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Cognitive radio (CR) has been proposed as a technology that is able to make efficient usage of the radio frequency
spectrum through opportunistic and dynamic spectrum access. CRs rely on a process, known as spectrum sensing (SS),
to gather information about the radio environment in which they wish to operate. This information allows them to
make use of appropriate spectrum resources and also helps them to avoid interference with other users of the spectrum.
However, the accuracy of this information is of paramount importance since inaccurate data could negatively impact
upon the performance of a CR network (CRN). One of the problems associated with SS is the hidden node problem,
where due to severe shadowing or multipath fading, it is often not possible for a single sensing node to obtain accurate
results. Cooperation between multiple sensing nodes may thus be employed to obtain more accurate SS decisions.
Energy efficiency in cognitive radio networks (CRN) is of paramount importance since secondary users (SU) are often
likely to be energy constrained. While spectrum sensing (SS) is a critical CRN function, repetitive SS events can
significantly reduce the battery life of sensing devices. However, energy efficiency can be improved by employing
spectrum opportunity forecasting (SOF) and optimal scheduling for sensor node activation to reduce the required
number of SS events. In this presentation, a combination of SOF and optimal scheduling is thus explored. The
application of SOF to cooperative SS is one of the unique contributions we have undertaken. Results indicated that this
combination could significantly reduce sensor node energy consumption and in so doing increase CRN lifetime. The
presentation will also cover the Spectrum Sensing measurement campaign done in South Africa and the opportunities
in TVWS for future wireless communications such as for Rural Communications using Dynamic Spectrum
Management for Long Range Wi-Fi Rural Broadband.