Opportunistic Spectrum Access System Research Articles

Opportunistic Spectrum Access with Dynamic Users: Directional Graphical Game and Stochastic Learning

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.

TSRA: An Adaptive Mechanism for Switching between Communication Modes in Full-Duplex Opportunistic Spectrum Access Systems

Full-duplex (FD) communications and self-interference suppression (SIS) techniques can be exploited in opportunistic spectrum access (OSA) systems for simultaneous transmission-sensing (TS) or simultaneous transmission-reception (TR). Motivated by the competing goals of primary user (PU) protection (in the TS mode) and secondary user (SU) performance (in the TR mode), we present an optimal adaptive switching strategy and an associated communication protocol for FD OSA systems. Specifically, we optimize the spectrum-awareness/efficiency tradeoff by allowing the SU link to adaptively switch between various modes, depending on the forecasted PU dynamics. The proposed three-stage adaptive mode-selection strategy maximizes an SU utility function subject to a constraint on the PU collision probability. We also propose a protocol that executes the switching mechanism in a distributed fashion. In practice, SIS is imperfect, resulting in residual self-interference that degrades the sensing performance in the TS mode. Accordingly, we study different spectrum sensing techniques in the TS mode, while illustrating their accuracy-complexity tradeoff. We evaluate the performance of the proposed switching scheme against the listen-before-talk (LBT) scheme using numerical results, simulations, and hardware USRP experiments.

A discrete-time multi-server queueing model for opportunistic spectrum access systems

In opportunistic spectrum access communication systems, secondary users (SUs) exploit the spectrum holes not used by the primary users (PUs) and cease their transmissions whenever primary users reuse their spectrum bands. To study the mean time an SU spends in the system we propose a discrete-time multi-server access model. Since periodic sensing is commonly used to protect the PU, discrete-time models are more convenient to analyze the performance of the SU system. Additionally, a multi-server access model is assumed in order to give the SU the capability to access a channel that is not occupied by a PU or any other SUs. We derive the probability generating function of the number of connections in the system. Then we derive a formula for the mean response time of an SU. In the numerical results we show the relationship between the mean response time and the SU traffic intensity. In addition, we show the effect of changing the number of channels in the system and the PU traffic intensity on the mean response time of an SU. Finally, simulation results are shown to validate the proposed theoretical model and reveal that the numerical results fit the simulated results well.

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.

Analysis of opportunistic spectrum access in cognitive radio networks using hidden Markov model with state prediction

We propose a study using hidden Markov model (HMM) with state prediction for opportunistic spectrum access (OSA) in cognitive radio (CR) networks. The primary channels are assumed to be operating in a TDMA manner with synchronous time slots of equal length and alternating between idle and busy states. The secondary users (SUs) may use them when they are idle by channel sensing. In contrast to the traditional scheme relying only on channel sensing for exploring spectrum opportunities, the proposed prediction scheme takes advantage of state prediction, channel sensing, and acknowledgments (ACKs) from the receiver in an attempt to maximize the utility. In the prediction scheme, there are three distinct actions: direct skip, sensing then conditional access, and direct access. We impose some constraints on the system parameters and derive thresholds by which we can specify the optimal action. We then conduct simulations to compare the performance of the prediction scheme to that of the traditional scheme. Results show that the former is superior to the latter. We believe the proposed prediction scheme is suitable for the OSA system using spectrum sensing.

Multi-User Opportunistic Spectrum Access Using Reinforcement Learning

This paper studies the channel exploration problem for the distributed opportunistic spectrum access (D-OSA) system, where multiple secondary users (SUs) sequentially sense multiple licensed channels and utilize one of idle channel. However, channel sensing order can affect the system performance seriously. When using a better sensing order, the SU can find faster a free channel with high quality and the less collisions among SUs can happen. In this paper, we propose a mechanism using reinforcement learning to find dynamically out a sensing order for improving the system performance. In the proposed mechanism, the interactions among SUs are considered. Simulation results are provided to show the effectiveness of the proposed mechanism and the significant improvement of the system performance.

