Shannon limit on power efficiency – demystified

The Shannon power efficiency limit is the limit of a band-limited system irrespective of modulation or coding scheme. It informs us the minimum required energy per bit required at the transmitter for reliable communication. It is also called unconstrained Shannon power efficiency Limit. If we select a particular modulation scheme or an encoding scheme, we calculate the constrained Shannon limit for that scheme.

Before proceeding, I urge you to go through the fundamentals of Shannon Capacity theorem in this article.

This article is part of the book
Wireless Communication Systems in Matlab (second edition), ISBN: 979-8648350779 available in ebook (PDF) format and Paperback (hardcopy) format.

Channel capacity and power efficiency

One of the objective of a communication system design is to reliably send information at the lowest possible power level. The system should be able to provide acceptable bit-error-rate (BER) performance at the lowest possible power level. Often, this performance is charted in terms of BER Vs. E_b/N_0. The quantity E_b/N_0 is called power efficiency, denoted as \eta_P. Power efficiency is defined as the ratio of signal energy per bit (E_b) to noise power spectral density per bit (N_0 – required at the receiver input to achieve certain BER.

\eta_P = \frac{E_b}{N_0} \quad \quad (1)

From equations (1) and (2) shown in this post, the condition for reliable transmission through a channel is given by

\eta_B = \frac{R}{B} < \frac{C}{B} = log_2 \left( 1 + \frac{E_b R}{N_0 B} \right) \quad \quad (2)

Re-writing in terms of spectral efficiency \eta_B, the Shannon limit on power efficiency \eta_P for reliable communication is given by

\frac{E_b}{N_0} > \frac{2^{\eta_B} - 1}{\eta_B}\quad \quad (3)

With this equation, we can calculate the minimum E_b/N_0 required to achieve a certain spectral efficiency. As an example, lets simulate and plot the relationship between E_b/N_0 and spectral efficiency \eta_B, as given in equation (3).

k =0.1:0.001:15; EbN0=(2.ˆk-1)./k;
semilogy(10*log10(EbN0),k);
xlabel('E_b/N_o (dB)');ylabel('Spectral Efficiency (\eta)');
title('Channel Capacity & Power efficiency limit')
hold on;grid on; xlim([-2 20]);ylim([0.1 10]);
yL = get(gca,'YLim');
line([-1.59 -1.59],yL,'Color','r','LineStyle','--');

The ultimate Shannon limit

From the plot in Fig. 1, we notice that the Shannon limit on E_b/N_0 is a monotonic function of \eta_B. When \eta_B=2, the Shannon limit on E_b/N_0 is equal to 1.76 \; dB. If \eta_B=1, the limit is at 0\;dB. When \eta \rightarrow 0, the Shannon limit on E_b/N_0 approaches -1.59 \;dB. This value is called ultimate Shannon limit or specifically absolute Shannon power efficiency limit. This limit informs us the minimum required energy per bit required at the transmitter for reliable communication. It is one among the important measures in designing a coding scheme.

Shannon power efficiency limit
Figure 1: Shannon Power Efficiency Limit

The ultimate Shannon limit can be derived using L’Hospital’s rule as follows. The asymptotic value, (E_b/N_0)_{min}, that we are seeking, is the value of E_b/N_0 as the spectral efficiency \eta approaches 0.

\left(\frac{E_b}{N_0}\right)_{min}=\lim _{n \to 0}\left( \frac{E_b}{N_0} \right)=\lim_{n \to 0}\left( \frac{2^\eta -1 }{\eta}\right) \quad\quad (4)

Let f(\eta)=2^\eta-1 and g(\eta)= \eta. As f(0)=g(0)=0 and the argument of the limit becomes indeterminate (0/0), L’Hospital’s rule can be applied in this case. According to L’Hospital’s rule, if \lim_{\eta \to k} f(\eta) and \lim_{\eta \to k}g(\eta) are both zero or are both \pm \infty, then for any value of k.

\lim_{\eta \to k} \left( \frac{f(\eta)}{g(\eta)} \right) = \lim_{\eta \to k} \left( \frac{f'(\eta)}{g'(\eta)} \right)\quad\quad (5)

Thus, the next step boils down to finding the first derivative of f(\eta) and g(\eta). Expressing 2^n in natural logarithm.

