techniques it is a "realistic" model of the environment. Now likely to find applicability to CR: a sequence of actions may be "tested" against a goal and an appropriate next action may be selected. The State space models and searching level of sophistication for the model of the environ- Ontological engineering ment is not well defined. These agents have increased Neural networks in CR capability of adapting because a prediction about the Fuzzy control in CR consequences of an action is available. There is no GAs in CR feedback, and learning is therefore limited. This is the Game theory in CR minimal level of sophistication for a CR. Knowledge-based reasoning in CR 4. A utility-based agent maps the state sequence (memory state) to a "happiness" value and therefore Signal processing includes feedback. The more sophisticated environ- Digital signal processing (DSP) technology enables rapid ment model may experiment with sequences of advances in CRs. Intellectual property resources are actions for selection of the best next action. This widely available for signal processing functions. In GPP model of a CR has the ability to learn and adapt [35]. (general-purpose processor) or DSP resources, libraries of Cognitive radio: The technologies required CHAPTER 2.4b routines realize functions in efficient assembly language. great confidence how to route a packet even if one is not In FPGA (field programmable gate array) or ASIC re- ready to go. Maintaining this knowledge across a MANET sources, licensable cores of signal processing engines are requires resources. If the connection links are very dy- available in very-high-speed integrated circuit (VHSIC) namic or the mobility of the nodes causes rapid handoff Hardware Design Language (VHDL) or Verilog. Signal from one "network" to another, then the overhead to processing routines are available for communication signal maintain the routing state may be high. In contrast, processing (modulation/demodulation, FEC, equaliza- reactive-routing approaches broadcast a short search