Rendezvous methods for opportunistic spectrum access in cognitive wireless networks: Analysis and implementation

Motivated by the low utilization of the licensed spectrum across many frequency bands, sensing-based oppor- tunistic spectrum access has recently emerged as an alternative to the outdated exclusive spectrum access policy. Under this new paradigm, a secondary (unlicensed) user monitors a primary (licensed) frequency band for a given and opportunistically transmits if it does not detect any ongoing licensed operations. Evidently, selection of the parameters involves balanc- ing a tradeoff between the speed and the quality with which the secondary user senses the licensed band. With the average throughput as the performance criterion, we obtain the parameters so as to optimize the performance of the secondary user while providing the primary user with its desired level of interference protection. I. INTRODUCTION As evidenced by recent measurements, many frequency bands across the licensed spectrum are significantly under- utilized (1), (2). This finding suggests that the spectrum scarcity, as perceived today, is largely due to the inefficient fixed frequency allocations rather than the physical shortage of the spectrum and has led the regulatory bodies to consider the opportunistic access to the temporally/spatially unused licensed bands (a.k.a. the white spaces) as a means to improve the efficiency of spectrum usage. In the absence of cooperation or signalling between the primary licensee and the secondary users, spectrum availability for the opportunistic access may be determined by direct spectrum where the secondary user monitors a licensed band for a given sensing time and opportunistically transmits if it does not detect any ongoing licensed operations. This approach is particularly appealing due to its low deployment cost and its compatibility with legacy primary users and is being considered for inclusion in the upcoming IEEE 802.22 standard for opportunistic access to the TV spectrum (3). Due to their ability to autonomously detect and to react to the changes in the spectrum usage, secondary users equipped with the spectrum capability may be considered as a primitive form of the cognitive radio (4). Design of any scheme involves balancing a tradeoff between the quality and the speed of through an appropriate selection of the time. As we shall illustrate, in the context of spectrum sensing, may be fine- tuned to enhance the secondary users' perceived quality-of- service (QoS) as long as the regulatory constraint for the protection of the primary users against harmful interference is satisfied. In particular, we will obtain the optimum times at different stages of the spectrum to maximize the average throughput of the secondary user. In this paper, simple energy detection (a.k.a. radiometry) (5) is chosen as the underlying detection scheme. In general, when some information about the structure of the primary signal is available, ad hoc feature-detectors offer a better performance (6). We note, however, that the methodology employed in this paper may be applied to optimize different spectrum sensors once the quality is characterized in terms of the time. The remainder of this paper is organized as follows. The regulatory constraints on spectrum are described in the following section. Section 3 provides an overview of the energy-based spectrum sensing. The optimum times for different stages of the spectrum are derived in Section 4. Finally, this paper is concluded in Section 5.

In this paper, we consider medium access control (MAC) protocol design for random-access cognitive radio (CR) networks. A two-level opportunistic spectrum access strategy is proposed to optimize the system performance of the secondary network and to adequately protect the operation of the primary network. At the first level, secondary users (SUs) maintain a sufficient detection probability to avoid interference with primary users (PUs), and the spectrum sensing time is optimized to control the total traffic rate of the secondary network allowed for random access when the channel is detected to be available. At the second level, two MAC protocols called the slotted cognitive radio ALOHA (CR-ALOHA) and cognitive-radio-based carrier-sensing multiple access (CR-CSMA) are developed to deal with the packet scheduling of the secondary network. We employ normalized throughput and average packet delay as the network metrics and derive closed-form expressions to evaluate the performance of the secondary network for our proposed protocols. Moreover, we use the interference and agility factors as the performance parameters to measure the protection effects on the primary network. For various frame lengths and numbers of SUs, the optimal performance of throughput and delay can be achieved at the same spectrum sensing time, and there also exists a tradeoff between the achievable performance of the secondary network and the effects of protection on the primary network. Simulation results show that the CR-CSMA protocol outperforms the slotted CR-ALOHA protocol and that the PUs' activities have an influence on the performance of SUs for both the slotted CR-ALOHA and CR-CSMA.

This paper presents optimization of secondary user (SU) access in sensing-transmission structure for dynamic spectrum access in cognitive radios. In opportunistic spectrum access, the white spaces in the transmissions of primary users (PUs) have to be sensed and utilized by SUs. There is a continuous tradeoff between the competing goals of PU transmission protection and SU access maximization. A threshold based sensing-transmission structure is developed. To maximize the SU access its reward is optimized with respect to collision cost using simulations. The results are presented in the form of traces of thresholds for different reward values and collision costs. This scheme optimizes SU throughput while safeguarding PU interests. It sets a standard and gives an insight for the design of sensing-transmission scheduling in cognitive radio networks such as IEEE 802.2.

