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Opportunity detection at secondary transmitters (TXs) is a key techniqueenabling cognitive radio (CR) networks. Such detection however cannot guaranteereliable communication at secondary receivers (RXs), especially when theirassociation distance is long. To cope with the issue, this paper proposes anovel MAC called sense-and-predict (SaP), where each secondary TX decideswhether to access or not based on the prediction of the interference level atRX. Firstly, we provide the spatial interference correlation in a probabilisticform using stochastic geometry, and utilize it to maximize the area spectralefficiency (ASE) for secondary networks while guaranteeing the service qualityof primary networks. Through simulations and testbed experiments using USRP,SaP is shown to always achieve ASE improvement compared with the conventionalTX based sensing.

Sense-and-Predict: Opportunistic MAC Based on Spatial Interference Correlation for Cognitive Radio Networks