  Opportunity detection at secondary transmitters (TXs) is a key technique
enabling cognitive radio (CR) networks. Such detection however cannot guarantee
reliable communication at secondary receivers (RXs), especially when their
association distance is long. To cope with the issue, this paper proposes a
novel MAC called sense-and-predict (SaP), where each secondary TX decides
whether to access or not based on the prediction of the interference level at
RX. Firstly, we provide the spatial interference correlation in a probabilistic
form using stochastic geometry, and utilize it to maximize the area spectral
efficiency (ASE) for secondary networks while guaranteeing the service quality
of primary networks. Through simulations and testbed experiments using USRP,
SaP is shown to always achieve ASE improvement compared with the conventional
TX based sensing.
