The cognitive radio (CR) concept has appeared as a promising technology to cope with the spectrum scarcity caused by increased spectrum demand due to the emergence of new applications. CR can be an appropriate mean to establish self-organization and situation awareness at the radio interface, which is highly desired to manage unexpected situations that may happen in a disaster scenario. The scout node proposed in this paper is an extended concept based on a powerful CR node in a heterogeneous nodes environment which takes a leading role for highly flexible, fast, and robust establishment of cooperative wireless links in a disaster situation. This node should have two components: one is a passive sensor unit that collects and stores the technical knowledge about the electromagnetic environment in a data processing unit so-called “radio environment map” in the form of a dynamically updated database, and other is an active transceiver unit which can automatically be configured either as a secondary node for opportunistic communication or as a cooperative base station or access point for primary network in emergency communications. Scout solution can be viable by taking advantage of the technologies used by existing radio surveillance systems in the context of CR. 1. Introduction Communication has been an indispensable part of everyday life in the present days. Apart from making the general life better, modern communications should also be applicable for relief and support to the victims of exceptional adverse situations which include disaster scenarios like earthquakes, floods, cyclones, forest fires and terrorist attacks. Such scenarios impose new requirements on the communication systems. Some of the tasks of a cognitive radio network for emergency situations may be (1) to support specific service requests (higher traffic, coverage, localization, emergency messages, etc.), (2) to re-establish communications in a short time, and (3) to assist rescue forces communications and provide interoperability among them and also among rescue forces and public network. One of the first tasks in disaster is to organize rescue operations in a quick and efficient manner which as well requires rescue forces to be provided with reliable and stable communication facilities. One of the common problems here is providing interoperability among rescue responders originally using different communication standards [1, 2]. In terms of public communication systems, obvious problems in such scenarios are capacity overload with the resulting service denial and absence of coverage
A. Gorcin and H. Arslan, “Public safety and emergency case communications: opportunities from the aspect of cognitive radio,” in Proceedings of the 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '08), pp. 1–10, October 2008.
Use Cases for Cognitive Applications in Public Safety Communications Systems—Volume 1: Review of the 7 July Bombing of the London Underground, Wireless Innovation Forum, 2007, http://groups.winnforum.org/d/do/1565.
Y. Zhao, J. H. Reed, S. Mao, and K. K. Bae, “Overhead analysis for radio environment mapenabled cognitive radio networks,” in Proceedings of the 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR '06), pp. 18–25, September 2006.
A. Sahai, R. Tandra, S. M. Mishra, and N. Hoven, “Fundamental design tradeoffs in cognitive radio systems,” in Proceedings of the 1st International Workshop on Technology and Policy for Accessing Spectrum (TAPAS '06), August 2006.
M. ？ner and F. Jondral, “Cyclostationarity based air interface recognition for software radio systems,” in Proceedings of the IEEE Radio and Wireless Conference (RAWCON '04), pp. 263–266, September 2004.
D. Cabric, “Addressing the feasibility of cognitive radios: using testbed implementation and experiments for exploration and demonstration,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 85–93, 2008.
F. F. Digham, M. S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” in Proceedings of the International Conference on Communications (ICC '03), pp. 3575–3579, May 2003.
M. Li, V. Rozgi？, G. Thatte et al., “Multimodal physical activity recognition by fusing temporal and cepstral information,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 4, pp. 369–380, 2010.
D. Kolb, U. Uebler, and E. N. N？th, “A novel transmission scanner framework for real-time applications,” in Proceedings of the RTO-MPIST-092- Military Communications and Networks. NATO Research and Technology Organisations, 2010.
S. Hong, “Multi-resolution bayesian compressive sensing for cognitive radio primary user detection,” in Proceedings of the 53rd IEEE Global Communications Conference (GLOBECOM '10), pp. 1–6, December 2010.
S. D. Blunt, T. Chan, and K. Gerlach, “Robust DOA estimation: the reiterative superresolution (RISR) algorithm,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 1, pp. 332–346, 2011.
M. Landmann, M. K. K？ske, and R. S. Thom？, “Impact of incomplete and inaccurate data models on high resolution parameter estimation in multidimensional channel sounding,” IEEE Transactions on Antennas and Propagation, vol. 60, no. 2, pp. 557–573, 2012.
R. M. Radaydeh and M.-S. Alouini, “Impact of co-channel interference on the performance of adaptive generalized transmit beamforming,” IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2616–2629, 2011.