Opportunistic Spectrum Access Networks Research Articles

Energy efficiency of full-duplex cognitive radio in low-power regimes under imperfect spectrum sensing

This paper investigates the energy efficiency of a full duplex (FD) cognitive radio (CR) system in the low-power regime under a practical self-interference cancellation performance and imperfect spectrum sensing. We consider an opportunistic spectrum access network in which the secondary user (SU) is capable of self-interference suppression (SIS). The SIS ability enables the SU to work in simultaneous transmit and sense (TS) mode in order to increase the quality of channel sensing. Towards this goal, we first study the sensing performance, i.e., false-alarm and miss-detection probabilities, of the FD CR networks using TS, and compare the results with traditional half duplex (HD) CR systems using transmit only mode (TO). In the next step, we show that due to imperfect spectrum sensing, the secondary network channel is best characterized as a Gaussian-Mixture (GM) channel, which is widely used to capture the asynchronism in heterogeneous cellular networks. We then analytically characterize the low-signal-to-noise-ratio (low-SNR) metrics of the minimum energy per bit and wideband slope of the spectral-efficiency curve, and obtain these fundamental limits in closed-form. Furthermore, the characterization of these fundamental metrics allows us to identify practical signaling strategies that are optimally efficient in the low-SNR regime for the considered FD CR system. The benefits in terms of energy efficiency offered by FD CR over HD CR are also clearly demonstrated.

Distributed Opportunistic Spectrum Access in an Unknown and Dynamic Environment: A Stochastic Learning Approach

In this paper, the problem of distributed throughput maximization in an opportunistic spectrum access network with multiple secondary users (SUs) and multiple primary channels is investigated. To address the challenges in designing efficient solutions in a dynamic and unknown environment, we formulate the optimization problem as a noncooperative game, which is further proved to be an ordinal potential game. We then propose a best-response-based algorithm to achieve the Nash equilibrium points (NEPs) of the formulated game, given that there exists a coordinator for SUs to work in a round-robin fashion and a common control channel for SUs to exchange their information. To further relieve the system overhead due to information exchange among SUs, we design a new stochastic learning automata (SLA)-based algorithm, called N-SLA, which can converge to the pure-strategy NEPs of the formulated ordinal potential game in a fully distributed way. To our best knowledge, we are the first to address the convergence issue of the SLA-based algorithms for general ordinal potential games. Simulation results validate the effectiveness of our proposed algorithms.

Stochastic MAC-layer interference model for opportunistic spectrum access: A weighted graphical game approach

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

Secondary VoIP Capacity in Opportunistic Spectrum Access Networks with Friendly Scheduling

In conventional cognitive radio, the primary network usually remains unchanged. In some cases, however, the primary network operator may wish to accommodate secondary user access. In this paper, we assess the secondary user VoIP capacity when primary basestation scheduling is designed to be secondary network friendly. Friendliness is measured by the number of connections that can be supported subject to typical quality of service constraints in the presence of delay tolerant primary traffic. An offline scheduler is first derived that maximizes friendliness using an integer linear program formulation. We show that this schedule can be found using a minimum cost flow graph construction in time complexity that is polynomial in the number of time slots. Two online scheduling algorithms are then compared that achieve various levels of friendliness. The first algorithm operates by having the primary network temporally shape its residual capacity subject to satisfying its own packet deadline constraints. The second algorithm assumes virtual secondary calls and applies scheduling to both primary traffic and virtual secondary traffic. Results are presented for a variety of parameters that show the degree to which friendly scheduling can improve secondary user VoIP capacity compared to non-friendly primary scheduling.

Goodput maximization in opportunistic spectrum access networks under constraints on the inter-packet transmission waiting time

We consider the problem of selecting the packet size, the Forward Error Correction (FEC) coding rate, and the duration of the spectrum sensing interval, which maximize the communication goodput (payload bits correctly received per unit time) of the Secondary Users (SUs) of a cognitive radio network. We start by deriving a closed form expression of the SUs achievable goodput under the assumption of Markovian primary channel use and perfect spectrum sensing. Then, we show that the optimal packet size and FEC coding rate selection change in case we impose a constraint on the maximum time SUs can wait before successfully transmitting a packet, i.e., on what we call the inter-packet transmission waiting time. Such waiting time depends on the alternating PU presence on the channel. Finally, we derive upper and lower bounds on the achievable goodput, which allow us to optimally select the packet size, the FEC coding rate, and the duration of the spectrum sensing time interval, in case of imperfect spectrum sensing. Extensive simulation results are provided that validate our findings. Specifically, we show that by adaptively and jointly selecting the system parameters as a function of received SU power and PU traffic statistics, it is possible to achieve a goodput increase in the order of 20%.

