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As wireless networks are becoming more omnipresent and pervasive, appropriate resource allocation and organization becomes an increasingly pressing challenge. There exist on the consumer market two important types of wireless network technologies. On the one hand, cellular mobile networks are highly centralized and hierarchical. By contrast, wireless local area networks (WLANs) are deployed in an ad-hoc unstructured manner, thus avoiding the need for elaborate and costly planning. However, WLANs such as those falling under the highly successful 802.11 standard do not manage interference effectively and tend to collapse at high offered traffic loads. It can be seen that cellular and WLAN represent two radically different approaches in radio resource management, characterized by different sets of advantages and drawbacks. The purpose of this chapter is to demonstrate the feasibility of a connection-oriented selforganized wireless system which offers efficient radio resource management and provides the best aspects of both cellular (reliable, connection-oriented operation even at high offered loads) and WLANs (ad-hoc deployment and distributed intelligence). This is achieved based on the multi-agent concept and local synergistic micro interaction (between neighboring transceivers) from which a global organization emerges. The notion of Multiple Agent (MA) considered is of the “ant” variety, whereby small minimalist agents sense their environment and react to it in an interdependent manner. Social insects and mostly ants or bees are the most cited biological examples. In the literature such approaches have already been used to solve many combinatorial/optimization problems (Beongku et al., 2003; Brueckner & Parunak, 2003; Muraleedharan & Osadciw, 2003). This design philosophy differs frommore traditional approaches which consist in postulating criteria expressed by equations and models in order to formulate the problem in such a way that an optimal solution is derived within the defined context. In the agent approach, precise mathematical formulation of the problem is neither required nor very useful. The approach thus becomes attractive for tackling complex multidimensional problems which would otherwise be intractable. Therefore, our goal is not to demonstrate an optimal design, but to illustrate how a Multi Agent System (MAS) can be empirically designed and fine-tuned to fit a specific application. Moreover, it will be seen that such a dynamically adaptive solution, in spite of its empirical nature, offers many advantages over a rigid analytically-derived counterpart. 26

Multi-Agent Design for the Physical Layer of a Distributed Base Station Network