I am confused understanding basic concepts of communication over AWGN channels. I know the capacity of a discrete time AWGN channel is: $$C=\frac{1}{2}\log_2\left(1+\frac{S}{N}\right)$$ and it is achieved when the input signal has Gaussian distribution. But, what does it mean that the input signal is Gaussian? Does it mean that the amplitude of each symbol of a codeword must be taken from a Gaussian ensemble? What is the difference between using a special codebook (in this case Gaussian) and modulating the signal with M-ary signaling, say MPSK?


Assuming a channel whose input at each time is a continuous random variable $X$ and its output is $Y=X+Z$, where $Z\sim\mathcal{N}(0,N)$ and $Z$ is independent of $X$, then $$C_{\text{CI-AWGN}}=\frac{1}{2}\log_2\left(1+\frac{P}{N}\right)$$ is the capacity of the continuous-input channel under the power constraint $$\mathsf{E}X^2\le P$$ The mutual information $I(X;Y)$ is maximized (and is equal to $C_{\text{CI-AWGN}}$) when $X\sim\mathcal{N}(0,P)$.

This means that if $X$ is a continuous Gaussian random variable with the given variance, then the output has the highest possible mutual information with the input. That's it!

When the input variable $X$ is discretized (quantized), a new formulation is required. Indeed, things can easily become difficult. To see it a little bit, one can consider the simple case of a very coarse discritization of $X$ where it can only have two values. So assume that $X$ is selected from a binary alphabet, for instance let $X\in\{\pm1\}$ (or a scaled version to satisfy a power constraint). In terms of modulation, it is identical to BPSK.

It turns out that the capacity (even in this simple case) has no closed form. I report from "Modern Coding Theory" by Richardson and Urbanke:

$$\begin{align}C&_{\text{BI-AWGN}}=1+\\&\frac{1}{\ln(2)} \left(\left(\frac{2}{N}-1\right)\mathcal{Q}\left(\frac{1}{\sqrt{N}}\right)-\sqrt{\frac{2}{\pi N}}e^{-\frac{1}{2N}}+\sum_{i=1}^{\infty}\frac{(-1)^i}{i(i+1)}e^{\frac{2i(i+1)}{N}}\mathcal{Q}\left(\frac{1+2i}{\sqrt{N}}\right)\right)\end{align}$$ A comparison between the two cases can be seen in the figure below:

enter image description here

  • $\begingroup$ What would you do if you want to get closer to the capacity? using a higher order PSK scheme? $\endgroup$ – Mah May 2 '17 at 14:25
  • $\begingroup$ @msm I have always believed that FEC is a general concept including H-ARQ, or H-ARQ is a just trick to reduce the codeword length per transmission, i.e. to reduce the complexity of decoding, with the cost of longer total transmission time, isn't it ? $\endgroup$ – AlexTP May 3 '17 at 14:06
  • $\begingroup$ @msm Please stop deleting your old, valuable posts! $\endgroup$ – Peter K. Jan 2 at 13:41
  • $\begingroup$ @msm When you signed up for SP.SE, you gave the site an irrevocable license to use the content. Please stop deleting your valuable content. $\endgroup$ – Peter K. Jan 2 at 14:55

The capacity formula $$C = 0.5 \log (1+\frac{S}{N}) \tag{1}$$ is for discrete time channel.

Assuming you have a sequence of data $\left\lbrace a_n \right\rbrace$ to send out, you need an orthonormal waveform set $\left\lbrace \phi_n(t) \right\rbrace$ for modulation. In linear modulation, whom M-ary modution belongs to, $\phi_n(t) = \phi(t - nT)$ where $T$ is symbol duration and $\phi(t)$ is prototype waveform so that the baseband continous time TX signal becomes $$x(t) = \sum_n a_n \phi(t-nT) \tag{2}$$

Typical modulations use the special case that $\left\lbrace \phi_n(t) \right\rbrace$ satisfies the Nyquist ISI criterion with matched filter to recover $a_n$. A well-known $\phi(t)$ is Root raised cosine.

The continuous AWGN channel is a model that $$y(t) = x(t) + n(t) \tag{3}$$

where $n(t)$ is a Gaussian white stochastic process.