With tremendous growth in wireless services, the demand for radio spectrum has significantly increased. However, spectrum resources are scarce and most of them have been already licensed to existing operators. Recent studies have shown that despite claims of spectral scarcity, the actual licensed spectrum remains unoccupied for long periods of time. Thus, cognitive radio (CR) systems have been proposed in order to efficiently exploit these spectral holes, when licensed primary users (PUs) are not present. CRs or secondary users (SUs) are wireless devices that can intelligently monitor and adapt to their environment, hence, they are able to share the spectrum with the licensed PUs, operating when the PUs are idle. In this chapter, we discuss spectrum sensing and access in cognitive radio networks. Depending on the nature of the activities in the network, different categories of MAB problems have been formulated. Solutions and regret bounds are provided.

In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. For secondary users, sensing costs are incurred as the result of primary users' activity. Furthermore, each secondary user pays the cost of transmission upon successful bidding for a channel. Knowledge regarding other secondary users' activity is limited due to the distributed nature of the network. The resulting formulation is thus a dynamic game with incomplete information. To solve such a problem, a Bayesian nonparametric belief update scheme is constructed based on the Dirichlet process. Efficient bidding learning algorithms are proposed via which users can decide whether or not to participate in the bidding according to the belief update. Properties of optimal bidding and initial bidding are proved. As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good trade-off between long-term efficiency and fairness.

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow secondary users (SUs) to transmit only when the licensed channel is not occupied by primary users (PUs). However, waiting for an idle time slot may lead to large packet delays and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (4G) in order to transmit his packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets’ delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.

Quality of experience (QoE) of a secondary spectrum user is mainly governed by its spectrum utilization, the energy consumption in spectrum sensing and the impact of channel switching in a cognitive radio network. It can be enhanced by prediction of spectrum availability of different channels in the form of their idle times through historical information of primary users’ activity. Based on a reliable prediction scheme, the secondary user chooses the channel with the longest idle time for transmission of its data. In contrast to the existing method of statistical prediction, the use and applicability of supervised learning based prediction in various traffic scenarios have been studied in this paper. Prediction accuracy is investigated for three machine learning techniques, artificial neural network based Multilayer Perceptron (MLP), Support Vector Machines (SVM) with Linear Kernel and SVM with Gaussian Kernel, among which, the best one is chosen for prediction based opportunistic spectrum access. The results highlight the analysis of the learning techniques with respect to the traffic intensity. Moreover, a significant improvement in spectrum utilization of the secondary user with reduction in sensing energy and channel switching has been found in case of predictive dynamic channel allocation as compared to random channel selection.

In this paper, we tackle the problem of opportunistic spectrum access in cognitive radio networks where a number of unlicensed Secondary Users (SU) operating on the standard CSMA/CA protocol access a number of frequency channels partially occupied by licensed Primary Users (PU). We apply evolutionary game theory to model the spectrum access problem and derive distributed mechanisms to converge to the Nash equilibrium. To this end, we combine a payoff computation methodology, relying on the estimation on the number of SUs on the same channel, with the channel access policy derived by the evolutionary game model. The conducted numerical analysis shows that a fast convergence is achieved and the proposed mechanisms are robust against errors in payoff computation.

Opportunistic channel access in cognitive radio networks (CRNs) under an unknown environment is gradually receiving a great deal of attention. This article studies the basic problem of decentralized secondary users (SUs) performing multiple channel sensing and access in CRN, when sensing is not perfect. The channel availability information is unknown and must be estimated and learned through big-data samples from the wireless channels by SUs. Both the independent identical distribution (i.i.d.) channel model and the Markov channel model are considered. In the i.i.d. model, the availability of each channel is modeled as an i.i.d. process, while in the Markov model, the availability of each channel is set as a Markov chain with an unknown probability transition matrix. If multiple SUs access to the identical channel, collision will occur and none of SUs gets a reward. Learning loss, which is also referred to as regret, is thus inevitable. To handle with the sampling data on large scale, we formulate the channel sensing and access process as a multi-armed bandit problem (MABP), based on which big-data driven online algorithms are proposed. The theoretical analysis and simulations prove that the regret of our algorithms is both logarithmic in finite time and asymptotically.

Market mechanisms have been exploited as important means for spectrum acquisition and access in cognitive radio networks. In this paper, we propose a two-tier market for decentralized dynamic spectrum access. In the proposed Tier-1 market, spectrum are traded from a primary user~(PU) to secondary users~(SUs) in a relatively large time scale to reduce signaling overhead. Then driven by dynamic traffic demands, SUs set up the Tier-2 market to redistribute channels among themselves in a small time scale. More specifically, we use Nash bargain game to model the spectrum acquisition of SUs in the Tier-1 market and derive the equilibrium prices. We then use strategic bargain game to study the spectrum bargain in the Tier-2 market, where SUs can exchange channels with low overhead through random matching, bilateral bargain, and the predetermined market equilibrium prices. We disclose how various factors, such as availability of channels and bargain partners, matching schemes, and traffic dynamics, impact the market relationships. This work provides better understanding on the spectrum market and valuable guidelines to the primary and secondary network operators.

