School of Electrical and Computer Engineering Georgia Institute of Technology
Broadband Wireless Networking Lab
Cognitive Radio Networks
Project Descriptions
Infrastructure-based CR Networks
Architecture for Cognitive Radio Networks
Current wireless network environment employs heterogeneity in terms of both spectrum policy and communication technologies. Hence, a clear description of the cognitive radio network architecture is crucial for development of communication protocols.
Figure 1. Cognitive radio network architecture.
The components of the cognitive radio network architecture, as shown in Figure 1, can be classified in two groups as the primary network and the cognitive network. Primary network is referred to as the legacy network that has an exclusive right to a certain spectrum band. On the contrary, cognitive network does not have a license to operate in the desired band. The basic elements of the primary and unlicensed networks are defined as follows:
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Spectrum Management Framework for Cognitive Radio Networks
CR networks impose unique challenges due to the coexistence with primary networks as well as diverse QoS requirements. Thus, new spectrum management functions are required for CR networks with the following critical design challenges:
Figure 2. Spectrum Management Framework.
In order to address these challenges, we provide a directory for different functionalities required for spectrum management in CR networks. The spectrum management process consists of four major steps:
The spectrum management framework for CR network communication is illustrated in Fig. 2. It is evident from the significant number of interactions that the spectrum management functions necessitate a cross-layer design approach. Thus, each spectrum management function cooperates with application, transport, routing, medium access and physical layer functionalities with taking into consideration the dynamic nature of the underlying spectrum.
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Spectrum Sensing for Cognitive Radio Networks
A cognitive radio should monitor the available spectrum bands, capture their information, and then detect the spectrum holes. Hence, spectrum sensing is a key enabling technology in cognitive radio networks. In spectrum sensing, the detection accuracy has been considered as the most important factor to determine the performance of cognitive radio networks.
However, in reality, RF frontend of CR users cannot differentiate the primary user signals and CR user signals. In case of the energy detection, widely used in spectrum sensing, transmission and sensing cannot be performed at the same time. Thus, during the sensing(observation time), all CR users should stop their transmissions and keep quiet. Due to this hardware restriction, CR users should sense the spectrum periodically with sensing period Ts and observation time ts, as described in Figure 3.
Figure 3. Periodic spectrum sensing strcuture.
However, the periodic spectrum sensing should consider following design issues:
In Cognitive radio network, available spectrums may show different characteristics with the bandwidth, the primary user activity, and acceptable interference limit, which affect both the sensing accuracy and spectrum efficiency. Thus, spectral efficient sensing technique is essential for cognitive radio networks. Hence, in this project we will propose the spectral efficient sensing technique for cognitive radio networks, which provides optimal spectrum sensing period and observation time to maximize the efficiency of each spectrum bands subject to the resource limitation and interference restriction.
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Spectrum Decision Framework for Cognitive Radio Networks
In cognitive radio (CR) networks, unused spectrum bands will be spread over a wide
frequency range including both unlicensed and licensed bands. These
unused spectrum bands detected through spectrum sensing show
different characteristics according to the radio environment. Since CR networks can have
multiple available spectrum bands having different channel characteristics, they should be capable of selecting the
proper spectrum bands according to the application requirements, called spectrum decision.
In this project, we propose an application-adaptive spectrum decision
method over heterogeneous spectrum bands. At first, each spectrum band
is characterized for the spectrum
decision, based on not only local observations of CR users but
also statistical information of primary networks. Through the local
measurement, CR users can estimate the channel conditions such as
capacity, bit error rate (BER), delay and jitter. In order to describe
the dynamic nature of CR networks, we propose a new
metric, primary user activity, defined as the probability of the
primary user appearance during the CR user transmission. After the
spectrum characterization, the CR network chooses the best
spectrum bands through the following spectrum operations. the CR
network uses multi-spectrum transmission based on OFDM technology. This
decision process can be modeled as an optimizaiton problem.
In this project, a QoS aware spectrum decision framework is proposed to
determine a set of spectrum bands by considering the application
requirements as well as the dynamic nature of spectrum bands as shown
in Figure 4. Specifically, for real-time applications, a minimum
variance-based spectrum decision (MVSD) is proposed so as to minimize
the capacity variance of the decided
spectrums subject to the capacity constraint. Furthermore, a maximum
capacity-based spectrum decision (MCSD) is proposed for the best effort
applications where spectrum bands are decided to maximize the total
throughput.
Moreover, a dynamic admission control scheme is developed to decide on
the spectrum bands adaptively dependent on the time-varying CR network
capacity.
Figure 4. Spectrum decision framework for cognitive radio networks.
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Inter-Cell Spectrum Sharing in Cognitive Radio Networks
Cognitive radio (CR) networking achieves high utilization of the scarce spectrum resources without causing any performance degradation to the licensed users. Since the spectrum availability varies over time and space, the infrastructure-based CR networks are required to have a dynamic inter-cell spectrum sharing capability. This allows fair resource allocation as well as capacity maximization and avoids the starvation problems seen in the classical spectrum sharing approaches. In this paper, a joint spectrum and power allocation framework is proposed that addresses these concerns by (i) opportunistically negotiating additional spectrum based on the licensed user activity (exclusive allocation), and (ii) having a share of reserved spectrum for each cell (common use sharing). Our algorithm accounts for the maximum cell capacity, minimizes the interference caused to neighboring cells, and protects the licensed users through a sophisticated power allocation method.
Figure 5. Inter-Cell Spectrum Sharing Framework.
Infrastructure-based CR networks are required to provide two different types of spectrum sharing schemes: intra-spectrum sharing and inter-spectrum sharing. In order to share spectrum resource efficiently, CR networks necessitate a unified framework to support cooperation among inter- and intra-cell spectrum sharing schemes and other spectrum management functions. Figure 5 shows the proposed framework for spectrum sharing in infrastructurebased CR networks, which consists of inter-cell spectrum sharing, intra-cell spectrum sharing, and event monitoring.
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Spectrum Mobility for Cognitive Radio Networks
Cognitive radio (CR) networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face to several challenges based on the fluctuating nature of the available spectrum as well as the diverse service requirements of various applications. Especially in CR cellular networks, CR users are traversing across multiple cells having heterogeneous spectrum availability. Furthermore, CR users should switch to a new spectrum band when the licensed user appears in the spectrum, the so-called spectrum mobility. Because of these dynamic spectrum environments, it is more complicated to maintain a reliable and seamless communication channels in CR cellular networks.
Figure 6. Mobility Management Framework.
In this paper, we propose a spectrum-aware mobility management scheme for CR cellular networks, which supports seamless mobile communications by considering the joint influence of user and spectrum mobilities. More specifically, to mitigate heterogeneous spectrum availability, a novel CR cellular network architecture based on a spectrum pooling concept is introduced. Based on this architecture, a unified mobility management framework is proposed so as to support diverse mobility events in CR networks, consisting of inter-cell resource allocation, spectrum and user mobility management functions as shown in Figure 6. Inter-cell resource allocation enables each cell to share spectrum resources with its neighbor cells for efficient mobility management. To improve cell capacity under time-varying spectrum environment, a spectrum mobility management scheme is developed, where the CR network determines the proper spectrums and target cells for CR users by exploiting both current spectrum utilization and the stochastic connectivity model. In a user mobility management scheme, a switching costbased handoff decision mechanism is proposed so as to minimize quality degradation caused by user mobility. Simulation results show that the proposed methods can achieve better performance in terms of both cell capacity as well as mobility support in communications.
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