Statistical QoS-driven power allocation over underwater cognitive acoustic networks

As a critical technique to support the multimedia services, the statistical quality-of-service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, since both the natural acoustic systems and artificial acoustic systems use the acoustic channels, the spectrum resource is severely limited in underwater wireless networks. Researchers have proposed the underwater cognitive acoustic network (UCAN) to achieve the environment-friendly and spectrum-efficient transmissions over acoustic channels. However, how to efficiently integrate the UCAN under the statistical QoS constraints is still an open problem. To overcome this difficulty, we propose the QoS-driven power allocation scheme over UCAN. In particular, we propose and analyze the underwater cognitive system models and develop the optimal power allocation scheme in cognitive acoustic scenario udder statistical QoS requirements. Also conducted is a set of simulations which evaluate the system performance over UCAN.