School of Electrical and Computer Engineering Georgia Institute of Technology
Broadband Wireless Networking Lab
Cognitive Radio Ad Hoc Networks
Project Description
Cognitive Radio Ad Hoc Networks
CR Mesh and Sensor Networks
Architecture for Cognitive Radio Ad Hoc Networks
The components of the cognitive radio ad hoc network (CRAHN) architecture, as shown in Figure 1. The components of the CRAHN architecture can be classified in two groups as the primary network and the CR network components. The CR component may itself be composed of different types of networks, such as, wireless sensor networks, mesh networks and mobile ad hoc networks. Moreover, they may exist in overlapped areas, without the presence of a centralized controller or an established network infrastructure.
The components of the cognitive radio ad hoc network (CRAHN) architecture, as shown in Figure 1. The components of the CRAHN architecture can be classified in two groups as the primary network and the CR network components. The CR component may itself be composed of different types of networks, such as, wireless sensor networks, mesh networks and mobile ad hoc networks. Moreover, they may exist in overlapped areas, without the presence of a centralized controller or an established network infrastructure.
The shared nature of the wireless channel necessitates coordination of transmission among the CR users. In the CRAHNs, the sensing schedules are determined and controlled by each user and are not synchronized by any central network entity. Thus, the CR ad hoc users independently perform sensing on an on-demand basis - i.e., when CR users want to transmit or are requested their spectrum availability by neighboring users. This closely couples the sensing functionality with spectrum sharing among the CR users, that is an integral part of the medium access control (MAC) layer coordination.
CRAHNs require capabilities to decide on the best spectrum band among the available bands according to the QoS requirements of the applications. This notion is called spectrum decision and constitutes a rather important but yet unexplored topic in CRAHNs. Here, spectrum decision needs to consider the end-to-end route consisting of multiple hops. Furthermore, available spectrum bands in CR networks may differ at each hop. As a result, the connectivity concept in CRAHNs is spectrum-dependent. Thus, spectrum decision should interact with routing protocols to find the best combination of route and spectrum bands, and new performance metrics are needed for choosing the routes.
The decentralized nature of the mesh network makes channel coordination between nodes of the same route difficult. When mesh routers are of a hetergeneous nature with different spectrum access capabilities (say, two adjacent routers A and B can only tune their radios to 700 MHz and 5 GHz respectively apart from the ISM band) this becomes a critical concern. Based on primary user activity information and secondary user QoS requirements, a new cognitive route metric is devised, as shown in Figure 4. Also, the mesh network is partitioned into trees, each tree being on a different spectrum band. This addresses the issue of heterogenous mesh routers operating on different bands and simplifies route management in case of new primary user arrivals. By maintaining a list of tree nodes that overlap with the other spectral trees, the root can perform fast lookups and path re-assignments. The tree structure also provides a bounded performance on the routing functionality, once it is set up.
PU activity on the currently used spectrum may necessitate the CR user to choose a new operating spectrum band, which is referred to as spectrum mobility. Spectrum mobility is closely linked to the maintenance of the routing path, as the need to change the spectrum may not always result in a new feasible spectrum for the affected link that meets the user constraints. At this time, the affected link, with minimal or highly varying spectrum availability, may need to be circumvented altogether and a new route must be formed that can provide the spectrum resource for acceptable performance.
The activity of the primary users (PUs) affects the channels of the licensed bands differently. This renders the channels unusable for the CR network to different geographical extents around the PU. In such a situation, the key decision is switching the channel in portions of the route, thus incurring a switching delay, or passing through entirely different regions altogether, thus increasing the latency. In addition, the frequently changing primary user (PU) activity and the mobility of the users make the problem of maintaining optimal routes in ad-hoc CR networks challenging. In this project, we develop a geographic forwarding based SpEctrum Aware Routing protocol for Cognitive Ad-hoc networks (SEARCH), that:
We consider a three-dimensional system, with the x-y plane representing the physical space where the CR network and the PUs are located. The z-axis shows the frequency scale and also the different channel bands. The shaded regions in the figure show that a single PU may affect several channels (frequencies) around its location. Moreover, the channels may be affected to different geographical extents, depending upon their frequency separation with the PU's transmission channel. SEARCH attempts to find paths which circumvent the PU coverage regions (Path 1 and 2) and link them together, whenever a performance benefit is seen.
