Cognitive radio architecture Cognitive engine design Components descriptions 1.sensors 2.optimizer 3.decison maker 4.policy engine 5.radio framework 6.user interface 7.cognitive controller configuration AI and its techniques Conclusion References
Sensors collect data from radio or other systems to describe and model the environment. The important aspect of a sensor is having a standard approach to how data is transferred to a cognitive controller. Application programming interface(API) Initialization Waiting for data request from cognitive controller Collecting data and building a model Transferring model to cognitive controller
Cognitive engine sends information to the sensor through some generic interface Sockets and SOAP : communicate information between software programs Functions and processing algorithms are retrieved through external library
”model-name” ”value” …….
the optimization process takes the user oriented information from sensors or user interface to select or design a waveform that will maximize the performance. Optimizer produces waveform that comes close to the QoS values with respect to the provided environmental data. Depending on the implementation the optimizer may build a new waveform or select it from a list of predefined waveforms.
It coordinates information and decides how to optimize and act. If optimization is required the decision maker will provide some context such as optimization goal or time limit for when a new waveform is required. The current method of decision making is based on CBDT. CBDT keeps database of observed cases,the action taken to respond to those cases and results of the action.
The policy engine must test and authenticate a waveform. Two main goals of policy engines 1) policy engine must be secure such that unauthorized waveforms cannot be transmitted. 2) It must be liberal enough to allow many different types of waveforms to run on the system.
It is a component that translates between cognitive engine and radio platform. When the cognitive engine wants to reconfigure the radio’s waveform it uses generic communication theory representation in XML. The mapping between the XML format to radio specific format is done by parsing the XML file from cognitive engine and formatting commands used to configure the radio.
The XML parser block is the translation block. The SDR control can also be accomplished by external interface such as through HTTP, message passing etc. The radio framework used in this work is GNU radio software radio.
It has widely varying responsibilities depending on the cognitive radio use case. Different instances 1) Control window 2) Simple configuration window In most idealist view of cognitive radio there is no user interface.
The important aspect of cognitive controller is its ability to use many different implementations of the components described above. It is configured through an XML file that defines which components are currently attached. The cognitive controller can define and connect to multiple sensors. Each component is described by a specific name that the cognitive radio uses to identify when collecting the information.
Successful cognitive radios are aware,can learn, and can take action for any situation that might araise.These radios require highly sophisticated learning and decision making capabilities. Techniques 1) Neural networks 2) HMM 3) Fuzzy logic 4) Evolutionary algorithms 5) Case based reasoning
The cognitive engine concepts were introduced and its implementation was shown. The major components of the platform include sensors,optimizer,decision maker, policy engine, radio framework, user interface. The discussion mostly focused on defining the roles and responsibilities of each component to provide the context from which to build a cognitive radio. Various AI techniques were discussed.
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