A real-time digital signal combining system for use with array feeds is proposed. The combining system attempts to compensate for signal-to-noise ratio (SNR) loss resulting from antenna deformations induced by gravitational and atmospheric effects. The combining weights are obtained directly from the observed residual carrier samples in each channel using a 'sliding-window' implementation of a maximum-likelihood (ML) parameter estimator. It is shown that with averaging times of about 0.1 s, combining loss for a seven-element array can be limited to about 0.1 dB in a realistic operational environment. This result suggests that the real-time combining system proposed is capable of recovering virtually all of the signal power captured by the array feed, even in the presence of severe wind gusts and similar disturbances.>

The performance of general binary block codes on an unquantized additive white Gaussian noise (AWGN) channel at low signal-to-noise ratios is considered. Expressions are derived for both the block error and the bit error probabilities near the point where the bit signal-to-noise ratio is zero. These expressions depend on the global geometric structure of the code, although the minimum distance still seems to play a crucial role. Examples of codes such as orthogonal codes, biorthogonal codes, the (24,12) extended Golay code, and the (15,6) expurgated BCH code are discussed. The asymptotic coding gain at low signal-to-noise ratios is also studied.>

Techniques from coding theory are applied to study rigorously the capacity of the Hopfield associative memory. Such a memory storesn-tuple of\pm 1's. The components change depending on a hard-limited version of linear functions of all other components. With symmetric connections between components, a stable state is ultimately reached. By building up the connection matrix as a sum-of-outer products ofmfundamental memories, one hopes to be able to recover a certain one of themmemories by using an initialn-tuple probe vector less than a Hamming distancen/2away from the fundamental memory. Ifmfundamental memories are chosen at random, the maximum asympotic value ofmin order that most of themoriginal memories are exactly recoverable isn/(2 \log n). With the added restriction that every one of themfundamental memories be recoverable exactly,mcan be no more thann/(4 \log n)asymptotically asnapproaches infinity. Extensions are also considered, in particular to capacity under quantization of the outer-product connection matrix. This quantized memory capacity problem is closely related to the capacity of the quantized Gaussian channel.

Motivated by a problem in optical communication, we consider the general problem of maximizing the entropy of a stationary random process that is subject to an average transition cost constraint. Using a recent result of Justesen and Hoholdt, we present an exact solution to the problem and suggest a class of finite state encoders that give a good approximation to the exact solution.

The structure of a maximum {\em a posteriori} decoder is derived for binary optical fields processed according to Kennedy's near-optimum scheme, but operating in the presence of random optical phase-error and interfering background fields. An exact expression is found for the error probability, and examples to illustrate decoder performance under various operating conditions of interest are provided.

Previous work has shown that the capacity of the single-mode photon-counting channel in the region in which quantum effects are most pronounced is1/(kT \ln 2)bit/J. HereTis the quantum noise temperature, andkis Boltzmann's constant. However, to achieve this, the number of photons per second must be very low, so this does not determine the capacity of the average power-limited photon channel in bits per second. Here the previous work is extended and the capacity is determined in bits per second. This includes a determination of the optimum counting time. The capacity in bits per second is for lowTasymptotic toP_{s}/(kT \ln 2), whereP_{s}is the average signal power. This is high enough to suggest that photon counting be seriously considered for applications such as a deep space to near Earth link. Some related problems are also considered including a peak power limitation.

With the Delsarte-MacWilliams inequalities as a starting point, an upper bound is obtained on the rate of a binary code as a function of its minimum distance. This upper bound is asymptotically less than Levenshtein's bound, and so also Elias's.

Abstract We consider a model which is applicable to time‐multi‐plexed systems, such as multiplexed communication channels and time‐shared computing facilities. In this (processor‐sharing) queueing model, all jobs currently in the system share equally the processing capability of the server. In this paper, we investigate the processor‐sharing model for the case of bulk arrivals. The mean response time of the system as a function of required service time is derived. An example is given to show the effect of bulk arrivals versus single arrivals for a constant utilization.