diff options
Diffstat (limited to 'dpd')
| -rw-r--r-- | dpd/README.md | 47 |
1 files changed, 47 insertions, 0 deletions
diff --git a/dpd/README.md b/dpd/README.md index 0ae70fb..24611d9 100644 --- a/dpd/README.md +++ b/dpd/README.md @@ -200,3 +200,50 @@ TODO the received signal should have a median absolute value of 0.05 in order to have a hight quality quantization. Do measurements to support or improve this heuristic. + - Check if we need to measure MER differently (average over more symbols?) + - Is -45dBm the best RX feedback power level? + +REFERENCES +---------- + +Some papers: + +The paper Raich, Qian, Zhou, "Orthogonal Polynomials for Power Amplifier +Modeling and Predistorter Design" proposes other base polynomials that have +less numerical instability. + +AladreĢn, Garcia, Carro, de Mingo, and Sanchez-Perez, "Digital Predistortion +Based on Zernike Polynomial Functions for RF Nonlinear Power Amplifiers". + +Jiang and Wilford, "Digital predistortion for power amplifiers using separable functions" + +Changsoo Eun and Edward J. Powers, "A New Volterra Predistorter Based on the Indirect Learning Architecture" + +Raviv Raich, Hua Qian, and G. Tong Zhou, "Orthogonal Polynomials for Power Amplifier Modeling and Predistorter Design" + + +Models without memory: + +Complex polynomial: y[i] = a1 x[i] + a2 x[i]^2 + a3 x[i]^3 + ... + +The complex polynomial corresponds to the input/output relationship that +applies to the PA in passband (real-valued signal). According to several +sources, this gets transformed to another representation if we consider complex +baseband instead. In the following, all variables are complex. + +Odd-order baseband: y[i] = (b1 + b2 abs(x[i])^2 + b3 abs(x[i])^4) + ...) x[i] + +Complete baseband: y[i] = (b1 + b2 abs(x[i]) + b3 abs(x[i])^2) + ...) x[i] + +with + b_k = 2^{1-k} \binom{k}{(k-1)/2} a_k + + +Models with memory: + + - Hammerstein model: Nonlinearity followed by LTI filter + - Wiener model: LTI filter followed by NL + - Parallel Wiener: input goes to N delays, each delay goes to a NL, all NL outputs summed. + +Taken from slide 36 of [ECE218C Lecture 15](http://www.ece.ucsb.edu/Faculty/rodwell/Classes/ece218c/notes/Lecture15_Digital%20Predistortion_and_Future%20Challenges.pdf) + |