The route maintenance and recovery procedure in SEARCH is as follows: SEARCH binds routes to regions found free of PU activity, rather than particular CR users. Thus even if nodes move away, the route is kept operational by choosing replacement nodes that are close to the original locations. Moreover, when new spectrum is detected or the channel becomes unusable owing to the appearance of a PU, the route is updated. During these update stages, the optimality of the route may be partially lost as the key consideration is prevention of performance degradation to the PU. SEARCH intelligently minimizes complete re-routing by undertaking local recovery actions.
The dynamic nature of the underlying spectrum in cognitive radio networks is known to have adverse influences on the overall throughput of the transmission functions. It is important to seamless integrate the channel sensing and primary user (PU) detection techniques in the design of higher layer, end-to-end transport protocols. In this project, we devise a window-based transport protocol, TP-CRAHN, that that not only addresses the key concerns on classical wireless ad hoc networks, such as mobility, but also considers spectrum sensing, channel switching, PU activity and other issues that are commonly seen CR networks.
The finite state machine diagram of the transport layer protocol is shown in Figure. 7, with the state changes. These states are (i) Connection Establishment, (ii) Normal, (iii) Spectrum Sensing, (iv) Spectrum Change, (v) Mobility Predicted, and (vi) Route Failure. Each of these states addresses a particular CR network condition and we describe them as follows.
The Wireless Mesh Network (WMN) paradigm is envisaged to be a key technology that allows ubiquitous connectivity to the end user. A typical WMN consists of mesh routers (MRs) forming the backbone of the network, interconnected in an ad-hoc fashion. Each MR can be considered as an access point serving a number of users or mesh clients (MCs) under it. The MCs could be mobile users, stationary workstations or laptops that exchange data over the Internet. Our proposed COgnitive Mesh NETwork (COMNET) architecture, takes the first step in leveraging the benefits of cognitive radio technology in the area of WMNs.
Figure 9. Cognitive mesh network architecture.
This project aims to address the following key challenges in a cognitive radio enabled mesh scenario:
Related work:
Cognitive Sensor Networks: Interferer Classification and Transmission Adaptation
Wireless sensor networks (WSNs) are being increasingly deployed for a variety of environment monitoring, commercial utility metering, military and surveillance applications. These applications necessitate reliable data delivery with minimum packet loss due to external interference. When these sensors form a CR network, some of the transmission channels may be affected by the PU. In a noisy environment with multiple interferer types, such as wireless LANs and commercial microwave ovens, distinguishing between them becomes a key challenge. By identifying the presence of a specific type of an interferer, the sensors can choose their transmission channel, and also adapt their packet scheduling at the MAC layer to avoid packet losses due to interference.
The problem of detecting the above interferer characteristics is addressed in this project by:
Figure 10. Identifying the interferer type and location.
We first conduct experiments to derive how the channels used by the sensors are affected due to specific interferers. For our experimental study, we use the WLAN and commercial microwave ovens. The measured received power in each channel is used to create a reference vector (Figure 10 (a)) in an n-dimensional space, where n is the total number of channels affected. This reference vector is then compared with an observed channel power vector during the network operation to identify the type of the interferer based on the channels that are affected. Finally, using clustering methods, the sensors that sense a common interferer are grouped together. The PU or interferer location can then be approximated as the cluster center (Figure 10 (b)). This process allows sensors to identify the PU characteristics by delegating the computational complexity to the sink node.
Related work:
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