Stochastic Game-Theoretic Spectrum Access in Distributed and Dynamic Environment

In this paper, we investigate the problem of channel selection for interference mitigation in opportunistic spectrum access networks using a stochastic game-theoretic approach. The studied network is distributed and dynamic , where each user only has its individual information, and no information exchange is available among users. Moreover, each user is considered to be dynamically active due to its specific data service requirement. Specifically, a user randomly becomes active and then competes for the wireless channel to transmit for a random duration. To capture such dynamic interactions among users, a dynamic interference graph is defined, and based on this, the interference mitigation problem is formulated as a graphical stochastic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Then, the performance bounds of the NE are theoretically analyzed. Furthermore, we design a fully distributed and online algorithm based on stochastic learning for the interference-mitigation channel selection, which is proved to converge to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for interference mitigation and throughput improvement in the distributed and dynamic environment.

Incorporating self-interference suppression for full-duplex operation in opportunistic spectrum access systems

Inspired by recent developments in full-duplex (FD) communications, we consider an opportunistic spectrum access (OSA) network in which secondary users (SUs) are capable of partial/complete self-interference suppression (SIS). This enables them to operate in either simultaneous transmit-and-sense (TS) or simultaneous transmit-and-receive (TR) modes, with the goal of achieving improved primary user (PU) detection and/or higher SU throughput. We first consider an overlay OSA setup, and we study the TS and TR modes. We also explore the spectrum awareness/efficiency tradeoff and determine an efficient adaptive strategy for the SU link. We then consider a spectrum underlay model, with the objective of optimizing SUs' transmission powers so as to maximize the sum-throughput of $K$ FD secondary links subject to a PU outage constraint. Operating in an FD fashion is not always efficient for SUs. Hence, we propose an optimal policy for switching between FD and half-duplex. The criteria for this policy depend mainly on the SIS capabilities of SUs. Finally, we propose a mode selection algorithm for the switching process. Numerical results indicate that operating in the TS mode can reduce the PU outage probability by up to 100% compared with the classical listen-before-talk scheme.

Queueing model for heterogeneous opportunistic spectrum access

In this study, we propose a queueing model to analyse the performance of an opportunistic spectrum access (OSA) system with service interruptions operating over heterogeneous channels in which the service transmission rate and the service interruption rate after the transmission is resumed are generally different than their value prior to the interruption. We first propose Markov chain models to analyse this system under memoryless service time and availability periods. On the basis of simplification assumptions, we also provide an analytical z-Transform analysis of the Markov models. The Markov model and approximations are validated with accurate system simulations. We also provide numerical results illustrating the non-convex relations between the traffic metrics and system parameters and that the proposed models are essential for optimal OSA network planning and operation. We further analyse and discuss the OSA queueing model for general distribution of service time and availability periods. The analytical and simulation results indicate that for usual system parameters, the queue average occupancy is similar for different distributions of service time and availability periods and that the memoryless Markov models can be used to accurately predict the heterogeneous OSA system traffic performance.

Priority queueing models for cognitive radio networks with traffic differentiation

In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and used to illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation.

Opportunistic Spectrum Access Using Partially Overlapping Channels: Graphical Game and Uncoupled Learning

This article investigates the problem of distributed channel selection in opportunistic spectrum access (OSA) networks with partially overlapping channels (POC) using a game-theoretic learning algorithm. Compared with traditional non-overlapping channels (NOC), POC can increase the full-range spectrum utilization, mitigate interference and improve the network throughput. However, most existing POC approaches are centralized, which are not suitable for distributed OSA networks. We formulate the POC selection problem as an interference mitigation game. We prove that the game has at least one pure strategy NE point and the best pure strategy NE point minimizes the aggregate interference in the network. We characterize the achievable performance of the game by presenting an upper bound for aggregate interference of all NE points. In addition, we propose a simultaneous uncoupled learning algorithm with heterogeneous exploration rates to achieve the pure strategy NE points of the game. Simulation results show that the heterogeneous exploration rates lead to faster convergence speed and the throughput improvement gain of the proposed POC approach over traditional NOC approach is significant. Also, the proposed uncoupled learning algorithm achieves satisfactory performance when compared with existing coupled and uncoupled algorithms.

