Autonomous Software-Defined Radio Receivers for Deep Space by Jon Hamkins, Marvin K. Simon, Joseph H. Yuen

By Jon Hamkins, Marvin K. Simon, Joseph H. Yuen

This publication introduces the reader to the concept that of an self sufficient software-defined radio (SDR) receiver. every one specific point of the layout of the receiver is handled in a separate bankruptcy written by way of a number of prime innovators within the box. Chapters commence with an issue assertion after which provide a whole mathematical derivation of an acceptable resolution, a call metric or loop-structure as applicable, and function effects.

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Extra resources for Autonomous Software-Defined Radio Receivers for Deep Space Applications (JPL Deep-Space Communications and Navigation Series)

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These include the blocks that estimate or classify the data rate, modulation index, and modulation type. This monograph derives architectures for each of these from scratch, in most cases by formulating the ML criterion for the estimator and attempting to solve it analytically. This led to excellent solutions for modulation classification, SNR estimation, and frequency tracking. In some other cases, the ML solution was not tractable, and promising ad hoc schemes were identified. We briefly summarize the status of the design and analysis of some of these estimators below.

1-1. Signal dependency graph. , [S]), and applications using convolutional codes nearly always use codes from these tables. CRC codes of a given length also typically use standard generator polynomials [7]. RS codes are specified by their blocklength, rate, field generator polynomial, and code generator polynomials. The latter two can be one of several possibilities, but in practice space communication systems have primarily used the one that is specified in the CCSDS standard [6]. The techniques of puncturing, shortening, and expurgating are commonly used to modify a code.

The first steps in the acquisition process are computation of errors for the two node phases. This is done sequentially. The results of these error counts are denoted by the “Integrate @o(So,N*)” and “Integrate @1(Si,N+)”blocks in Fig. 2-7. NQ is the number of bits used in the integration process (a multiple of 1000 bits), and Si denotes error count for each code phase, @i. If the difference in the error counts is sufficiently large, IS0 - ,911 2 (YO (00 is a pre-determined threshold between 0 and 255) and the smallest of the two error counts, is less than a second pre-determined threshold a1 (between 0 and 2048), then the node phase is set to the phase associated with the minimum error count and acquisition is completed.

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