![matlab 64 qam matlab 64 qam](https://it.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/31158/versions/1/screenshot.png)
The order of the QAM modulation has to be set at the transmitter, because the transmission is only one way, and in addition to this, there are thousands of receivers, making it impossible to have a dynamically adaptive form of modulation. In this way, changing the modulation order, and the error correction, the data speed can be optimised whilst maintaining the required error rate.įor domestic broadcast applications for example, 64 QAM and 256 QAM are often used in digital cable television and cable modem applications. The level of error correction used is also altered. However the downside is that a better signal to noise ratio is required to achieve this.įor some systems the order of the modulation format is fixed, but in others where there is a two way link, it is possible to adapt the order of the modulation to obtain the best throughput for the given link conditions. the data throughput achievable under ideal conditions increases. As the order of the QAM signal is increased, i.e. There is a balance between data throughput and signal to noise ratio required. However some specific variants of QAM are used in some specific applications and standards. QAM is in many radio communications and data delivery applications. Bit sequence mapping for a 16QAM signal QAM formats and applications Whilst further error correction can be introduced to mitigate any deterioration in link quality, this will also decrease the data throughput. To utilise the high order QAM formats, the link must have a very good E b/N o otherwise data errors will be present.When the E b/N o deteriorates, then other the power level must be increased, or the QAM order reduced if the bit error rate is to be preserved.Īccordingly there is a balance to be made between the data rate and QAM modulation order, power and the acceptable bit error rate. If there is a good margin, higher orders of QAM can be used to gain a faster data rate, but if the link deteriorates, lower orders are used to preserve the noise margin and ensure that a low bit error rate is preserved.Īs the QAM order increases, so the distance between the different points on the constellation diagram decreases and there is a higher possibility of data errors being introduced. Many data transmission systems migrate between the different orders of QAM, 16QAM, 32QAM, etc., dependent upon the link conditions.
![matlab 64 qam matlab 64 qam](https://www.researchgate.net/profile/Emil-Dumic/publication/258644309/figure/fig2/AS:297441174802449@1447926910675/QPSK-16-QAM-and-64-QAM-mapping-non-hierarchical.png)
As 16QAM transitions to 64QAM, 64QAM to 256 QAM and so forth, higher data rates can be achieved, but at the cost of the noise margin. QAM, quadrature amplitude modulation provides some significant benefits for data transmission. Modulation formats: Modulation types & techniques Amplitude modulation Frequency modulation Phase modulation Quadrature amplitude modulation, QAM basics QAM theory QAM formats QAM modulators & demodulators Quadrature Amplitude Modulation, QAM Tutorial Includes: T(1,i) = 1-(1-2*sqrt(M-1)/sqrt(M)*qfunc(sqrt(3*k(1,i)/(M-1)))).QAM Formats: 8-QAM, 16-QAM, 32-QAM, 64-QAM, 128-QAM, 256-QAM Quadrature amplitude modulation can be used with a variety of different formats: 8QAM, 16QAM, 64QAM, 128QAM, 256QAM, but there are performance differences and trade-offs T = zeros(1,length(k)) %Theoritical result for BER is calculated R = zeros(1,length(k)) %BER is calculated Can you help please? clear allĪ = randi(, 1, 1000000) %random binary arrayī(i) = 8*a(4*i-3) + 4*a(4*i-2) + 2*a(4*i-1) + a(4*i) Ĭ = qammod(b,16,'UnitAveragePower',true) %16-QAM modulationĭ(i,1:250000) = awgn(c,k(i),'measured') %noise is added for different SNR valuesĮ = qamdemod(d,16) %demodulation of 16-QAMįor j=1:length(k)%results of numerical values are converted to 4-bit binary
![matlab 64 qam matlab 64 qam](https://www.mathworks.com/help/examples/comm/win64/PlotRectQAMReferenceConstellationExample_04.png)
This program worked well for 4-QAM but it got not-so-well results for 16-QAM. When I compare the BER for theoritical and experimental result, I see that my results are much worse than theoritical values. Then it is demodulated and each result is converted to binary. Then it enters 16-QAM modulation, AWGN noise is added for SNRs 0 to 20dB. First, this program creates an array of 1000000 randomly generated bits, it groups them as 4-bit values and converts into numerical values. I want to write a program which simulates the modulation and demodulation of 16-QAM.