitive radio network when compared to the values obtained when scheduling is done on a slot-by-slot basis.

A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

A large scale of spectrum sensing techniques are proposed to improve the use of spectrum resources. However, the EE (energy efficiency) should be guaranteed for both primary and secondary users, especially under various detection performance constraints. In this regard, the linking between activities of PU (primary user) and dynamic access behaviors of SUs (secondary users) should be considered in an integrated way. This survey has compared different existing scenarios and frameworks on EE optimizations. The principal objective is to enhance the system throughput and to coordinate on the physical layer of both PU and SUs, in order to enable a high-quality spectrum detection and a more efficient spectrum access. In the technical part, several optimization methods are introduced under PU’s constraints, and different methods based on game theory are applied to suitable cooperative sensing scenarios for SUs’ optimal access. Finally, the complexity of algorithms is compared, to further reduce the execution time and deploy real-time adaptation for users with lower delay.

Multi-Resource Allocation: Analysis of a Paid Spectrum Sharing Approach Based on Fluid Models

Nowadays industrial and academic communities are focusing much of their efforts to define the main characteristics of 5G. Cognitive radio (CR), offering the possibility to significantly increase the spectrum efficiency, will play a key role in the whole range of Internet of Things communications (one of the use cases of 5G networks). While CR is one of the most discussed topics in contemporary spectrum management, there are still many issues and challenges to be solved, even more when we think about large networks. In this context, one of the main question that motivates this paper is: how to encourage the spectrum sharing behavior of primary users (PUs)? With this in mind, we study a paid-sharing approach where secondary users (SUs) pay for spectrum utilization. We assume a preemptive system where PUs have strict priority over SUs, and the affected SUs will then be reimbursed, implying some cost for the PUs service provider. This paper bears on the analysis of the behavior of the system, where the number of users is arbitrary large and an admission control policy over SUs is applied. We develop a computationally efficient methodology to find an accurate estimation of the optimal admission control boundary based on fluid limits.

A Reinforcement Learning Based Routing in Cognitive Radio Networks for Primary Users with Multi-stage Periodicity

Designing an efficient routing protocol for cognitive radio networks is critical due to the dynamic behavior of the primary users. Based on empirical studies, the primary users activity on the licensed channels has periodicity comprised of several stages, and that the model of primary users activity may change during different stages. This paper has identified two main challenges facing designers: how to transmit packets via a stable route, and how to ensure imposing of minimal interference on the primary users. To address these, we propose a routing protocol which is based on a generalized version of Q-learning and which exploits the said model of primary users behavior. We divide the infinite time horizon into cycles (corresponding to periods), then break each cycle into several sub-cycles (corresponding to stages), making an assumption that the statistical model parameters of the primary users activity will not change during a sub-cycle. The extensive simulations confirm that our proposed routing approach outperforms existing schemes in terms of throughput and minimal interference. The paper also verifies that imposing of significant interference on the primary users and degradation of QoS of secondary users stem from lack of attention to the multi-stage periodic behavior of primary users.

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.

Sensing Device Management for History‐Based Spectrum Sharing in Cognitive Radio Networks

A novel approach to managing a fully distributed cognitive radio network (CRN) is presented. This approach builds on the concept of history‐based spectrum access, in which cognitive base stations (BSs) independently estimate the system load using history records and adaptively swap their occupied spectrum bands to ensure allocation fairness and high overall throughput. In addition, cognitive BSs monitor primary user (PU) behavior in order to avoid interfering with active PUs. In this work, we address two issues that afflict history‐based access: the first is the high cost of the sensing devices needed at each cognitive BS to be able to independently draw conclusions about the status of the CRN and the second is the unreliability inherent in practical sensing hardware (such as energy detectors). Simulation results show that the proposed technique manages to solve the two abovementioned issues without any noticeable drop in performance and without sacrificing the distributed nature of the protocol.

Discrete-Time Markov Chain Analysis of Energy Efficiency in a CR Network Regarding Primary and Secondary Traffic With Primary User Returns

In cognitive radio (CR) networks coexistence between primary and secondary networks is allowed to exploit the spectrum bands more efficiently. To have a green CR network, energy efficiency (EE) as well as collision probability between such networks are vital key performance. While, it is desired to improve EE, the latter should be decreased as much as possible. Therefore, interference between the primary user (PU) and secondary user (SU) is of high importance. Two kinds of interference are unavoidable. They are sensing error and PU re-occupancies interferences. While most studies focus on the sensing error interference, with the dynamic PU, the latter is also quite intense. Moreover, a precise investigation on the effect of such interference necessitates considering the PU and SU traffic behavior. Therefore, in this paper a new analytical framework is proposed in which the EE and collision probability are formulated. The traffic behaviors of PU and a general SU traffic are also considered. Using renewal reward theory, EE, and collision probability are then extracted and investigated for different dynamic rates of the PU traffic. For this, sensing error interference, PU-re-occupancy interference, and total energy consumption are derived in closed form expressions. The queue stability of SU’s buffer is also investigated. Finally, simulation results are provided to justify the theoretical results. Suitable values for sensing and transmission times are obtained to maximize the EE under collision and queue stability constraints.

