Opportunistic Spectrum Access Scheme Research Articles

Modeling and analysis of opportunistic spectrum access schemes in cognitive cellular networks under imperfect sensing

Cognitive radio (CR) is an exciting technology to solve the spectrum scarcity problem in the future cellular networks. As the grade-of-service (GoS) performances directly reflect the user's experiences in the cognitive cellular networks (CCNs), the GoS performance analysis of the OSA scheme in the CCNs has attracted much attention. In this article, we derive GoS performance metrics with a three-dimension continuous-time Markov chain (CTMC) to model the opportunistic spectrum access (OSA) schemes with non-hopping and with hopping in the CCNs under imperfect sensing. Under imperfect sensing, for a given primary user traffic, the range of achievable user equipment (UE) traffic is derived with guarantying the GoS of the UE. Simulations are conducted to verify the validity of the proposed CTMC model and corresponding analysis.

Learning-Theoretic Multi-Channel Spectrum Sensing and Access in Full-Duplex Cognitive Radio Networks with Unknown Primary User Activities

The majority of the opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. In this paper, we consider the problem of optimal opportunistic multi-channel spectrum sensing and access using full-duplex (FD) radios in the presence of uncertain primary user (PU) activity statistics. A joint learning and spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely affected. To address this problem, we formulate a robust optimization problem. Our analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. We analyze the impact of the sensing period on the PU collision probability and the SU throughput, and find the optimal sensing period via convex optimization. We show that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies also demonstrate that the resulting robust solution provides a good trade-off between optimizing the SU's throughput and protecting the PU.

Spatial⁻Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things.

The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology that can address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatial positions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum.

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost). We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.

On the performance of single ring law based sensing approaches for opportunistic spectrum access

Multiple antenna systems are becoming increaingly prominent startig from 4G systems to the evolving 5G systems. During the course of these system deployments, spectrum scarcity has been a well prevailing problem. In this regard, opportunistic spectrum access schemes have been developed, which adopt periodic sensing approaches to detect the presence of the primary user and thereby allow efficient spectrum utilization by secondary users. Extension of spectrum sensing approaches to multiple antenna systems require a proper detection framework. The recent formulations of large random matrices and associated single ring law (SRL) for signal detection have been validated only for massive antenna systems. Further, its detection performance with respect to existing sensing approaches has not been studied so far. In this paper, first we present a detection framework suitable for any multiple antenna system. Introducing modifications to this framework so as to reduce the number of computations, we explore the suitability of single ring law (SRL) based spectrum sensing in comparison to other approaches. Finally, we propose a hybrid dual-stage sensing approach, which renders better detection performance than the existing ones. Using system simulations, under two different cases of sparse channel environments, namely multiple channels having exact common support and approximate common support, the efficacy of the proposed approach is demonstarted using detection probability versus signal to noise ratio (SNR) plots.

Energy-Efficient Cooperative Spectrum Sensing With Reporting Errors in Hybrid Spectrum Sharing CRNs

Recently, cooperative spectrum sensing (CSS) in cognitive radio networks has been extensively researched. However, most of the studies mainly focus on maximizing the spectral efficiency while the energy consumption is generally ignored. However, since the secondary users (SUs) are usually battery-powered devices, energy saving is very important. This paper studies the mean energy efficiency (EE) maximization problem of the CSS system using the hybrid spectrum sharing (HSS) scheme. Specifically, the effects of imperfect spectrum sensing and reporting channel errors on the EE are considered. Our goal is to maximize the mean EE of SUs while maintaining the detection accuracy by jointly optimizing the sensing time and the number of cooperative SUs, subject to the SUs' average/peak transmission power constraints (ATPC/PTPC) and minimum data rate constraint, and the average interference power constraint of primary user. To address the non-convexity of the optimization problem, an energy-efficient iterative power allocation algorithm is developed. Simulations compare the achievable mean EE under ATPC/PTPC with three hard combining fusing rules using the HSS scheme and the opportunistic spectrum access (OSA) scheme, respectively; the results show that our proposed HSS scheme can obtain higher mean EE than the conventional OSA scheme and that the EE achieved under ATPC is better than that under PTPC. Moreover, among the three hard combining fusing rules, the Majority rule has the best EE.

Compressive cyclostationary spectrum sensing with a constant false alarm rate

Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.

