Human Bond Communication Using Cognitive Radio Approach for Efficient Spectrum Utilization

This chapter explores the novel machine learning (ML) based approaches to cognitive radio (CR) systems developed here will lead to innovative human bond communication (HBC) applications that will serve the needs of a community. An example of a community could range from spectators in a sports arena to ad hoc vehicular networks to a community in a neighbourhood sharing common interests. This chapter explores the synergy between ML and cognitive radio systems. Based on this exploration, we will formulate novel algorithms to share spectrum through dynamic spectrum leasing methodologies and adaptive policy decision making processes that seek to maximize the utilization of available scarce spectrum. This exploration will also lead to a better understanding of the traditional CR techniques and to an integrated approach that will formulate cognitive cycles at multiple levels of a system and to cross‐cognitive cycle approaches that will lead to further efficiencies in spectrum utilization. The research will also facilitate a tighter coupling between CR theories and ML theories and lead to new theoretical analyses and novel algorithms that could bring about greater synergy between wireless networks and computer science.