The prime wireless spectrum is inherently a critical yet scarce resource. As the demand of wireless bandwidth grows exponentially, it becomes a crucial issue to improve the spectrum utilization for the development and deployment of any new wireless technologies. In this thesis, we seek to address this problem through cooperative diversity and dynamic spectrum trading, in the context of the envisioned primary-secondary dynamic spectrum sharing paradigm. For an OFDMA-based cellular primary network which owns an exclusive right to access a certain spectrum band, we propose XOR-assisted cooperative diversity to improve the spectral efficiency of the allocated band, as well as an optimization framework to address the resource allocation problem. For the secondary network that utilizes cognitive radios to opportunistically exploit the spectrum white spaces, we establish a spectrum secondary market, design the market institution based on double auctions, and solve the decision making problem using reinforcement learning, to improve spectrum utilization via trading among secondary users.

The prime wireless spectrum is inherently a critical yet scarce resource. As the demand of wireless bandwidth grows exponentially, it becomes a crucial issue to improve the spectrum utilization for the development and deployment of any new wireless technologies. In this thesis, we seek to address this problem through cooperative diversity and dynamic spectrum trading, in the context of the envisioned primary-secondary dynamic spectrum sharing paradigm. For an OFDMA-based cellular primary network which owns an exclusive right to access a certain spectrum band, we propose XOR-assisted cooperative diversity to improve the spectral efficiency of the allocated band, as well as an optimization framework to address the resource allocation problem. For the secondary network that utilizes cognitive radios to opportunistically exploit the spectrum white spaces, we establish a spectrum secondary market, design the market institution based on double auctions, and solve the decision making problem using reinforcement learning, to improve spectrum utilization via trading among secondary users.