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A 3-state hypothesis test model for cognitive radio systems

This paper presents a cognitive engine that is applied to dynamic channel selection and parameter configuration of opportunistic spectrum users. This engine uses a 3-state hypothesis test model to make ternary decisions about channel status: vacant, underutilized, or congested. A simple autoregressive model is also used to rank channels taking channel history into considerations. The set of ranked channels is stored in a look-up table. An opportunistic user seeking a channel for its transmission uses the look-up table to access a vacant channel or share the best underutilized channel. Channel selection and parameter configuration are performed on Wi-Fi data collected using a test-bed of 16 sensors operating in the 2.4 GHz ISM band in an outdoor urban radio environment. Mining sensed data showed underutilized spectrum with a sufficient room for opportunistic spectrum use. Mining sensed data also favored making autonomous channel status decisions. In addition, a detection metric based on channel occupancy time is favored over the energy based received signal strength indicator (RSSI). A comparative performance is presented between 2-state and 3-state hypothesis test models with detection metrics based on signal energy and channel occupancy time. Using a 3-state hypothesis test model instead of a 2-state model has made more channels available for opportunistic spectrum use where underutilized channels can be shared instead of being classified busy.