A self-configurable radio receiver system was developed for relaying communication signals from multiple deep space assets. Most conventional radio receivers are hardwired to receive a specific type of signal and are incapable of receiving other types of signals without preconfiguration according to a predetermined schedule. The new radio receiver can recognize multiple signal types and autonomously reconfigure itself to decode the particular type of signal that is received.
Without prior knowledge of the defining characteristics of the received signals, this receiver accurately recognizes these characteristics and responds intelligently to define the functions of the receiver without explicit preconfiguration or reprogramming. This saves configuration time on the ground; in space, this allows in-situ reception without complicated reconfiguration commands from Earth.
The radio receiver system includes a suite of software modules for autonomously recognizing the various attributes that define a signal including the angle of arrival, data rate, symbol timing, carrier frequency and phase, modulation index and type, signal-to-noise ratio (SNR), code type, and decoded message bits. Through multiple iterations of estimation and calculation, the system is able to make a maximum-likelihood estimate for each of the defining characteristics and select an appropriate decoder for decoding the received signal.
Since the quality of each of the estimators is limited by its lack of knowledge of any of the other parameters, the order in which these estimations and calculations are performed is critical. Partially ordering the receivers estimators yields a workable bootstrapping approach to estimating all of the parameters necessary for proper operation of the entire receiver. After each estimator completes its first estimate in the proper bootstrap order, the deeper-level estimators send soft information to the upper-level estimators; this begins a second iteration, wherein each estimator makes use of this additional extrinsic information to improve its performance. Such coupled systems are typically quite robust and can provide near maximum likelihood estimation and decoding.