Mitigating spectrum sensing data falsification attacks in hard-decision combining cooperative spectrum sensing

In this paper we propose a simple yet robust secure cooperative spectrum sensing scheme with hard decision combining for Opportunistic Spectrum Access (OSA) networks. Based on the historical sensing records and the assistance of trusted OSA nodes, the proposed approach effectively mitigates spectrum sensing data falsification attacks by excluding misbehaving OSA nodes from the process of cooperation. Both analyses and numerical results are provided for various performance metrics, including overall detection probability. Comparison of the proposed method with existing well-known secure spectrum sensing methods verifies the robustness and effectiveness of the proposed scheme.

PERFORMANCE ANALYSIS OF COGNITIVE NETWORK WITH PRIMARY AND SECONDARY CHANNELS

Abstract. We consider a cognitive network with a primary and a sec-ondary channel. Primary users have higher priority on the usage of theprimary channel, and secondary users are allowed to opportunisticallyaccess the primary channel at times when the channel is not occupiedby primary users. The secondary channel is dedicated only to secondaryusers. An analytical model is presented to obtain the performance ofan opportunistic spectrum access using both the primary and secondarychannels, and is validated by simulations. 1. IntroductionReports of spectrum eciency reveal that a considerable region of the spec-trum remains unused. As a solution for such inecient spectrum usage, op-portunistic spectrum access (OSA) has been intensively studied. Under thissystem, a secondary user that does not have a license to use the spectrum isallowed to opportunistically occupy an idle spectrum band owned by a licenseethat is termed by the primary user [1, 2]. Much attention has been paid to thequality-of-service of secondary users. Wang et al. [3] proposed two cognitiveMAC schemes to support voice services in the presence of primary users. Lee[4] developed a simple approximate model for unslotted OSA networks undernon-saturation conditions. Considering prioritization among secondary users,Lee [5] proposed an OSA scheme with channel reservation for high prioritysecondary users and bu ering for low priority secondary users.This paper considers a cognitive network with a primary and a secondarychannel. Primary users have higher priority on the usage of the primary chan-nel, and secondary users are allowed to opportunistically access the primarychannel at times when the channel is not occupied by primary users. Thesecondary channel is dedicated only to secondary user. The main objective of

An Improved Algorithm of Spectrum Access in OSA

We present a distributed spectrum sensing and access algorithm applied in opportunistic spectrum access (OSA) networks. It is proposed in the condition of cognitive radio (CR) users’ correct spectrum sensing. In each slot, a CR user will decide whether to sense, which channel to sense, and whether to access. However, due to the hardware and energy constraints, the CR users may not be able to sense or monitor all the channels, so we design this access algorithm derived from the theory of Partially Observable Markov Decision Process (POMDP). To simplify the analysis, we assume that the CR users will sense and access only one channel. Simulation results show that the proposed access algorithm can greatly improve the throughput of CR users than the random access algorithm. It has good access performance, and can quickly achieve stable access throughput as well. It is shown that with greater channel transition probability difference the CR users will obtain higher access throughput, and when the difference equals to zero this access algorithm becomes random access algorithm.

When Channel Bonding is Beneficial for Opportunistic Spectrum Access Networks

Transmission over multiple frequency bands combined into one logical channel speeds up data transfer for wireless networks. On the other hand, the allocation of multiple channels to a single user decreases the probability of finding a free logical channel for new connections, which may result in a network-wide throughput loss. While this relationship has been studied experimentally, especially in the WLAN configuration, little is known on how to analytically model such phenomena. With the advent of Opportunistic Spectrum Access (OSA) networks, it is even more important to understand the circumstances in which it is beneficial to bond channels occupied by primary users with dynamic duty cycle patterns. In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control layer for OSA networks with channel bonding enabled. We show that channel bonding is generally beneficial, though the extent of the benefits depend on the features of the OSA network, including OSA network size and the total number of channels available for bonding. In addition, we show that performance benefits can be realized by adaptively changing the number of bonded channels depending on network conditions. Finally, we evaluate channel bonding considering physical layer constraints, i.e. throughput reduction compared to the theoretical throughput of a single virtual channel due to a transmission power limit for any bonding size.