As an effective approach to improve spectrum efficiency, cognitive radio network make it possible for secondary users (SU) to share the spectrum with primary users (PU), on the condition that the primary users have preemptive priority. In this paper, we applied the Dynamic Chinese restaurant game, which ideally modeled the spectrum sensing and access in cognitive network. We propose the use of Multilayer Feed-forward Neural Networks (MFNN) as an effective method for users to learn the network state, which can be regarded as how a customer learn the table state in the restaurant. Moreover, in order to select an optimal table for a secondary user, subsequent secondary users' sequential decisions are considered. The effectiveness and efficiency of the proposed scheme is verified in the simulation.

The specificity of overlay spectrum sharing in cognitive radio network, is that the secondary user has to handoff and vacate immediately the channel once the Primary User appears and reclaims it. Such handoff becomes more frequent specially in case of multichannel access since that in a wider band, a PU is likely to reclaim a part of the band more quickly, and the SU has to switch to a different set of channels. However, a frequent handoff may alter the quality of the communication and generate an additional delay since that for each channel switching a sensing phase has to take place, additionally to channel evacuation and link set up times. In this paper we propose a Holding Time based access model in order to avoid such handoff. The effectiveness of this model is investigated within a Common Pilot Channel framework.

We consider a non-cooperative Opportunistic Spectrum Access (OSA) where Secondary Users (SUs) access opportunistically the spectrum licensed for Primary Users (PUs) in TV white spaces (TVWS). As sensing licensed channels is time and energy consuming, we consider a hierarchical Cognitive Radio (CR) architecture, where CR base stations sense a subset of the spectrum in order to locate some free frequencies. Thereafter, a SU that needs to communicate through TVWS sends a request to a CR base station for a free channel. We model the problem using a Partially Observable Stochastic Game (POSG), and we take into consideration the energy consumption of CR base stations and the Quality of Services (QoS) of SUs. Since solving POSG optimally may require a significant amount of time and computational complexity, we model the OSA problem using a game theoretical approach, and we propose a symmetric Nash equilibrium solution concept. Finally, we provide some simulations that validate our theoretical findings.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> With the current proliferation of high bandwidth and delay-sensitive multimedia applications and services, each wireless user will try to maximize its utility by acquiring as much spectrum resources as possible unless a preemptive mechanism exists in the network. Thus, emerging solutions for dynamic spectrum access in cognitive radio networks will need to adopt market-based approaches in order to effectively regulate the available resources. In this paper, we show how various centralized and decentralized spectrum access markets can be designed based on a stochastic game framework, where wireless users (also referred to as secondary users) can compete over time for the dynamically available transmission opportunities (spectrum “holes”). When operating in such spectrum access “markets,” wireless users become selfish, autonomous agents that strategically interact in order to acquire the necessary spectrum opportunities. We also show how wireless users can successfully compete with each other for the limited and time-varying spectrum opportunities, given the experienced dynamics in the wireless network, by optimizing both their external actions (e.g., the resource bids, power and channel used for transmission, etc.) and their internal actions (e.g., the modulation schemes, etc.). To determine their optimal actions in an informationally decentralized setting, users will need to learn and model directly or indirectly the other users' responses to their external actions. We studied the outcome of various dynamic interactions among self-interested wireless users possessing different knowledge and determine that the proposed framework can lead to multiuser communication systems that achieve new measures of efficiency, rationality and fairness. Lastly, our illustrative results show that the presented game-theoretic solution for wireless resource management enables users deploying enhanced (“smarter”) learning and communication algorithms and being able to make efficient use of the spectrum resources can derive higher utilities. This presents the designers of wireless devices and systems with important incentives to endow their next-generation products and services with enhanced capabilities to gather information, learn, and strategically compete for resources in the emerging spectrum resource markets made possible by the cognitive radio network technologies. </para>

Competitive spectrum access is studied for cognitive radio networks. Based on the assumption of rational secondary users, the spectrum access is modeled as a graphical game, in which the payoff of a secondary user is dependent on only other secondary users that can cause significant interference. The Nash equilibrium in the graphical game is computed by minimizing the sum of regrets. To alleviate the local knowledge of payoffs (each secondary user knows only its own payoff for different channels), a subgradient based iterative algorithm is applied by exchanging information across different secondary users. When information exchange is not available, learning for spectrum access is carried out by employing stochastic approximation (more specifically, the Kiefer-Wolfowitz algorithm). The convergence of both situations is demonstrated by numerical simulations.