On opportunistic use of location of licensed users in improving the throughput of cognitive radio

The opportunistic spectrum access (OSA) and spectrum sharing (SS) are most commonly used schemes in accessing the underutilized licensed spectrum by a cognitive radio (CR) system. Unlike to SS scheme in which the CR users are permitted to co-exist on the same band being used by licensed users (LUs), in OSA scheme the unlicensed users (UUs) are allowed to access an idle band only. During a CR communication in OSA scheme, in order to remain aware of the re-appearance of LUs on the band of interest, the process of spectrum sensing is of high importance. During sensing, in order to decide the idle/busy status of the licensed band, the proper selection of decision threshold \({\uplambda }\) is of high importance. \({\uplambda }\) is mainly selected by using two principles called as constant detection rate (CDR) in which \({\uplambda }\) is calculated by taking fixed value of probability of detection \(\left( {{{\varvec{P}}}_{{\varvec{d}}} } \right) \) and constant false alarm rate (CFAR) in which \({\uplambda }\) is calculated by taking fixed value of probability of false alarm \(\left( {{\varvec{P}}}_{{\varvec{fa}}} \right) \). The CDR principle is favorable in protecting the communication of LUs while CFAR principle suits in improving the throughput of CR system. So, blind use of any of the principle lead to a compromise either in the security of LUs or throughput of CR. This paper proposes an approach which based on the mobile/stationary nature of UU node, and the distance of UU node from LU, makes selection of an appropriate scheme among the CDR-OSA, CFAR-OSA, and SS, to opportunistically improve the CR-throughput.

Coverage probability in cognitive radio networks powered by renewable energy with primary transmitter assisted protocol

This paper studies the opportunistic spectrum access (OSA) of the secondary users in a large-scale overlay cognitive radio (CR) network powered by renewable energy to improve both the energy and spectral efficiency of data transmission. Particularly, with energy harvesting module and energy storage module, the primary transmitters (PTs) and secondary transmitters (STs) are assumed to be able to collect ambient renewables and store them in batteries for future use. Upon harvesting sufficient energy, the corresponding PTs and STs (denoted by eligible PTs and STs) are then allowed to access the spectrum according to their respective medium access control (MAC) protocols. For the primary network, an Aloha type of MAC protocol is considered, under which the eligible PTs make independent decisions to access the spectrum with probability ρp. For the secondary network, a threshold-based Opportunistic spectrum access (OSA) scheme, namely the primary transmitter assisted (PTA) protocol, is investigated, under which an eligible ST is allowed to access the spectrum only if the maximum signal power of the received pilots sent from the active primary transmitters (PTs) is lower than a certain threshold Nta. By applying tools from random walk theory and stochastic geometry, and assuming infinite battery capacity for energy storage module, we characterize the transmission probabilities of PTs and STs, respectively. With the obtained results of transmission probability, we then evaluate the coverage (transmission non-outage) performance of the overlay CR network powered by renewable energy. Simulations are provided to validate our analysis.

The Impact of Network Size and Mobility on Information Delivery in Cognitive Radio Networks

There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in cognitive radio networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius $\alpha$ , which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on $\alpha$ , below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.

Power saving and improving the throughput of spectrum sharing in wideband cognitive radio networks

This paper considers a wideband cognitive radio network which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and proposes a novel cognitive radio system that exhibits improved sensing throughput and can save power consumption of secondary user (SU) compared to the conventional cognitive radio system studied so far. More specifically, under the proposed cognitive radio system, we study the problem of designing the optimal sensing time and power allocation strategy, in order to maximize the ergodic throughput of the proposed cognitive radio system under two different schemes, namely the wideband sensing-based spectrum sharing scheme and the wideband opportunistic spectrum access scheme. In our analysis, besides the average interference power constraint at primary user, the average transmit power constraint of SU is also considered for the two schemes and then a subgradient algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

A novel protocol for transparent and simultaneous spectrum access between the secondary user and the primary user in cognitive radio networks

In this paper, we propose a novel protocol for cognitive radio networks, termed spectrum co-access protocol (SCAP), for secondary users to transparently and simultaneously access spectrum with primary users. The motivation is to eliminate the disruption to secondary user communications by the resurgence of primary user transmission. This protocol enables mutually beneficial coexistence between the primary user network and the secondary user network. Through spectrum co-access, SCAP creates a virtual secondary user control channel in licensed spectrum that is invisible to the primary user. As a result, SCAP is a medium access control protocol that allows for simultaneous spectrum access between the secondary user and the primary user networks. The performance evaluation indicates that SCAP provides significant performance improvement for the secondary user network over the existing opportunistic spectrum access scheme.