2 =e^{ln2} \quad\quad (6)
2^\eta = (e^{ln2} )^{\eta}=(e^{\eta ln2}) \quad\quad (7)

Let u= \eta ln(2) and y=e^u, then by chain rule of differentiation,

f'(\eta) =\frac{d2^\eta}{d \eta} = \frac{dy}{d \eta} = \frac{dy}{du} \frac{du}{d \eta} = e^u ln(2) = e^{\eta ln 2} ln(2) \quad\quad (8)

Since g(\eta)=\eta, the first derivative of g(\eta) is

g'(\eta) = 1 \quad \quad (9)

Using equations (8) and (9), and applying L’Hospital’s rule, the Shannon’s limit on E_b/N_0 is given by

equation for Shannon's limit for power efficiency
Shannon limit illustrated using 2-PAM modulation on AWGN channel
Figure 2: Shannon limit illustrated using 2-PAM modulation on AWGN channel

Unconstrained and constrained Shannon limit

The absolute Shannon power efficiency limit is the limit of a band-limited system irrespective of modulation or coding scheme. This is also called unconstrained Shannon power efficiency Limit. If we select a particular modulation scheme or an encoding scheme, we calculate the constrained Shannon limit for that scheme.

Shannon power efficiency limit does not depend on error probability. Shannon limit tells us the minimum possible E_b/N_0 required for achieving an arbitrarily small probability of error as M \to \infty, where M is the number of signaling levels for the modulation technique, for BPSK M=2, QPSK M=4 and so on. It gives the minimum possible E_b/N_0 that satisfies the Shannon theorem. In other words, it gives the minimum possible E_b/N_0 required to achieve maximum transmission capacity ( R=C, where, R is the rate of transmission and C is the channel capacity). It will not specify error probability at that limit. Nor will it give any direction on coding technique that can be used to achieve that limit. As the capacity is approached, the system complexity will increase drastically. So the aim of any system design is to achieve that limit. For example, the error probability performances of Turbo codes are very close to Shannon limit [1].

As an example, let’s evaluate the performance of a 2-PAM (Pulse Amplitude Modulation) system and determine the maximum possible coding gain that can be achieved by the most advanced coding scheme. The methodology for simulating the performance of a 2-PAM system is described in chapter 5 and 6. Using this methodology, the performance of a 2-PAM system is simulated and plotted in Figure 2. The absolute Shannon power efficiency limits when the spectral efficiency is \eta=0 and \eta=2 are also referenced on the plot.

The spectral efficiency of an ideal 2-PAM system is \eta=2 \;bits/second/Hz. Hence, if the target bit error rate is 10^{-5}, then a coding gain of 7.8\;dB can be achieved using powerful codes, if we have to maintain the nominal spectral efficiency at \eta=2 \;bits/second/Hz.

If there is no limit on the spectral efficiency, then we can let \eta \rightarrow 0. In this case, the absolute Shannon power efficiency limit is -1.59 \;dB when \eta \rightarrow 0. Thus a coding gain of approximately 11 \; dB is possible with powerful codes if we let the spectral efficiency approach zero.

Rate this article: PoorBelow averageAverageGoodExcellent (4 votes, average: 5.00 out of 5)

References

[1] C. Berrou, A. Glavieux, and P. Thitimajshima, Near Shannon limit error-correcting coding and decoding: Turbocodes, IEEE Int. Conf. on Comm., (ICC ’93), 2:1064-1070, May 1993.↗

 Related topics in this chapter

Introduction
Shannon’s noisy channel coding theorem
Unconstrained capacity for bandlimited AWGN channel
● Shannon’s limit on spectral efficiency
Shannon’s limit on power efficiency
● Generic capacity equation for discrete memoryless channel (DMC)
 □ Capacity over binary symmetric channel (BSC)
 □ Capacity over binary erasure channel (BEC)
● Constrained capacity of discrete input continuous output memoryless AWGN channel
● Ergodic capacity over a fading channel

Books by the author

Wireless Communication Systems in Matlab
Wireless Communication Systems in Matlab
Second Edition(PDF)

Note: There is a rating embedded within this post, please visit this post to rate it.
Digital modulations using Python
Digital Modulations using Python
(PDF ebook)

Note: There is a rating embedded within this post, please visit this post to rate it.
digital_modulations_using_matlab_book_cover
Digital Modulations using Matlab
(PDF ebook)

Note: There is a rating embedded within this post, please visit this post to rate it.
Hand-picked Best books on Communication Engineering
Best books on Signal Processing

Post your valuable comments !!!