I know that all periodic continuous time signal have discrete spectral representations, but are all discrete spectral representations periodic in continuous time?
Also, can all periodic signals be represented by a fourier series?
I know that all periodic continuous time signal have discrete spectral representations, but are all discrete spectral representations periodic in continuous time?
Also, can all periodic signals be represented by a fourier series?
This is a 2 parts answer:
All discrete spectral representations that exist strictly on a fixed spacing grid that includes 0 (DC) represent a signal that is periodic in time. Other grids (with different irrational number spacings for instance) can indicate aperiodic waveforms in continuous time.
Yes, all periodic signals can be represented by the Fourier Series, and the discrete time signals have periodic spectra (you can try this by applying DFT or FFT for a discrete time signal in MATLAB). The reason for this is you are trying to find the coefficients $X(e^{j \;\omega})$ using the formula for a DFT which has the term $\exp{(\dfrac{-j2\pi k n}{N})}$, because of which you can find periodicity in the spectrum.
i get into fights occasionally at the USENET newsgroup comp.dsp regarding the inherent nature of the DFT. but i'll repeat it here:
anytime one uniformly samples a continuous function in one domain, it makes it representable as a discrete function in that domain and it causes periodicity in the reciprocal domain. and anytime one makes a function periodic in one domain, it causes it to be discrete (appearing as uniformly sampled) in the reciprocal domain. that is always the case.
the thing that gets me in trouble with some of my peers (but not with the math, i'm quite comfortable with the DFT math) is that i (and not just me) conclude that the DFT transforms one discrete and periodic function (with period $N$) in one domain to another discrete and periodic function (having the same period $N$) in the reciprocal domain. but in both domains, the periodic function is discrete, so it is fully described with $N$ numbers in either domain.
this means that the DFT effectively periodically extends the data passed to it. you pass to the DFT (or FFT) $N$ samples, and the DFT will treat it as if it were one period of a periodic function. the DFT is essentially the same as the DFS.