Spectrally-Temporally Adapted Spectrally Modulated Spectrally Encoded (SMSE) Waveform Design for Coexistent CR-Based SDR Applications

This work expands the applicability of the Spectrally Modulated, Spectrally Encoded (SMSE) framework by developing a waveform optimization process that enables intelligent waveform design. The resultant waveforms are capable of adapting to a spectrally diverse transmission channel while meeting coexistent constraints. SMSE waveform design is investigated with respect to two different forms of coexisting signal constraints, including those based on resultant interference levels and those based on resultant power spectrum shape. As demonstrated, the SMSE framework is well-suited for waveform optimization given its ability to allow independent design of spectral parameters. This utility is greatly enhanced when soft decision selection and dynamic assignment of SMSE design parameters are incorporated. Results show that by exploiting statistical knowledge of primary user spectral and temporal behavior, the inherent flexibility of the SMSE framework is effectively leveraged such that SMSE throughput (Bits/Sec) is maximized while limiting mutual coexistent interference to manageable levels

Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics

This research proposes and evaluates a feature based modulation classification system designed to discriminate between AM, BFSK, OFDM, DS-CDMA, 4-ASK, 8-ASK, BPSK, QPSK, 8-PSK, 16- PSK, 16-QAM, and 64-QAM signals without a priori knowledge of critical signal parameters, including carrier frequency, symbol rate, or phase offset, among others. The classifier is based on the principles of cyclostationarity and leverages cyclic statistics to make its classification decision. The classification process is performed in a hierarchical process in order to exploit the lower variance of lower order cyclic statistics before making use of the higher order cyclic statistics. The initial classification is based on the estimated Spectral Coherence Function (SOF) of the received signal, followed by estimates of the signal\u27s fourth-through eighth-order Cyclic Cumulants (CC). The performance of the classification system is evaluated under fading flat, two-path, and 20-path transmission channels, and multiantenna combining methods are exploited to increase the system performance

Congestion modelling and optimisation of routers with correlated traffic and arbitrary service times

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