Digital Communication Simulation Using Matlab Computer Science Essay Example

normal or Gaussian distribution. This type of noise introduces a single impairment in the system [8]. In digital telecommunications, pulse shaping can be employed to modify the waveform of transmitted pulses. This adjustment ensures that the signal bandwidth matches the bandwidth of the communication channel, thereby reducing distortion and intersymbol interference. The goal is to optimize the compatibility between the transmitted signal and the communication channel by limiting the effective transmission bandwidth. Pulse shaping is often applied after modulation.

The process of rectangular pulse shaping involves repeating each output from the modulator a fixed number of times to create an up-sampled signal. While this method can be used as a first or exploratory step in algorithm development, it is considered less realistic compared to other types of pulse shaping. If the modulated signal is up-sampled by the transmitter, the received signal should be down-sampled before demodulation. One way to accomplish this is through the "integrate and dump" operation.

Demodulation often involves filtering or an "integrate and dump" operation. This assignment requires you to design and implement the modulation process for a random binary data stream using 64-level QAM (quadrature amplitude modulation). The transmitted signal will be sent over an AWGN (Additive White Gaussian Noise) wireless communication channel and will be demodulated using a 64-QAM demodulator. Your system should include a baseband modulator, AWGN channel, and demodulator.

The table below shows the relevant functions from the Matlab Communications Toolbox that can be utilized in this assignment. You can use the functions provided by the toolbox for the 64-QAM modulator/demodulator, or you have the option to implement them yourself. In the code mentioned above, there is a mapping of

bits to symbols. While a single bit cannot have values ranging from 0 to 63, a group of bits can. The two signals transmitted are identical because both are modulated using the same modulation scheme (64-QAM) and sent through the same channel (Additive White Gaussian Noise wireless channel). The received signals have distinct scatter plots.

The difference in the scatter plots is due to the varying signal-to-noise ratios (SNR) of the channels through which the signals were transmitted. The initial signal had an SNR of 40dB, while the other signal had an SNR of 14dB. The second scatter plot clearly shows significant noise in the channel, whereas there is no indication of noise in the first scatter plot. If an ADSL line has an SNR lower than 15dB, it can lead to frequent disconnections and various issues. Based on these scatter plots, it was expected that there would be no bit errors or errors in the first signal with its high SNR because it was transmitted over a channel without any noise.

Contrarily, the second signal has an SNR of 14dB, resulting in a non-zero bit error rate and expected errors. These confirmations verify both expectations. The signal-to-noise ratio (SNR) is the ratio of signal strength to noise level. A high SNR indicates that the signal power exceeds the noise power significantly, minimizing errors.

In this task, you are required to investigate the effects of rectangular pulse shaping and the integrate-and-dump operation in a communication system. The SNR reduction may result in incorrect demodulation and error occurrence. Modulation is typically followed by pulse shaping, while demodulation is often preceded by filtering or an integrate-and-dump operation. Rectangular pulse shaping

involves repeating each output from the modulator a fixed number of times to create an upsampled signal. If the modulated signal is upsampled at the transmitter, the received signal should be downsampled before demodulation at the receiver. The integration-and-dump operation serves as one method for downsampling the received signal. The table below showcases additional relevant functions from the Matlab Communications Toolbox that can be utilized for this assignment.

Compare and explain the results obtained in Task 1 with those obtained in Task 2, clearly stating the reasons for the differences. The differences between Task 1 and Task 2 arise from the use of rectangular pulse shaping and an integrate and dump operation. In Task 2, the signal is upsampled after modulation using a rectangular pulse shape, which applies a square pulse to the signal and repeats each symbol multiple times (in this case, 6 times).

The dump operation downsamples the signal by integrating it over a single period. This process involves modulation, pulse shaping, demodulation, and an integrate and dump operation. As rectangular pulse shaping repeats each output from the modulator a fixed number of times, our signals in this task are expected to be better than those in task1.

The fulfillment of this expectation was realized as BER for SNR=40 dB is 0 and for SNR=14 dB is 0.0038. In the case of SNR=14 dB, it is evident that both BER and the number of errors decreased significantly. The presence of filtering on the transmitter results in intersymbol interference. According to one source, "The combination of filtering at the transmitter and the channel leads to received pulse sequences suffering from intersymbol interference, creating a blurry signal that

is not quite ready for sampling and detection." When the channel's bandwidth greatly exceeds the pulse's bandwidth, there will be minimal spreading of the pulse. Conversely, if the channel's bandwidth is similar to the signal's bandwidth, the spreading will surpass the symbol duration and will cause overlapping of signal pulses. This overlapping phenomenon is known as intersymbol interference. To minimize distortion and the impact of intersymbol interference, rectangular pulse shaping was implemented.

Filtering the transmitted signal is advantageous for improving its fit to the communication channel, as it limits the effective bandwidth. This leads to a reduction in Bit Error Rate (BER) and errors, as symbols are sent multiple times. Filtering enhances the quality of the signal by preventing rapid transitions between states and narrowing the frequency spectra. Without filtering, signals would have unnecessarily wide frequency spectra for transmitting information.

Conclusions

When the Signal-to-Noise Ratio (SNR) value is sufficiently high, minimal noise will be present in the received signal. A high SNR value indicates a low Bit Error Rate that approaches zero.

When there is noise and interference, it is important to boost the signal power in order to lower the chance of errors. The system's bit error rate (BER) indicates the link's quality. Good bandwidth efficiency requires filtering. "Higher level M-array schemes (like 64-QAM) are highly efficient in terms of bandwidth, but they are more vulnerable to noise and need linear amplification."