M-QAM Modulation:

In M-ASK modulation the information symbols (each k=log2(M) bit wide) are encoded into the amplitude of the sinusoidal carrier. In M-PSK modulation the information is encoded into the phase of the sinusoidal carrier. M-QAM is a generic modulation technique where the information is encoded into both the amplitude and phase of the sinusoidal carrier. It combines both M-ASK and M-PSK modulation techniques.M-QAM modulation technique is a two dimensional modulation technique and it requires two orthonormal basis functions

\begin{matrix}\phi_I(t) = \sqrt{\frac{2}{T_s}} cos(2 \pi f_c t)& 0\leq t\leq T_s \\ \phi_Q(t) = \sqrt{\frac{2}{T_s}} sin(2 \pi f_c t) & 0\leq t\leq T_s \end{matrix}

The M-QAM modulated signal is represented as

\begin{matrix} S_i(t) = V_{I,i} \sqrt{\frac{2}{T_s}} cos(2 \pi f_c t) + V_{Q,i} \sqrt{\frac{2}{T_s}} sin(2 \pi f_c t) & 0\leq t\leq T_s\\ & i=1,2,...,M \end{matrix}

Here V_{I,i} and V_{Q,i} are the amplitudes of the quadrature carriers amplitude modulated by the information symbols.

Baseband Rectangular M-QAM modulator:

There exist other constellations that are more efficient (in terms of energy required to achieve same error probability) than the standard rectangular constellation. But due to its simplicity in modulation and demodulation rectangular constellations are preferred.

In practice, the information symbols are gray coded in-order to restrict the erroneous symbol decisions to single bit error, the adjacent symbols in the transmitter constellation should not differ more than one bit. Usually the gray coded symbols are separated into in-phase and quadrature bits and then mapped to M-QAM constellation. The rectangular configuration of QAM makes it easier to consolidate the previously mentioned steps into a simplified Look-Up-Table (LUT) approach.

Check here to know more on constructing a LUT for M-QAM modulation techniques.

16-QAM Modulation Scaling Factor:

In order to get a fair comparison across all other modulations, the energy transmitted signal has to be normalized. In general the constellation points for a M-QAM modulation can be generated as

The energy a single constellation point is calculated as E = {V_I}^2 + {V_Q}^2 . Where V_I and V_Q are the I and Q components of the signaling points. For a set of n constellation points, the total energy is calculated as

E = \sum_{i=1}^{n}\left ( {V_{I,i}}^2 + {V_{Q,i}}^2 \right ) .

In 16 QAM there are 16 signal points in the constellation that are equally divided into four quadrants (each with four constellation points). Since the constellation is divided equally into four quadrants, normalizing the energy in a single quadrant will simplify things.

Calculating the total energy in any one of the quadrant, say for example -the top-right quadrant,

E = \left ( 1^2+1^2\right )+\left ( 1^2+3^2\right )+\left ( 3^2+1^2\right )+\left ( 3^2+3^2\right )= 40

The average energy is E_{avg} = E/4 = 10 and the normalization factor will be 1/\sqrt{E_{avg}}=1/\sqrt{10} .

The values in the LUT (where the reference constellation is stored) are normalized by the above mentioned normalization factor and then the 16-QAM signal is generated.

Simulation Model:

The simulation model for M-QAM modulation is given in the next figure. The receiver uses Euclidean distance as a metric to decide on the received symbols.

Theoretical Symbol Error Rate:

The theoretical symbol error rate for M-QAM modulation technique in AWGN when k=log2(M) is even,is given by

P_s \leq 1-\left ( 1-2\left ( 1-\frac{1}{\sqrt{M}} \right )Q\left( \sqrt{\frac{3kE_b}{(M-1)N_0}}\right ) \right )^2

Or equivalently,
P_s \leq 1-\left ( 1-\left ( 1-\frac{1}{\sqrt{M}} \right )erfc\left(\sqrt{ \frac{3kE_b}{2(M-1)N_0}}\right ) \right )^2

Matlab Code:

Check this book for full Matlab code.
Simulation of Digital Communication Systems Using Matlab – by Mathuranathan Viswanathan

Simulation Results:

 

 

See Also

[1] Constructing a rectangular constellation for 16-QAM
[2] BER Vs Eb/N0 for 8-PSK modulation over AWGN
[3] BER Vs Eb/N0 for QPSK modulation over AWGN
[4]QPSK modulation and Demodulation
[5] Simulation of BER Vs Eb/N0 for BPSK modulation over AWGN in Matlab
[6] Intuitive derivation of Performance of an optimum BPSK receiver in AWGN channel
[7] Simulation of M-PSK modulation techniques in AWGN channel

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Mathuranathan

– Founder and Author @ gaussianwaves.com which has garnered worldwide readership. He is a masters in communication engineering and has 7 years of technical expertise in channel modeling and has worked in various technologies ranging from read channel design for hard drives, GSM/EDGE/GPRS, OFDM, MIMO, 3GPP PHY layer and DSL.
He also specializes in tutoring on various subjects like signal processing, random process, digital communication etc..,He can be contacted at support@gaussianwaves.com

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