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A cognitive radio network with decentralized control (i.e., a cognitive ad hoc network) is considered in this demonstration. The demo implements a decentralized and localized algorithm for through put maximization through joint routing and interference-avoiding waveform selection. The algorithm adapts to time-varying traffic demands, interference profile, and network topology to locally maximize the achievable data rate while avoiding harmful interference to co-located primary or secondary users. The prototype is based on a cross-layer protocol stack implemented in Python, which leverages GNU Radio for adaptive signal generation on a USRP2 software-defined-radio platform.