This book introduces the reader to the concept of an autonomous software–defined radio (SDR) receiver, which automatically recognizes attributes of an incoming signal and reconfigures itself to receive it. This is in contrast to conventional software–defined radios, which are reconfigurable but not autonomous.
The book explores the challenges of such automatic reconfiguration, and explains the design and development of algorithms that permit its successful operation. Among the topics covered are automatic identification of the carrier frequency, modulation index, data rate, modulation type, and pulse shape, based on observations of the received signal.
Each distinct aspect of the design of the receiver is treated in a separate chapter written by one or more leading innovators in the field. Chapters begin with a problem statement and then offer a full mathematical derivation of an appropriate solution, a decision metric or loop–structure as appropriate, and performance results.
Two of the chapters serve specifically to pull all the individual elements together and help readers see how a successful autonomous SDR receiver works:
- Chapter 2, The Electra Radio, provides a detailed review of NASA′s Electra radio, which was developed for deep space applications
- Chapter 11, Implementation and Interaction of Estimators and Classifiers, the final chapter, demonstrates the performance of an actual software implementation of the various algorithms working in concert with each other
In summary, all the materials and techniques that an engineer needs to implement an autonomous SDR are included. Although the technology is intended for deep space applications, the theoretical development and algorithms presented in this text can be applied to any terrestrial radio with the capability of processing more than one type of signal.
Chapter 1 : Introduction and Overview (Jon Harnkins and Marvin K . Simon).
1.2 Radio Receiver Architectures.
1.3 Estimators and Classifiers of the Autonomous Radio.
1.4 An Iterative Message–Passing Architecture.
1.5 A Demonstration Testbed.
Chapter 2: The Electra Radio (Edgar Satorius. Tom Jedrey. David Bell. Ann Devereaux. Todd Ely. Edwin Grigorian. Igor Kuperman. and Alan Lee).
2.1 Electra Receiver Front–End Processing.
2.2 Electra Demodulation.
2.3 Electra Digital Modulator.
Chapter 3: Modulation Index Estimation (Marvin K. Simon and Jon Hamkins).
3.1 Coherent Estimation.
3.2 Noncoherent Estimation.
3.3 Estimation in the Absence of Knowledge of the Modulation. Data Rate. Symbol Timing. and SNR.
3.4 Noncoherent Estimation in the Absence of Carrier Frequency Knowledge.
Chapter 4: Frequency Correction (Dariush Divsalar).
4.1 Frequency Correction for Residual Carrier.
4.2 Frequency Correction for Known Data–Modulated Signals.
4.3 Frequency Correction for Modulated Signals with Unknown Data.
Chapter 5: Data Format and Pulse Shape Classification (Marvin K . Simon and Dariush Divsalar).
5.1 Coherent Classifiers of Data Format for BPSK.
5.2 Coherent Classifiers of Data Format for QPSK.
5.3 Noncoherent Classification of Data Format for BPSK.
5.4 Maximum–Likelihood Noncoherent Classifier of Data Format for QPSK.
5.5 Maximum–Likelihood Coherent Classifier of Data Format for BPSK with Residual and Suppressed Carriers.
5.6 Maximum–Likelihood Noncoherent Classifier of Data Format for BPSK with Residual and Suppressed Carriers.
5.7 Maximum–Likelihood Pulse Shape Classification.
Chapter 6: Signal–to–Noise Ratio Estimation (Marvin K . Simon and Samuel Dolinar).
6.1 Signal Model and Formation of the Estimator.
6.2 Methods of Phase Compensation.
6.3 Evaluation of h+/–.
6.4 Mean and Variance of the SNR Estimator.
6.5 SNR Estimation in the Presence of Symbol Timing Error.
6.6 A Generalization of the SSME Offering Improved Performance.
6.7 A Method for Improving the Robustness of the Generalized SSME.
6.8 Special Case of the SSME for BPSK–Modulated Data.
6.9 Comparison with the Cramer–Rao Lower Bound on the Variance of SNR Estimators.
6.10 Improvement in the Presence of Frequency Uncertainty.
6.11 The Impact of the Ovzrsampling Factor on the Performance of the Modified SSME in the Presence of Symbol Timing Error.
6.12 Other Modulations.
6.13 The Time–Multiplexed SSME.
Appendix 6–A Derivation of Asymptotic Mean and Variance of SSME.
Chapter 7: Data Rate Estimation (by Andre Tkacenko and Marvin K . Simon).
7.1 Data Rate Estimation Based on the Mean of the SSME SNR Estimator.
7.2 Effects of Symbol–Timing Error on Estimating the Data Rate.
7.3 Quantization of the Symbol–Timing Error.
7.4 Simulation Results for the SSME–Based Estimation Algorithms.
Chapter 8: Carrier Synchronization (by Marvin K . Simon and Jon Harnkins).
8.1 Suppressed versus Residual Carrier Synchronization.
8.2 Hybrid Carrier Synchronization.
8.3 Active versus Passive Arm Filters.
8.4 Carrier Synchronization of Arbitrary Modulations.
Appendix 8–A Cramer–Rao Bound on the Variance of the Error in Estimating the Carrier Phase of a BPSK Signal.
Chapter 9: Modulation Classification (Jon Hamkins and Marvin K . Simon).
9.2 Modulation Classifiers.
9.3 Threshold Optimization.
9.5 Classification Error Floor.
9.6 Numerical Results.
9.7 Unknown Symbol Timing.
9.8 BPSK/a/4–QPSK Classification.
9.9 Noncoherent Classification of Offset Quadrature Modulations.
9.10 Modulation Classification in the Presence of Residual Carrier Frequency Offset.
Appendix 9–A Parameter Estimation for the GLRT.
Appendix 9–6 ML Estimation of Carrier Phase for al4–QPSK Modulation.
Chapter 10: Symbol Synchronization (Marvin K . Simon).
10.1 MAP–Motivated Closed–Loop Symbol Synchronization.
10.2 The DTTL as an Implementation of the MAP Estimation Loop for Binary NRZ Signals at High SNR.
10.3 Conventional versus Linear Data Transition Tracking Loop.
10.4 Simplified MAP–Motivated Closed–Loop Symbol Synchronizers for M–PSK.
10.5 MAP Sliding–Window Estimation of Symbol Timing.
10.6 Symbol Synchronization in the Absence of Carrier Phase Information.
10.7 The Impact of Carrier Frequency Offset on Performance.
10.8 Coarse Estimation of Symbol Timing for Use in SNR Estimation.
Chapter 11 : Implementation and lnteraction of Estimators and Classifiers (Jon Harnkins and Hooman Shirani–Mehr)
11. 1 Signal Model.
11.2 Interaction of Estimator and Classifiers.
11.3 Coarse and Fine Estimators/Classifiers.
Acronyms and Abbreviations.
MARVIN K. SIMON, PhD, is a Senior Research Engineer at the Jet Propulsion Laboratory. His research in modulation, coding, and synchronization has been instrumental in the design of many of NASA′s deep space and near earth missions, for which he has been awarded dozens of patents and awards. This is Dr. Simon′s twelfth book.