Several candidate crosslink communications architectures are described for a packet-switched, low-altitude, multiple satellite system (MSS). One architecture represents a frequency-division approach, in which a cellular concept is combined with dynamic time-domain allocation within a cell. A second architecture uses an unsynchronized space-time division approach consisting of directional antennas and random accessing. A third architecture uses toroiclal antennas with unsynchronized random accessing. The recommended architecture uses a synchronized space-time division approach, consisting of directional antennas and contention-based pair-wise scheduling. This architecture, called PRS (Pseudo-random Scheduling) is found to have significantly reduced power and bandwidth requirements relative to the other approaches.

In Fourier analysis when estimating the power spectrum of a stochastic process from a sample function when the time-measurement resolution bandwidth product is large, typically transform lengths are selected which correspond closely to the desired measurement bandwidth and then time integration is used to increase the degrees of freedom of the spectral estimate. From the standpoint of estimation error an equally valid approach is to increase the time window associated with each transform and average across adjacent narrowband power estimates within the desired measurement bandwidth. This approach can be practical and offers some inherent leakage reduction which can be directly related to the data time window function characteristics. This paper uses an often overlooked result of spectral estimation to illustrate the relationship between the data time window and leakage associated with bandwidth averaging. Results associated with some commonly used window modification functions are presented.

A method for adaptive linear estimation is proposed based on a Time Domain Orthogonality condition. This algorithm arises naturally from the criterion used rather than through the application of a numerical analysis method as in the derivation of the LMS Gradient Algorithm. However, in addition to being a new and potentially useful algorithm, the resulting recursive method is suprisingly similar to the LMS Gradient Algorithm. With the addition of certain simplifying assumptions, the TDO algorithm reduces to the LPIS Gradient Algorithm except that a data dependent term replaces the constant parameter µ found In the LMS Gradient Method. In fact, it is shown that this data dependent term is an estimate of the optimum µ for maximum rate of convergence.

An Adaptive Linear Estimation Algorithm which can be applied to the Spectral Line Enhancement problem is proposed. Through computer simulation, the rate of convergence of the proposed method is found to be significantly better than that of the LMS Gradient Algorithm for the problem considered. Also, this increased performance is obtained with only a modest increase in computational complexity.