From (2), we can see that $a_n$ is the projection of $x(t)$ on $\left\lbrace \phi_n(t) \right\rbrace$. Do the same thing with $n(t)$, the projections of $n(t)$ on an orthonormal set is a sequence of iid Gaussian random variables $w_n = \langle n(t),\phi_n(t) \rangle$ (I really think that $n(t)$ is defined from its projections); and call $y_n = \langle y(t),\phi_n(t) \rangle$. Voilà, we have an equivalent discrete time model $$y_n = a_n + w_n \tag{4}$$

The formula (1) is stated for $S$ and $N$ are energy (variance if $a_n$ and $w_n$ are zero mean) of $a_n$ and $w_n$, respectively. If $a_n$ and $w_n$ are Gaussian, so is $y_n$ and the capacity is maximized. (I can add a simple proof if you want).

what does it mean that the input signal is Gaussian? Does it mean that the amplitude of each symbol of a codeword must be taken from a Gaussian ensemble?

It mean random variables $a_n$ are Gaussian.

What is the difference between using a special codebook (in this case Gaussian) and modulating the signal with M-ary signaling, say MPSK?

The waveform $\phi_n(t)$ set needs to be orthonormal, which is true for M-PSK, so that $w_n$ is iid Gaussian.

Update However $a_n$ is quantized so in general, it is not Gaussian anymore. There is some researchs about this topic, such as usage of Lattice Gaussian Coding (link).

  • $\begingroup$ @msm I meant "discrete time" channel. Yes, these random variables are continuous, their support are continuous. I have talked about continous time and discrete time because the author asked about modulation. $\endgroup$ – AlexTP May 2 '17 at 7:33
  • $\begingroup$ @msm my (3) is continuous, and (4) is the equivalent discrete. Physically in non-quantum scale, we are in (3). To analyze, we use (4). We are just talking about two different stuffs, I suppose. I have edited my answer to use the correct terminology. $\endgroup$ – AlexTP May 2 '17 at 7:35
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    $\begingroup$ @msm saw your answer and find out that I misunderstood what the author of question wanted to ask about modulation and what you are telling me. I have updated my answer to avoid the misleading part. Thanks. $\endgroup$ – AlexTP May 2 '17 at 11:48
  • $\begingroup$ "I really think that n(t) is defined from its projections" -- The problem is that white noise has infinite dimensions. What is interesting is that, for the problem of recovering $a_n$, only the projection on $\phi_n(t)$ is relevant -- all the other infinite possible projections do not help. See the "theorem of irrelevance". $\endgroup$ – MBaz May 2 '17 at 13:40
  • $\begingroup$ @MBaz yes I do agree. Theorem of irrelevance and theorem of sampling are the couple to etablish basic discrete time channel model. The orthogonal part is uncorrelated thus independent under Gaussian assumption. However I think I would not modify my answer because this projection stuff does not relate directly to the question. Thanks for making it clear. $\endgroup$ – AlexTP May 2 '17 at 14:05

To say that the input signal has a Gaussian distribution means that it is distributed as a Gaussian random variable. In practice, one relies on coding over multiple instances of the channel (in time) instead of relying on a Gaussian input distribution. There is a beautiful theory full of proofs that is beyond the scope of this answer (Information Theory). Error control codes (or channel codes) typically rely on the use of familiar QAM/PSK modulations, but through the redundancy of the code and multiple channel uses, they can approach (though not quite reach) the channel capacity. A sketch of the reasoning (without full details) is provided next.

The definition of channel capacity is $$ C = \sup_{p_X(x)} I(X; Y)$$ where $X$ can loosely be referred to as your input random variable, and $Y$ can loosely be referred to as your output random variable, and $I(\cdot,\cdot)$ is the Mutual Information of $X$ and $Y$. This definition requires us to search over all possible distributions of the input $p_X(x)$ for distributions that maximize the Mutual Information. The discrete AWGN channel has an input/output relationship defined as $$ Y = X + Z $$ where $Z$ is a zero mean Gaussian with variance $\sigma_Z^2$ (notice that $\sigma_Z^2=N$ and $\sigma_X^2=S$ in your notation). I don't have time to provide all of the details right now. However, any book on information theory can walk you through the proof that shows that if $X$ is distributed as a Gaussian then $I(X;Y)$ (the mutual information of $X$ and $Y$) is maximized. For example, see Elements of Information Theory by Thomas Cover. If you haven't read it yet, Shannon's original treatise A Mathematical Theory of Communication is a worthwhile read with clear reasoning throughout.

  • $\begingroup$ No reason given for the down vote? $\endgroup$ – hops May 2 '17 at 13:55

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