Application of UWB Technology for Underlay Signaling in Cognitive Radio Networks

Opportunistic Spectrum Access (OSA) is a promising solution to increase spectral efficiency in Cognitive Radio (CR) networks. According to this paradigm, unlicensed secondary CR users can access the spectrum licensed to primary users, provided that harmful interference is avoided. However, in OSA networks secondary users need a Common Control Channel (CCC) to identify the spectrum opportunities, i.e., common spectrum holes unused by primary users. To fulfill this requirement, the sharing of the spectrum sensing information via Ultra-wideband (UWB) has been proposed in the literature WRAP. In this paper, we summarize the main results we obtained on the design and dimensioning of a highly available underlay UWB signaling network. To this aim, we analyse the trade-off between the connectivity degree of a multi-hop underlay signaling network, related to the parameters of the CCC, and its coexistence with PUs. We also assess the impact of the availability and the characteristics of the CCC on the accuracy of PU detection in OSA networks. Results show that the correct dimensioning of the UWB signaling network allows to achieve high accuracy of PU detection while avoiding harmful interference. The review also discusses relevant patents on this topic.

Finding Cheap Routes in Profit-Driven Opportunistic Spectrum Access Networks: A Truthful Mechanism Design Approach

In this paper, we explore the economic aspects of routing/relaying in a profit-driven opportunistic spectrum access (OSA) network. In this network, primary users lease their licensed spectrum to secondary radio (SR) providers, who in turn provide opportunistic routing/relaying service to end-users if this service is profitable, i.e., if the payment offered by the end-user (a.k.a. the price) exceeds the SR's relaying spectrum cost. This cost is considered private information known only to SRs. Therefore, the end-user has to rely on costs reported by SRs to determine his routing and payment strategy. The challenge comes from the selfish nature of SRs; an SR may exaggerate his cost to achieve greater profit. To give incentive to an SR to report the true cost, the payment must typically be higher than the actual cost. However, from the end-user's perspective, “overpayment” should be avoided as much as possible. Therefore, we are interested in the “optimal” route selection and payment determination mechanism that minimizes the price of the selected route while simultaneously guaranteeing truthful cost reporting by SRs. We formulate this problem as finding the least-priced path (LPP), and we investigate it without and with link capacity constraints. In the former case, polynomial-time algorithm is developed to find LPP and calculate its truthful price. In the latter case, we show that calculating the truthful price of the LPP is in general computationally infeasible. Consequently, we consider a suboptimal but computationally feasible approximate solution, which we refer to as truthful low-priced path (LOPP) routing. A polynomial-time algorithm is proposed to find the LOPP and efficiently calculate its truthful price. A payment materialization algorithm is also developed to guarantee truthful capacity reporting by SRs. The effectiveness of our algorithms in terms of price saving is verified through extensive simulations.

Throughput and Collision Analysis of Multichannel Multistage Spectrum Sensing Algorithms

Multi-stage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The contribution of our work lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, primary and secondary user traffic, buffering of incoming secondary user traffic, as well as MAC layer sensing algorithms. Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is, in most cases, superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing provides a good balance between secondary users throughput and collision probability while meeting successful detection constraints subjected by Opportunistic Spectrum Access communication.

Optimal Detection of Spatial Opportunity in Wireless Networks

The scarceness of the radio spectrum leads to opportunistic spectrum access or cognitive radio. In this letter, we analyze the spectrum sensing threshold to improve reliability of opportunity detection in the spatial domain. We minimize the weighted sum of exposed- and hidden node probabilities, and derive the optimal spectrum sensing threshold as a closed form formula. We take the estimation error into account, which is parameterized into the sensing threshold. Our analytic results will provide a fundamental framework for opportunistic spectrum access networks or wireless LANs.

Modelling and analysis of nonsaturated opportunistic spectrum access networks

An analytic model for an unslotted opportunistic spectrum access network under nonsaturation traffic conditions is proposed and its performance, such as MAC access delay and system delay of secondary packets, is analysed. Numerical examples are given to show the performance of secondary users in cognitive radio networks.

MODELING AND ANALYSIS FOR OPPORTUNISTIC SPECTRUM ACCESS

We present an analytic model of an unslotted opportunistic spectrum access (OSA) network and evaluate its performance such as interruption probability, service completion time, and throughput of secondary users. Numerical examples are given to show the performance of secondary users in cognitive networks. The developed modeling and analysis method can be used to evaluate the performance of various OSA networks.