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Modeling and analysis of system dynamics and state estimation in cognitive radio networks
Abstract
Cognitive radio networks (CRN) are subject of a multidisciplinary research in wireless networking, digital communications, artificial intelligence, and other fields. Special emphasis is given to investigation of the CRN capability of improved RF spectrum utilization by means of dynamic spectrum access (DSA). This paper proposes a new analytic framework for modeling the adaptive behavior of CRN. In particular, a model of the CRN is developed which incorporates sensing of the electromagnetic environment, estimating states of the communication channels, making decisions based on some policy and dynamically switching the transmissions to the channels that are not currently occupied by primary users. The proposed framework is analyzed using mathematical tools of queueing theory and the performance of the CRN is evaluated. We show the impact of sensing rate and the system dynamics on the waiting times of secondary users.
... Although this model allows an analytic study of CRN performance, it lacks the modeling of the cognition cycle. A basic version of cognition cycle model is given in our previous work [9]. This paper presents three significant contributions to the problem of modeling the CRN. ...
... This paper presents three significant contributions to the problem of modeling the CRN. Firstly, we enhance the model of [9] by introducing CRN with penalty for interfering PU. The penalty provides an incentive for CRN to enhance its perception level in order to avoid interference with PU, which is an essential requirement in any realistic scenario. ...
... This process forms a three dimensional CTMC illustrated in Fig. 2, which is homogeneous, irreducible and stationary. The exact structure of transitions within the CTMC is constructed in the same manner as in [9], [13]. Next, matrix-geometric approach is applied to calculate the steady state probabilities of CTMC. ...
In this paper we consider cognitive processes and their impact on the performance of cognitive radio networks (CRN). We model the cognition cycle, during which cognitive radio (CR) sequentially senses and estimates the environment state, makes decisions in order to optimize certain objectives and then acts. Model-based analysis of CRN is used to solve control and decision making tasks, which actually gives the radio its "cognitive" ability. Particularly, we design an efficient strategy for accessing the vacant spectrum bands and managing the transmission-sampling trade-off. In order to cope with the high complexity of this problem the policy search uses the stochastic optimization method of cross-entropy. The developed model represents CRN ability to intelligently react to the network's state changes and gives a good understanding of the cross-entropy optimized policies.
... We define {X t ,S t ,Ŝ t } to be the process of the entire system for which at time t there are X t (X t ∈{0,1,2,…}) queued packets in CR's buffer. This process forms a three dimensional CTMC, exact structure and analysis of which were studied in [16], [17]. Similar guidelines are used here to compare the performance of the prediction-based and the reactive operation modes. ...
... Namely, we compare the performance of reactive CR, proactive (prediction-based) CR, and ultimate CR. The last model is fully analyzed in [16]. Reactive CR, is also analytically tractable by same means. ...
A prediction-based spectrum access for cognitive radio (CR) is considered. In a typical scenario, CR accesses the communication channels as a secondary user (SU), under the requirement for collisions avoidance with the primary users (PU). In order to meet this requirement, CR frequently senses the communication channels and estimates their availability. Generally, sensing and transmission processes are mutually exclusive, which turns the sensing time into an overhead. Therefore, CR has a strong incentive to predict the future availability of the channels. Consequently, by sensing less, CR is able to increase the proportion of time allocated for the transmissions. We propose a novel analytical model of CR which consists of communication processes and cognitive components. The communication processes include packet arrivals, queueing and transmissions, while the cognitive components include sensing, estimation, multi-step ahead prediction of the channels' states and a component of decision-making. The prediction based policies show a significant improvement in the performance measures of throughput and delay compared to the reactive behavior.
... Although this model allows an analytic study of CRN, it lacks the modeling of the cognition cycle as it neglects the phase of environment sensing and its state estimation. A basic version of cognition cycle model is given in our previous work [19]. However it lacks the sensing-transmission tradeoff and penalty for interference with PU. ...
... This paper presents three significant contributions to the problem of modeling the CRN. Firstly, we enhance the model of [19] by introducing CRN with penalty for interfering PU. The penalty provides an incentive for CRN to enhance its perception level in order to avoid interference with PU, which is an essential requirement in any realistic scenario. ...
In this article we model the cognitive processes and evaluate their impact on the performance of cognitive radio networks (CRN). Operation of the cognitive radio nodes, can be characterized by two types of processes: communication processes such as packets transmission, and cognitive processes such as estimation of the network state and decision-making for dynamic resource allocation. We propose a continuous time Markov chain model of CRN that couples these processes into unified queueing framework and analyze it by means of the matrix-geometric approach. From the obtained results, we derive the performance measures of CRN such as average delay and throughput, and establish their dependencies on the underlying cognitive processes. Additionally, we design an efficient policy for accessing the vacant channels and managing the transmission-sensing trade-off, which arises when transmissions and sensing are mutually exclusive. The policy search is carried out by the stochastic optimization method of cross-entropy. The optimized policy leads to significantly enhanced performance of CRN.