An Adaptive Stopping Rule for Distributed Opportunistic Spectrum Access Systems with Multi-channel and Multi-user

This paper studies the channel exploration problem for the distributed opportunistic spectrum access (D-OSA) systems, where multiple secondary users (SUs) sequentially sense multiple licensed channels and access one of idle channels. SUs operate on a frame-by-frame basis and need to sense channels at the beginning of each frame. According to the stopping rule, SUs determine to stop at the sensed channel or continue to sense next channels. However, the optimal design in the scenario of multiple SUs with the multiple licensed channels is difficult to realize for D-OSA systems. To solve this problem, we introduce an adjusted function considering the interactions among SUs, and then propose an adaptive stopping rule to improve the performance of the multi-user system. Simulation results are provided to show the effectiveness of the adaptive stopping rule and the significant improvement of the throughput performance.

Opportunistic Spectrum Access with Spatial Reuse: Graphical Game and Uncoupled Learning Solutions

This article investigates the problem of distributed channel selection for opportunistic spectrum access systems, where multiple cognitive radio (CR) users are spatially located and mutual interference only emerges between neighboring users. In addition, there is no information exchange among CR users. We first propose a MAC-layer interference minimization game, in which the utility of a player is defined as a function of the number of neighbors competing for the same channel. We prove that the game is a potential game with the optimal Nash equilibrium (NE) point minimizing the aggregate MAC-layer interference. Although this result is promising, it is challenging to achieve a NE point without information exchange, not to mention the optimal one. The reason is that traditional algorithms belong to coupled algorithms which need information of other users during the convergence towards NE solutions. We propose two uncoupled learning algorithms, with which the CR users intelligently learn the desirable actions from their individual action-utility history. Specifically, the first algorithm asymptotically minimizes the aggregate MAC-layer interference and needs a common control channel to assist learning scheduling, and the second one does not need a control channel and averagely achieves suboptimal solutions.

Efficient Objective Functions for Coordinated Learning in Large-Scale Distributed OSA Systems

In this paper, we derive and evaluate private objective functions for large-scale, distributed opportunistic spectrum access (OSA) systems. By means of any learning algorithms, these derived objective functions enable OSA users to assess, locate, and exploit unused spectrum opportunities effectively by maximizing the users' average received rewards. We consider the elastic traffic model, suitable for elastic applications such as file transfer and web browsing, and in which an SU's received reward increases proportionally to the amount of received service when the amount is higher than a certain threshold. But when this amount is below the threshold, the reward decreases exponentially with the amount of received service. In this model, SUs are assumed to be treated fairly in that the SUs using the same band will roughly receive an equal share of the total amount of service offered by the band. We show that the proposed objective functions are: near-optimal, as they achieve high performances in terms of average received rewards; highly scalable, as they perform well for small- as well as large-scale systems; highly learnable, as they reach up near-optimal values very quickly; and distributive, as they require information sharing only among OSA users belonging to the same band.

Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions

Opportunistic spectrum access (OSA) has been regarded as the most promising approach to solve the paradox between spectrum scarcity and waste. Intelligent decision making is key to OSA and differentiates it from previous wireless technologies. In this article, a survey of decision-theoretic solutions for channel selection and access strategies for OSA system is presented. We analyze the challenges facing OSA systems globally, which mainly include interactions among multiple users, dynamic spectrum opportunity, tradeoff between sequential sensing cost and expected reward, and tradeoff between exploitation and exploration in the absence of prior statistical information. We provide comprehensive review and comparison of each kind of existing decision-theoretic solution, i.e., game models, Markovian decision process, optimal stopping problem and multi-armed bandit problem. We analyze their strengths and limitations and outline further research for both technical contents and methodologies. In particular, these solutions are critically analyzed in terms of information, cost and convergence speed, which are key concerns for practical implementation. Moreover, it is noted that each kind of existing decision-theoretic solution mainly addresses one aspect of the challenges, which implies that two or more kinds of decision-theoretic solutions should be incorporated to address more challenges simultaneously.

