We present an OFDM-based transmission scheme which is suitable for robust transmission in fast fading environments, where a reliable channel estimate is impossible or very difficult to obtain. Our scheme is based on the combination of noncoherently detected M-ary frequency shift keying (MFSK) and orthogonal frequency division multiplexing (OFDM). Noncoherent detection of OFDM-MFSK allows an arbitrary phase choice for all subcarriers in the transmitter. One possibility to exploit this degree of freedom is to choose the subcarrier phases such, that the peak-to-average power ratio (PARR) is reduced. A second possibility is to use the subcarrier phases to transmit additional data. This can be done by differentially modulating the subcarriers that are occupied by the OFDM-MFSK scheme. Both possibilities do not affect the robustness of the underlying noncoherently detected OFDM-MFSK modulation.
Recurrent neural networks (RNNs) are well known for their capability to minimize suitable cost functions without the need for a training phase. This is possible because they can be Lyapunov stable. Although the global stability analysis has attracted a lot of interest, local stability is desirable for specific applications. In this brief, we investigate the local asymptotical stability of two classes of discrete-time, continuous-state, complex-valued RNNs with parallel update and inner state feedback. We show that many already known results are special cases of the results obtained here. We also generalize some known results from the real-valued case to the complex-valued one. Finally, we investigate the stability in the presence of time-variant activation functions. Complex-valued activation functions in this brief are separable with respect to the real and imaginary parts.
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