1. Digital Communications I: Modulation and Coding Course Spring - 2013 Jeffrey N. Denenberg Lecture 3b: Detection and Signal Spaces

2. Lecture 3b2 Last time we talked about: Receiver structure Impact of AWGN and ISI on the transmitted signal Optimum filter to maximize SNR Matched filter receiver and Correlator receiver

3. Lecture 43 Receiver job Demodulation and sampling: Waveform recovery and preparing the received signal for detection: Improving the signal power to the noise power (SNR) using matched filter Reducing ISI using equalizer Sampling the recovered waveform Detection: Estimate the transmitted symbol based on the received sample

4. Lecture 44 Receiver structure Digital Receiver Frequency down-conversion Receiving filter Equalizing filter Threshold comparison For bandpass signals Compensation for channel induced ISI Baseband pulse (possibly distored)‏ Sample (test statistic)‏ Baseband pulse Received waveform Step 1 – waveform to sample transformation Step 2 – decision making Demodulate & SampleDetect

5. Lecture 45 Implementation of matched filter receiver Bank of M matched filters Matched filter output: Observation vector

6. Lecture 46 Implementation of correlator receiver Bank of M correlators Correlators output: Observation vector

7. Lecture 47 Today, we are going to talk about: Detection: Estimate the transmitted symbol based on the received sample Signal space used for detection Orthogonal N-dimensional space Signal to waveform transformation and vice versa

8. Lecture 48 Signal space What is a signal space? Vector representations of signals in an N-dimensional orthogonal space Why do we need a signal space? It is a means to convert signals to vectors and vice versa. It is a means to calculate signals energy and Euclidean distances between signals. Why are we interested in Euclidean distances between signals? For detection purposes: The received signal is transformed to a received vector. The signal which has the minimum Euclidean distance to the received signal is estimated as the transmitted signal.

9. Lecture 49 Schematic example of a signal space Transmitted signal alternatives Received signal at matched filter output

10. Lecture 410 Signal space To form a signal space, first we need to know the inner product between two signals (functions): Inner (scalar) product: Properties of inner product: = cross-correlation between x(t) and y(t) Analogous to the “dot” product of discrete n-space vectors

11. Lecture 411 Signal space … The distance in signal space is measure by calculating the norm. What is norm? Norm of a signal: Norm between two signals: We refer to the norm between two signals as the Euclidean distance between two signals. = “length” or amplitude of x(t)‏

12. Lecture 412 Example of distances in signal space The Euclidean distance between signals z(t) and s(t):

13. Lecture 413 Orthogonal signal space N-dimensional orthogonal signal space is characterized by N linearly independent functions called basis functions. The basis functions must satisfy the orthogonality condition where If all, the signal space is orthonormal. See my notes on Fourier SeriesFourier Series

14. Lecture 414 Example of an orthonormal basis Example: 2-dimensional orthonormal signal space Example: 1-dimensional orthonormal signal space Tt 0 0 0

15. Lecture 415 Signal space … Any arbitrary finite set of waveforms where each member of the set is of duration T, can be expressed as a linear combination of N orthonogal waveforms where. where Vector representation of waveform Waveform energy

16. Lecture 416 Signal space … Waveform to vector conversionVector to waveform conversion

17. Lecture 417 Example of projecting signals to an orthonormal signal space Transmitted signal alternatives

18. Lecture 418 Signal space – cont’d To find an orthonormal basis functions for a given set of signals, the Gram-Schmidt procedure can be used. Gram-Schmidt procedure: Given a signal set, compute an orthonormal basis 1. Define 2. For compute If let If, do not assign any basis function. 3. Renumber the basis functions such that basis is This is only necessary if for any i in step 2. Note that

19. Lecture 419 Example of Gram-Schmidt procedure Find the basis functions and plot the signal space for the following transmitted signals: Using Gram-Schmidt procedure: Tt Tt -AA 0 0 0 Tt 0 1 2

20. Lecture 420 Implementation of the matched filter receiver Bank of N matched filters Observation vector

21. Lecture 421 Implementation of the correlator receiver Bank of N correlators Observation vector

22. Lecture 422 Example of matched filter receivers using basic functions Number of matched filters (or correlators) is reduced by 1 compared to using matched filters (correlators) to the transmitted signal. Reduced number of filters (or correlators)‏ Tt Tt Tt 0 1 matched filter Tt 0 0 0

23. Lecture 423 White noise in the orthonormal signal space AWGN, n(t), can be expressed as Noise projected on the signal space which impacts the detection process. Noise outside of the signal space Vector representation of independent zero-mean Gaussain random variables with variance