Navigation Data-Assisted Opportunistic Spectrum Scheduling for Network-Based UAV Systems: A Parallel Restless Bandits Formulation

A myriad of applications and services of information and communication technology (ICT) has been developed in the past decades, within which networks are the fundamental architecture to connect people and things. As one of the network-based ICT applications, recently, unmanned aerial vehicle (UAV) cooperation systems have attached much attention due to their successful applications in complex military and civilian missions. In this paper, we propose a navigation data-assisted optimal opportunistic spectrum access scheme for wireless communications in heterogeneous UAV networks, to achieve maximized long-term data rate by flexibly scheduling the spectrum subbands. Thanks to the navigation data that is always available locally at the entities in the network, rough prediction of wireless link quality can be obtained to assist subband allocations. Furthermore, to formulate the optimal spectrum scheduling problem, we develop a parallel restless bandits model, which can reduce the NP-hard optimization problem to simply selecting the link with the minimum index in the online stage. Extensive simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing one.

Opportunistic Spectrum Access in Imperfect Spectrum Sensing Cognitive Networks

—Spectrum sensing strategy is key to realize cognitive radio. However, spectrum sensing error would affect the access strategy of secondary users in cognitive networks. This paper addresses the spectrum sensing strategy under imperfect spectrum sensing, and proposes opportunistic spectrum access strategies for the imperfect spectrum sensing and fading channels respectively. By setting the optimal operating point of the spectrum detection and updating the confidence vector, we turn the spectrum access optimizations under the imperfect spectrum sensing and fading channels into ones under the perfect spectrum sensing based on the partially observable Markov decision process model. Simulation results show that the strategies proposed could make the secondary users achieve about 10% margin in throughput and the cognitive networks have higher spectrum utilization.

A Dynamic QoS Model for Improving the Throughput of Wideband Spectrum Sharing in Cognitive Radio Networks

This paper considers a wideband cognitive radio network (WCRN) which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and studies the ergodic throughput of the WCRN that operated under: the wideband sensing-based spectrum sharing (WSSS) scheme and the wideband opportunistic spectrum access (WOSA) scheme. In our analysis, besides the average interference power constraint at PU, the average transmit power constraint of SU is also considered for the two schemes and a novel cognitive radio sensing frame that allows data transmission and spectrum sensing at the same time is utilized, and then the maximization throughput problem is solved by developing a gradient projection method. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

New coalition formation game for spectrum sharing in cognitive radio networks

SUMMARYIn cognitive radio networks, Secondary Users (SUs) can access the spectrum simultaneously with the Primary Users (PUs) in underlay mode. In this case, interference caused to the licensed users has to be effectively controlled. The SUs have to make spectrum access decisions in order to enhance their quality of service, but without causing harmful interference to the coexisting PUs. In this paper, we propose a cooperative spectrum decision, which enables the SUs to share the spectrum with the PUs more efficiently. Our approach is based on a new coalitional game in which the coalition value is a function of the SUs' spectral efficiencies, the inter‐SUs interference, and the interference caused to the PUs. By applying new Enter and Leave rules, we obtain a stable coalition structure. Simulation results show that the SUs' spectral efficiencies are considerably increased and that the interference caused to the coexisting PU is reduced by about 7.5% as compared to an opportunistic spectrum access scheme. Moreover, the proposed coalitional game results in a more balanced spectrum sharing in the network. Copyright © 2014 John Wiley & Sons, Ltd.