The Effectiveness of Opportunistic Spectrum Access: A Measurement Study

Dynamic spectrum access networks are designed to allow today's bandwidth-hungry to share spectrum allocated to legacy devices, or The success of this wireless communication model relies on the availability of unused spectrum and the ability of secondary devices to utilize spectrum without disrupting transmissions of primary users. While recent measurement studies have shown that there is sufficient underutilized spectrum available, little is known about whether secondary devices can efficiently make use of available spectrum while minimizing disruptions to primary users. In this paper, we present the first comprehensive study on the presence of usable spectrum in opportunistic spectrum access systems, and whether sufficient spectrum can be extracted by secondary devices to support traditional networking applications. We use for our study fine-grain usage traces of a wide spectrum range (20 MHz-6 GHz) taken at four locations in Germany, the Netherlands, and Santa Barbara, CA. Our study shows that on average, 54% of spectrum is never used and 26% is only partially used. Surprisingly, in this 26% of partially used spectrum, secondary devices can utilize very little spectrum using conservative access policies to minimize interference with primary users. Even assuming an optimal access scheme and extensive statistical knowledge of primary-user access patterns, a user can only extract between 20%-30% of the total available spectrum. To provide better spectrum availability, we propose frequency bundling, where secondary devices build reliable channels by combining multiple unreliable frequencies into virtual frequency bundles. Analyzing our traces, we find that there is little correlation of spectrum availability across channels, and that bundling random channels together can provide sustained periods of reliable transmission with only short interruptions.

Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.

Effective capacity region of two-user opportunistic spectrum access

The effective capacity region of the two-user opportunistic spectrum access (OSA) system in Rayleigh fading environment is derived in this paper. Although OSA is a contemporary research topic, little attention has been given to the capacity analysis under quality-of-service (QoS) constraints. Hence, we use the effective capacity, which is a powerful tool to analyze the QoS requirements in wireless communication systems, to study the sustainable packet arrival rate of the secondary users while meeting statistical QoS constraints. We derive the effective capacity region of the two-user OSA system and propose a method to obtain the capacity region boundary. In addition, by combining the finite state channel model and the activity of the primary user, an extended finite state channel model for the two-user OSA system is also proposed.

Discrete-time Queuing Analysis of Opportunistic Spectrum Access: Single User Case

This article studies the discrete-time queuing dynamics of opportunistic spectrum access (OSA) systems, in which the secondary user seeks spectrum vacancies between bursty transmissions of the primary user to communicate. Since spectrum sensing and data transmission can not be performed simultaneously, the secondary user employs a sensing-then-transmission strategy to detect the presence of the primary user before accessing the licensed channel. Consequently, the transmission of the secondary user is periodically suspended for spectrum sensing. To capture the discontinuous transmission nature of the secondary user, we introduce a discrete-time queuing subjected to bursty preemption to describe the behavior of the secondary user. Specifically, we derive some important metrics of the secondary user, including secondary spectrum utilization ratio, buffer length, packet delay and packet dropping ratio. Finally, simulation results validate the proposed theoretical model and reveal that the theoretical results fit the simulated results well.

An optimal OSA approach based on channel-usage estimate under collision probability constraint in cognitive radio systems

Opportunistic spectrum access system allows the secondary user to access spectrum holes not being utilized by the primary user. Traditional opportunistic spectrum access approaches only sense and utilize current spectrum holes. This can result in uncontrollable collision probability, which exceed the maximum collision probability allowed by the primary user network. In this paper, we consider a cognitive radio system with one primary channel and one secondary user, and then, we introduce a channel-usage pattern model and a fundamental access scheme in this system. Based on the fundamental access scheme, we adopt fixed detection duration and transmission duration ratio approach to analyze what and how to determine spectrum holes utilization and collision probability in this model. On the basis of this model and fundamental access scheme, we study optimal opportunistic spectrum access problem and formulate it as an optimization problem that the secondary user maximizes spectrum holes utilization under the constraint of collision tolerable level, and then we solve this optimization problem in two cases: one is that the idle period is exponential distribution, the other is that the idle period is Pareto distribution. According to the solution of the optimization problem, we respectively propose an optimal opportunistic spectrum access algorithm in each case. Theoretical analysis and simulation results both show that the optimal opportunistic spectrum access algorithms can maximize spectrum holes utilization under the constraint that the collision probability is bounded below collision tolerable level.