Stochastic Description of Wireless Channel for Cognitive Radio

The common description of wireless propagation channel is frequency dependence of the attenuation, which is not appropriate parameter for modern communication systems using micro cells. It is more required to have information about utilization and interference in the particular channels, because there is no di-culty to set up link budget for small cell wireless transmission. This paper introduces innovative stochastic channel description mainly for cognitive radio application. Based on the localization of the primary users in the frequency spectrum at current location, cognitive system minimizes risk of the interference between primary and cognitive users using dynamic spectrum sharing. Hence we are presenting suitable model of spectrum occupancy for cognitive system based on stochastic analyses. According to the typical utilization of the wireless channel it is possible to classify typical radio transmission according to several criteria as following: utilization of the speciflc frequency band, bandwidth of the unused part of spectrum, duration of the spectral holes in the frequency band. These parameters should be used for wireless service planning rather than conventional one. Subsequently, we can reach more reliable results. 1. INTRODUCTION Data communications have been changed during last few decades. Slow telephone line was replaced by optical cable transporting several Gbps. Similar progress can be observed in wireless systems as well. The best example is well-known WI-FI system, working in ISM band. The basic speed several Mbps in IEEE 802.11b was increased up to several hundred Mbps in IEEE 802.11n (1). All of these improvements were connected with using similar or lower radiation power but using more complicated modulation schemes (64 QAM and more), which need su-cient SINR (signal to interferences and noise ratio) to secure reliable data demodulation. It became more complicated to plan wireless coverage using conventional models based on the radio wave attenuation (2) on signiflcant frequency. New methods of the planning in micro and femto cells are necessary to ensure reliable results. Moreover, the problem of nowadays communication systems is also under utilization of the frequency spectrum. Conventional licensing schemes led to massive under utilization of the frequency bandwidth (3,4). Moreover, some shared parts of the spectrum (such as ISM band) are used by several services (WI-FI, Bluetooth etc.) at the same time and location. Therefore it is very di-cult to reach su-cient transmitting/receiving conditions in these shared bands. The possible solution of the problems mentioned above was recently opened and discussed in dynamic spectrum sharing (5). Dynamic spectrum sharing is mostly taken into account as a part of cognitive radio system (6,7), which is an intelligent, autonomous wireless system using its awareness ability of the other wireless environment (providers, users, etc.) to optimize its wireless tra-c. The most important part of the cognitive radio is called cognitive engine and it is managing all data inputs and outputs. The main goal of the cognitive engine is to maximize SINR and minimize efiect of the cognitive radio (secondary) users for the current (primary) users of the shared wireless domain. Dynamic spectrum sharing divides the spectrum (or users into two main groups). Firstly, the licensed (primary) users have their privilege to use the frequency band by a licensing operator. Secondly, the transmitting resources of the cognitive (secondary) users, are managed by cognitive engine (8). Cognitive engine should allocate primary users band when privileged user is inactive for secondary users to increase capacity of their service or reallocate cognitive users to minimize interference. It is obvious, that model of frequency spectrum and its changes in time are necessary for es- tablishing cognitive system. Spectrum sensing, as an awareness feature of cognitive radio, should be improved by statistic data. In this paper we present preliminary results of the stochastic (9) wireless channel modelling. We are not focusing on the conventional frequency parameters such as attenuation and dispersion but we are establishing model on time evolution of the primary user's frequency spectrum utilization and spectrum holes durations.