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• W2114371821 abstract "As part of the effort toward building a cognitive radio (CR) network testbed, we have demonstrated real-time spectrum sensing. Spectrum sensing is the cornerstone of CR. However, current hardware platforms for CR introduce time delays that undermine the accuracy of spectrum sensing. The time delay named response delay incurred by hardware and software can be measured at two antennas colocated at a secondary user (SU), the receiving antenna, and the transmitting antenna. In this paper, minimum response delays are experimentally quantified and reported based on two hardware platforms, i.e., the universal software radio peripheral 2 (USRP2) and the small-form-factor software-defined-radio development platform (SFF SDR DP). The response delay has a negative impact on the accuracy of spectrum sensing. A modified hidden Markov model (HMM)-based single-secondary-user (single-SU) prediction is proposed and examined. When multiple SUs exist and their channel qualities are diverse, cooperative prediction can benefit the SUs as a whole. A prediction scheme with two stages is proposed, where the first stage includes individual predictions conducted by all the involved SUs, and the second stage further performs cooperative prediction using individual single-SU prediction results obtained at the first stage. In addition, a soft-combining decision rule for cooperative prediction is proposed. To have convincing performance evaluation results, real-world Wi-Fi signals are used to test the proposed approaches, where the Wi-Fi signals are simultaneously recorded at four different locations. Experimental results show that the proposed single-SU prediction outperforms the 1-nearest neighbor (1-NN) prediction, which uses current detected state as an estimate of future states. Moreover, even with just a few SUs, cooperative prediction leads to overall performance improvement." @default.
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• W2114371821 date "2011-05-01" @default.
• W2114371821 modified "2023-10-16" @default.
• W2114371821 title "Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio" @default.
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• W2114371821 doi "https://doi.org/10.1109/tvt.2011.2116051" @default.
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