It has been observed previously that downlink noise correlation has no effect on the average error probability of a differentially phase shift keyed (DPSK) satellite system when the symbols are equiprobable. We show thai this observation holds for a satellite system exhibiting amplitude-modulation-to-amplitude-modulation (AM-AM) conversion effects and amplitude-modulation-to-phase-modulation (AM-PM) conversion effects. It is also shown thai noise correlation has no effect even when a constant phase error is caused by the delay line of the DPSK receiver in terrestrial and satellite links.

Differential detection of binary frequency-shift keyed signals (binary FM) is analyzed. It is shown that, by proper selection of the delay interval, the error rate of differentially detected binary FM can be made equal to the error rate of binary DPSK provided that the modulation indexhsatisfiesh \geq 0.5. Ifh > 0.5, it is shown that the differential detection of binary FM yields poorer performance than DPSK.

This paper considers the performance evaluation of binary differentially-phase-shift-keyed signals which have been transmitted through a hard limiting channel in the presence of intersymbol interference and additive Gaussian noise. An expression for the probability of error is derived which is in the form of an infinite series. In the analysis, the effects of Gaussian noise in the uplink and downlink are taken into consideration. The intersymbol interference is assumed to be present in the uplink only. In order to illustrate the applicability of the method, numerical results are computed for a channel which employs a Butterworth filter.

In agreement with a recent paper by Pierce, it is shown that ternary, phase-shift keyed (PSK) signals attain the lowest bit error rate at high signal-to-noise ratio (SNR) if more than4/3binary digits can be mapped to a symbol. This point is further demonstrated by using a 3B-2T Code which maps 1.5 bits to a symbol. The spectrum and bit error rate are evaluated for the 3B-2T coded Ternary. PSK signal and compared with those of quaternary phase-shift keyed (QPSK) signal.

A decision feedback receiver structure for receiving binary phase-shift keyed signals over bandlimited, nonlinear satellite channels, in the presence of both uplink and downlink additive Gaussian noise, is derived. As an illustrative example of the receiver, a hard-limiting channel model is considered in detail, and two special cases are examined. Based on the analysis, an alternative receiver structure which is more suitable for implementation is derived. The performance of the receiver is evaluated by computer simulation techniques, and it is shown that significant performance improvement over the single sample sign detector can be obtained.

An expression is derived for the error probability of binary CPSK signals received over a soft-limited channel in the presence of pre- and post-limiter additive Ganssian noise. Although this expression is not in closed form, it may be efficiently evaluated to any desired accuracy using a recurrence method developed in the appendix. System performance is also evaluated when the signals are subjected to bandlimiting as well as Gaussian noise preceding the limiter. Moment space techniques are used to obtain upper and lower bounds on the errorrate in terms of the error-rate in the presence of Gaussian noise alone. Numerical examples are presented for a channel which includes a Chebyshev filter preceding the limiter. These illustrate that the bounds are tight enough for most practical purposes.

A method is presented for analyzing the performance of M-ary coherent phase-shift-keyed (CPSK) signals which have been transmitted through a hard limiting channel in the presence of linear intersymbol interference preceding the limiter and additive Gaussian noise both preceding and following it. The probability of error is obtained in the form of an infinite series as a function of the expected values of trigonometric functions of the interference. The characteristic function of the interference is expanded in a power series and used to obtain these expected values. The resulting error probability is obtained as the product of two absolutely convergent series. Numerical examples are presented to illustrate the technique.