... Oklander and Sidi [57] aim to model system dynamics in an interweave CRN. Matrix geometric analysis is used to determine the stationary probabilities of a CTMC. ...
Cognitive Radio Networks (CRNs) are an emerging paradigm for next generation wireless communication systems allowing for more efficient radio spectrum utilization. In order to harness the full potential that CRNs may offer, many challenges and problems need to be overcome and addressed. One of the critical questions is the performance of secondary networks under primary user activity constraints. In this respect, queueing assumes a primary role in characterizing the delay, throughput and other performance metrics for secondary users, which in turn has implications for resource allocation, medium access control and Quality of Service (QoS) provisioning. This survey presents an overview and classification of the various queueing models and techniques which have been proposed in the literature in the context of CRNs. Furthermore, open problems, future research directions and further potential applications related to queueing for CRNs are identified.
In this paper, motivated by an increasing interest in Cognitive Radio wireless transmission systems, we study a stochastic model of a single node of such a system with underlay transmission and balking. The considered model is essentially a single-server system with an ON–OFF type environment governing the service time intensity and triggering the balking events. We utilize the matrix analytic method for steady-state analysis, and perform transient analysis by Complete Level Crossing Information approach. The results of analysis are validated and illustrated by simulation.
While essentially all of the frequency spectrum is allocated to different applications, observations provide evidence that usage of the spectrum is actually quite limited, particularly in bands above 3 GHz. In this paper we present a Cognitive Radio approach for usage of Virtual Unlicensed Spectrum (CORVUS), a vision of a Cognitive Radio (CR) based approach that uses allocated spectrum in a opportunistic manner to create "virtual unlicensed bands" i.e. bands that are shared with the primary (often licensed) users on a non-interfering basis. Dynamic spectrum management techniques are used to adapt to immediate local spectrum availability. We define the system requirements for this approach, as well as the general architecture and basic physical and link layer functions of CORVUS.
In an effort to improve radio spectrum management and promote a more efficient use of it, regulatory bodies are currently trying to adopt a new spectrum access model. At the same time, cognitive radio technology has received a lot of interest as a possible enabling technology. In this paper, we provide a brief description of the broad impact of cognitive radios in different markets. At Virginia Tech's Center for Wireless Telecommunications (CWT), we have designed a biologically inspired cognitive engine with dynamic spectrum access (DSA) as one of its intended applications. An experimental software simulation shows a 20 dB SINR improvement using cognitive techniques in an interference environment over that provided by current IEEE 802.11a service PHY standard
One of the reasons for the limitation of bandwidth in current generation wireless networks is the spectrum policy of the Federal Communications Commission (FCC). But, with the spectrum policy reform, open spectrum wireless networks, and spectrum agile radios are set to drive next general wireless networks. In this paper, we investigate continuous-time Markov models for dynamic spectrum access in open spectrum wireless networks. Both queueing and no queueing cases are considered. Analytical results are derived based on the Markov models. A random access protocol is proposed that is shown to achieve airtime fairness. A distributed version of this protocol that uses only local information is also proposed based on homo egualis anthropological model. Inequality aversion by the radio systems to achieve fairness is captured by this model. These protocols are then extended to spectrum agile radios. Extensive simulation results are presented to compare the performances of fixed versus agile radios.
This is a survey of material on matrix-geometric solutions to stochastic models.
Dynamic spectrum access (DSA) is important for spectrum management in the future. DSA with prioritization for two types of radio systems are presented through analytical formulation. The DSA with prioritization model is solved numerically using a two-dimensional Markov chain. Grades of service (GoSs) like system airtime and blocking probabilities are considered. The performance of these GoS measures and the effect of the size of the number of frequency channels for DSA with prioritization are evaluated. Numerical results illustrate that the advantages of DSA with prioritization for one type radio system over another type of radio system.
Today’s wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. The term xG networks is used throughout the paper. The novel functionalities and current research challenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the xG network architecture is introduced. Moreover, the xG network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performance of the upper layer protocols such as routing and transport are investigated and open research issues in these areas are also outlined. Finally, the cross-layer design challenges in xG networks are discussed.
