References & Further Reading

References

1. Haykin, S. and Moher, M., Communication Systems, Wiley, 5th ed., 2009

   Comprehensive coverage of signals, systems, and analog/digital communications. Chapters 2–3 parallel our Sections 4.1–4.4.

2. Oppenheim, A. V. and Willsky, A. S., Signals and Systems, Prentice Hall, 2nd ed., 1997

   The classic signals and systems textbook. Rigorous treatment of CT/DT systems, Fourier analysis, and the Z-transform.

3. Proakis, J. G. and Manolakis, D. G., Digital Signal Processing: Principles, Algorithms, and Applications, Pearson, 4th ed., 2007

   In-depth coverage of sampling, DFT/FFT, and digital filter design. Useful for the discrete-time material in Section 4.3.

4. Tse, D. and Viswanath, P., Fundamentals of Wireless Communication, Cambridge University Press, 2005

   Chapter 2 covers the wireless channel model using the LTV framework. Excellent treatment of delay/Doppler spreading.

5. Goldsmith, A., Wireless Communications, Cambridge University Press, 2005

   Chapters 2–3 cover signals, systems, and channel modelling. Good bridge between signal theory and wireless applications.

6. Bello, P. A., Characterization of Randomly Time-Variant Linear Channels, IEEE Transactions on Communications Systems, vol. 11, no. 4, 1963

   The seminal paper establishing the four system functions framework for LTV channel characterisation.

7. Papoulis, A. and Pillai, S. U., Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 4th ed., 2002

   Rigorous treatment of random processes, PSD, and the Wiener–Khinchin theorem. Reference for Section 4.6.

8. Turin, G. L., An Introduction to Matched Filters, IRE Transactions on Information Theory, vol. 6, no. 3, 1960

   Classic tutorial on the matched filter and its optimality.

Further Reading

• Wavelet transforms and time–frequency analysis

  Mallat, S. — A Wavelet Tour of Signal Processing (Academic Press, 2009); Hlawatsch & Auger — Time-Frequency Analysis (Wiley, 2008)

  The Fourier transform gives global frequency content but loses time localisation. Wavelets and the short-time Fourier transform provide joint time–frequency representations — essential for analysing non-stationary signals and designing OFDM systems.

• Compressive sensing and sub-Nyquist sampling

  Eldar & Kutyniok — Compressed Sensing (Cambridge, 2012); Mishali & Eldar — "Sub-Nyquist Sampling" (IEEE SPM, 2011)

  The Nyquist theorem assumes no structure in the signal. If the signal is sparse, it can be reconstructed from far fewer samples — with profound implications for wideband spectrum sensing in cognitive radio.

• Advanced matched filtering and detection theory

  Van Trees — Detection, Estimation, and Modulation Theory, Part I (Wiley, 2001); Kay — Fundamentals of Statistical Signal Processing: Detection Theory (Prentice Hall, 1998)

  The matched filter is optimal only for known signals in AWGN. For unknown or partially known signals, detection theory provides the Neyman–Pearson, Bayes, and GLRT frameworks.