Reciprocally opportunistic spectrum access

AbstractThe problem of opportunistic spectrum access in cognitive radio networks (CRNs) is considered in this paper. Especially, we discuss designing distributed learning protocols for non‐cooperative secondary users (SUs) to efficiently utilise the spectrum opportunities. It is well known that the SUs’ selfish behaviors will result in a network collapse. Each SU is thus enabled to form conjectures over how the competing SUs would respond to its decision‐makings and believes that they alter future spectrum access strategies in proportion to its own current strategy variation. The conjectural belief adapts in accordance with local observations. The interaction among SUs is modelled as a stochastic learning procedure, in which each autonomous SU behaves as an intelligent agent. In this way, all SUs can independently ‘learn’ the behaviors of their competitors, optimise the opportunistic spectrum access strategies and finally achieve the goal of reciprocity in CRNs. Based on the belief model, two learning mechanisms, that is, the best response learning algorithm and the gradient ascent learning algorithm, are proposed to stabilise the opportunistic CRNs. We also theoretically prove that the SUs’ stochastic behaviors and beliefs converge to the conjectural equilibrium under some conditions rising from the learning procedure. Numerical results are provided to evaluate the performance of the proposed mechanisms, and show that the achieved system performance gain outperforms the multi‐agent Q‐learning scheme and the Nash equilibrium scheme when there are numerous SUs in the CRN. Copyright © 2014 John Wiley & Sons, Ltd.

Energy-Efficient Spectrum Access in Cognitive Radios

Cognitive radio (CR) and energy-efficient design have emerged as two promising techniques to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for an orthogonal frequency division multiplexing (OFDM)-based CR network with multiple secondary users (SUs). Both worst EE and average EE are considered and optimized for different emphases and application scenarios. Since the original optimization issues belong to nonconvex integer combinatorial fractional program and are essentially NP-hard for an optimal solution, we use continuous and convex relaxation to modify the problems for somewhat better mathematical tractability. For the relaxed worst-EE-based spectrum access problem, we first demonstrate the joint quasiconcavity of EE on subchannel and power allocation matrices and then develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution. The relaxed average-EE-based spectrum access problem is still NP-hard and may have many local optima. We first transform the problem into an equivalent form and introduce a general concave envelope based branch-and-bound (B&B) approach to find the global optimal solution. We then exploit the underlying properties of the energy-efficient transmission to speed up the convergence of the B&B approach. Besides, we develop a low-complexity heuristic approach to find a suboptimal solution. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones while the low-complexity suboptimal approaches can well balance the performance and complexity.

Spatial Throughput Characterization in Cognitive Radio Networks with Threshold-Based Opportunistic Spectrum Access

This paper studies the opportunistic spectrum access (OSA) of the secondary users in a large-scale overlay cognitive radio (CR) network. Two threshold-based OSA schemes, namely the primary receiver assisted (PRA) protocol and the primary transmitter assisted (PTA) protocol, are investigated. Under the PRA/PTA protocol, a secondary transmitter (ST) is allowed to access the spectrum only when the maximum signal power of the received beacons/pilots sent from the active primary receivers/transmitters (PRs/PTs) is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocols, the concept of spatial opportunity, which is defined as the probability that an arbitrary location in the primary network is detected as a spatial spectrum hole, is introduced and then evaluated by applying tools from stochastic geometry. Based on spatial opportunity, the coverage (non-outage transmission) performance in the overlay CR network is analyzed. With the obtained results of spatial opportunity and coverage probability, we finally characterize the spatial throughput, which is defined as the average spatial density of successful transmissions in the primary/secondary network, under the PRA and PTA protocols, respectively.

Opportunistic Spectrum Access for Cognitive Radio Networks withMultiple Secondary Users

We develop channel-aware opportunistic spectrum access strategies for cognitive radio networks consisting of multiple secondary users. In addition to balancing between the cost of foregoing weaker transmission opportunities in pursuit of channels that offer better data rates, we consider collisions between primary users (PUs) and secondary users (SUs), and among the SUs themselves. We derive strategies for both cooperative setting where SUs maximize their sum total of throughputs, as well as non-cooperative, game theoretic setting where each SU tries to maximize its own throughput. We show that the optimal schemes for both scenarios are pure threshold policies, where each SU decides to use or skip transmission opportunities by comparing the channel qualities to a fixed threshold. In the non-cooperative case, we establish the existence of Nash equilibrium and develop best response strategies that can converge to equilibria, with SUs relying only on their local observations. We study the tradeoff between maximal throughput in cooperative setting and fairness in the non-cooperative setting, and schemes based on utility functions and pricing that mitigate this tradeoff. Lastly, we analyze the issue of interference of SUs to the PUs. We show numerical results illustrating the schemes and analysis.