In cognitive radio (CR) networks, identifying the available spectrum resource through spectrum sensing, deciding on the optimal sensing and transmission times, and coordinating with the other users for spectrum access are the important functions of the medium access control (MAC) protocols. In this survey, the characteristic features, advantages, and the limiting factors of the existing CR MAC protocols are thoroughly investigated for both infrastructure-based and ad hoc networks. First, an overview of the spectrum sensing is given, as it ensures that the channel access does not result in interference to the licensed users of the spectrum. Next, a detailed classification of the MAC protocols is presented while considering the infrastructure support, integration of spectrum sensing functionalities, the need for time synchronization, and the number of radio transceivers. The main challenges and future research directions are presented, while highlighting the close coupling of the MAC protocol design with the other layers of the protocol stack.
Analytical formulations of dynamic spectrum access (DSA) with complete partitioning (CP), complete sharing (CS) and virtual partitioning (VP) for two radio systems are presented. The DSA with CP and CS models are solved explicitly using two one-dimensional Markov chains and a two-dimensional Markov chain, respectively, while the DSA with virtual partitioning model is solved numerically using a two-dimensional Markov chain. Grades of service (GoSs) like system airtime, blocking probabilities and preemption probabilities are considered. The performance of these GoS measures and the effect of the size of the nominal number of frequency channels for DSA with VP are evaluated. Numerical results illustrate that the advantages of DSA with VP as compared with DSA with CP and DSA with CS.
An analytical formulation of the dynamic spectrum allocation problem for handling multiclass services in two cellular radio systems using a complete sharing (CS) scheme is presented. A CS scheme with multiple guard channels for giving prioritization of radio system 1psilas new and handoff call connection over those of radio system 2 and giving prioritization of handoff connections over new connections in each radio system is used for the resource sharing policy at the connection level. The CS model is solved using a K-dimensional Markov chain. Numerical results are illustrated to show the performance of this scheme. The performance metrics include new and handoff connection blocking probabilities and system utilization.
Analytical formulations of dynamic spectrum access (DSA) with perfect sensing (PS) and imperfect sensing (IS) for two radio systems are presented. The DSA with PS model is solved explicitly using a two-dimensional Markov chain, respectively, while the DSA with IS model is solved numerically using a two-dimensional Markov chain. Grades of service (GoSs) like system airtime and blocking probabilities are considered. The performance of these GoS measures and the effect of the probability of false alarm and probability of misdetection for DSA with IS are evaluated. Numerical results illustrate that the effect of DSA with IS as compared with DSA with PS.
Relates the performance of enabling hardware technologies to
software radio requirements, portending a decade of shift from hardware
radios toward software intensive approaches. Such approaches require
efficient use of computational resources through topological consistency
of radio functions and host architectures. This leads to a layered
topology oriented design approach encapsulated in a canonical open
architecture software radio model. This model underscores challenges in
simulation and computer-aided design (CAD) tools for radio engineering.
It aso provides a unified mathematical framework for quantitative
analysis of algorithm structures, host architectures, and system
performance for radio engineering CAD environments of the 1990s
Software radios are emerging as platforms for multiband multimode
personal communications systems. Radio etiquette is the set of RF bands,
air interfaces, protocols, and spatial and temporal patterns that
moderate the use of the radio spectrum. Cognitive radio extends the
software radio with radio-domain model-based reasoning about such
etiquettes. Cognitive radio enhances the flexibility of personal
services through a radio knowledge representation language. This
language represents knowledge of radio etiquette, devices, software
modules, propagation, networks, user needs, and application scenarios in
a way that supports automated reasoning about the needs of the user.
This empowers software radios to conduct expressive negotiations among
peers about the use of radio spectrum across fluents of space, time, and
user context. With RKRL, cognitive radio agents may actively manipulate
the protocol stack to adapt known etiquettes to better satisfy the
user's needs. This transforms radio nodes from blind executors of
predefined protocols to radio-domain-aware intelligent agents that
search out ways to deliver the services the user wants even if that user
does not know how to obtain them. Software radio provides an ideal
platform for the realization of cognitive radio
Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: · highly reliable communication whenever and wherever needed; · efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
Cognitive radio: Making software radios more personal Figure 7. Waiting times of CTMC model for parameters M={4,6,8,10}, α=10,β=2,μ=1. Figure 6. MSE of Ŝt for parameters M=5, α=0.5, β=1, μ=1, λ=1, d={10,100,1000,10000}. Figure 5. Waiting times of CTMC model for parameters M=5
- J Mitola
J. Mitola et al., " Cognitive radio: Making software radios more personal, " IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999. Figure 7. Waiting times of CTMC model for parameters M={4,6,8,10}, α=10,β=2,μ=1. Figure 6. MSE of Ŝt for parameters M=5, α=0.5, β=1, μ=1, λ=1, d={10,100,1000,10000}. Figure 5. Waiting times of CTMC model for parameters M=5, α=0.5, β=1, μ=1, λ=1, d={10,100,1000,10000}.