The matched-filter bound (MFB) for continuously dispersive Rayleigh fading channels with receive diversity is derived. The treatment is quite general, as the case of discrete multipath channels with correlated fading is included in our model as a special case, and we impose no constraints with regard to the selectivity of the channel. It is assumed that a statistical characterization of the channel is available through its direction-delay-Doppler power spectral density. The Karhunen-Loe/spl grave/ve expansion for vector processes is used to describe the received signal. The MFB is analyzed with respect to several parameters characterizing the mobile radio channel, the waveform, and the antenna configuration.

Time-discrete signals with good autocorrelation properties are used in various applications in communications engineering. From an implementation point of view, usually sequences with a small phase alphabet and a maximal energy efficiency, i.e., a uniform envelope, are most favorable. For this reason, known methods as well as several new construction techniques of binary and quadriphase sequences with optimal or best known autocorrelation properties are discussed in this correspondence. In many cases, the achievable correlation properties can be improved significantly if a single zero element per sequence is accepted. These "almost" binary and "almost" quadriphase sequences are considered as well. The optimality criteria used include the maximum absolute sidelobe and the merit factor of the periodic and the odd-periodic autocorrelation function, respectively. For sequence lengths of up to 44 in the binary case and up to 32 in the quadriphase case, the best known parameters obtained by computer search are compared with the constructed results.

We discuss a method for classification of digitally modulated signals based on performing subspace decomposition on a positive definite matrix of higher order moments of the received signals. Specifically, we specialize a general approach originally introduced for detection and classification of noise contaminated patterns to the case of digitally modulated signals such as M-ary PSK and QAM. We consider two different classifiers: one that provides only satisfactory performance for high signal-to-noise ratio, and one that performs also well in the low SNR regime. The former has the additional advantage of being invariant to both unknown phase angle (rotation) and signal amplitude, and can be used for all QAM signal constellations (including M-ary PSK), whereas the latter is only used for discrimination of M-ary PSK signals. Using simulation, we analyze the performance of the proposed classifier for transmission over the additive white Gaussian noise channel and both coherent and non-coherent reception. Moreover, the robustness of the classifier against mismatched noise modeling is discussed.

With the rising number of modulation types used in multi-user and multi-service digital communication systems, the need to find efficient methods for their discrimination in the presence of noise has become increasingly important. We present a new approach based on a pattern recognition method previously applied to word spotting problems in binary images. In this approach, a large number of spatial moments are arranged in a symmetric positive definite matrix for which eigendecomposition and noise subspace processing methods can be applied. The resultant denoised moment matrix has entries which are used in place of the raw moments for improved pattern classification. In this paper, we generalize the moment matrix technique to grey scale images and apply the technique to discrimination between M-ary PSK and QAM constellations in signal space. Invariance to unknown phase angle and signal amplitude is achieved by representing the in-phase and quadrature components of the signal in the complex plane, and computing joint moments of normalized magnitude and phase components.