Lecture 1 of the Wireless Communications course (SSY135) at Chalmers University of Technology. Academic year 2018-2019. Slides available at tinyurl.com/y5...
Just got a chance to shift my job to system research from baseband developer then I suddenly find myself forget all these MIMO stuff. But what a surprise to find your lectures here!
Thanks for the lecture The below is just some of my note and questions. I find that I have not learnt and forgot most of the basic. haha 1. delay spread; time between first response and last response; typically a few nanosecond 10^-9 2. coherence time; time that channel is roughly the same; a order of ms 10^-3; how to define roughly the same? 3. Channel can be represented by 3 components: path loss; shadowing; multipaths 4. k; index/number of path ? 5. small change in delay multiplied with a very high f_c result in high rotation ? 6. Amplitude; frequency; delay are varying over time and paths at 7:01 7. a_k is symbol, p is a pulse shape? what is pulse shape at 8:25 8. dB and dBm at 9:50 9. what is u(t - tau)? signal? c(tau) is impulse response? at 10:30 10. what is mean by "when the input signal goes through this"? I see some rectangular boxes in the graph but what do they mean? 11. time varying fourier transform: C(f, t) = fourier transform of c(t, tau) with respect to tau. so what is it? it there any example? at 11:26 12. complex guassian: CN(mui, sigma^2) 13. WSS: is channel WSS? why we need WSS? 14. PSD: why F(autocorr) is PSD? how will we use PSD? I just heard the white noise PSD is N_0/2 and but why? at 14:32 15. s_k is symbol. what is the subscript k meaning? path ? 16. gamma_s mean SNR per symbol 17. why E_s need square root? 18. why n_k variance become N_0? not N_0/2? 19. gamma_b mean SNR per bit 20. gamma_b is Es/log(2,M) and then divided by N_0 21. how to construct a max likelihood detector? 22. how to construct the graph of Pe vs SNR for different constellation? at 17:14 23. convex optim, maybe I should take another course on it. 24. No idea about it at 18:57 25. SVD..... I need to re-remember it 26. maybe I should take another course on it. at 20:08 1. main property 1.1. refer to 3. Channel can be represented by 3 components: path loss; shadowing; multipaths 2. challenges?????? same as properties?
Thanks for the excellent lecture.... slide 15 states Gaussian noise process PSD ...the written expression is in fact White Noise ... Gaussian does not have to be white
Yes, you are correct. Only if the PSD is constant is the noise white. If the PSD varies as a function of frequency, the noise is colored. In this course, we only consider white noise with flat PSD.
Thank you so much. Could you please explain what "passband" and "baseband" are in slide 10? I understand we use complex numbers in baseband propcessing, but it is anti-instinct that parameter "fc" appears in h_BB rather than h_PB.
The baseband signal is the complex signal centered around zero frequency. Let's call this signal s(t) (complex) The passband signal is the real signal, which is obtained after up-conversion. Let's call this signal x(t) (real). Then x(t) =Real (s(t) exp(j 2\pi f_c t)). This signal passes through the channel, the received baseband signal will depend on the carrier frequency. More examples are given in the later lectures.
Because in wireless communications, we generally work with a complex baseband representation of signals, rather than real passband signals. This makes the math simpler on the long run.