Maximizing Cognitive Radio Networks Throughput Using Limited Historical Behavior of Primary Users

Cognitive radios (CRs) mainly aim to reuse the spectrum holes in order to efficiently utilize the available scarce radio spectrum. However, current CRs techniques have a throughput limitation problem which ultimately limits telecommunication applications horizons nowadays. Moreover, achieving high throughput will overcome the bottleneck of CRs application limitations to the reporting and browsing applications only. To tackle this emerging throughput limitation issue in the CRs, this paper proposes the online greedy throughput maximization (OGTM) algorithm which overcomes the throughput limitations. OGTM allows the sensing cycle frame to have a variable length according to the assumed decision validity interval. Then, OGTM varies the decision validity interval of secondary users (SUs) based on the primary users (PUs) historical behavior. As a proof of concept, we developed a simulator in order to evaluate the performance of the proposed OGTM technique. The simulation results show that SUs benefit from the limited PU historical behavior learning, which resultantly increases the throughput up to 95% and at the same time decreases the miss detection probability by 50%.

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs) with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs), we propose a dynamic spectrum allocation (DSA) algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users’ status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7–26%, and the specific improvement is negatively correlated with the SU arrival rate.

Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation

In this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design issue of selecting subchannels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. With the Markov assumption of the primary user (PU) behavior, a persistent spectrum sensing strategy (PSSS) is proposed to take advantage of the PU traffic patterns in determining the channels to sense. Theoretical and simulation results show that RSSS and PSSS display different performance in different ranges of PU traffic parameters. We finally propose an adaptive spectrum sensing strategy (ASSS), which determines whether to use RSSS or PSSS for spectrum sensing at a given time based on the estimated PU traffic parameters. Numerical results under various system parameters are presented to evaluate the performance of RSSS, PSSS, and ASSS. The ASSS is shown to gain the advantages of both RSSS and PSSS in different ranges of PU traffic parameters and provide more available subchannels for SU.

Performance of Concatenated Kernel Code in Cognitive Radio Networks

Error correction coding is extensively used to achieve reliability in digital communication especially in Physical Layer. In this paper, construction of concatenated kernel code defined over algebraic structure group is discussed. Minimal trellis representation is given for constructed concatenated kernel codeword. Constructed code is tested in cognitive radio network environment subjected to continuous interference due to the behavior of primary users. Proposed code is tested through simulations and its performance is analyzed in terms of Bit Error Rate (BER) in mitigating the effect of continuous interference. Maximum likelihood graph Viterbi decoding technique is used to decode the concatenated kernel code.

Dynamic Spectrum Leasing Under Uncertainty: Modeling, Analysis and Algorithms

Dynamic spectrum leasing (DSL) is a promising method to improve the spectrum efficiency. A lot of work has been done about spectrum sharing issues with static models. In this paper, we want to study the behavior of the primary users (PUs) under uncertainty. For this objective, we propose a model with time-varying parameters. The model captures both technical and economic features. We study the long-term behavior of the PUs in the model with stochastic Cournot game theory and variational inequality theory. We show that the PUs can maximize their long-term utilities with a static supply of spectrum. In addition, we also propose two algorithms based on stochastic approximation theory. The convergence and performance of the algorithms are demonstrated theoretically and numerically. The proposed model and analytical framework can be used to study and predict the behaviors of PUs given a DSL system and provide design insights for the DSL system.

Fungible Orthogonal Channel Sets for Multi-User Exploitation of Spectrum

This paper proposes a two-stage process for assigning fungible orthogonal channel sets to multiple cognitive radios (CRs) for opportunistic spectrum access. Assigning orthogonal channel sets to the CRs eliminates the possibility of collision among them, and allows the CRs to focus on avoiding collisions with the primary user (PU). In particular, each CR uses a learning-based dynamic channel selection (DCS) algorithm to maximize the exploitation of the assigned channels. We propose a neural network that can accurately estimate the performance of the adopted learning-based DCS algorithm on a set of channels, using the duty cycle and the complexity of the PU's behavior on the channels. Our simulations on synthetic and real measurement data sets show that the proposed channel sets allocation algorithm, together with the neural network, significantly outperforms a method that selects channels with the lowest duty cycle.