# BER-calculation-in-AMI-coding
This project took place for the course of *Intergrated Communication Systems* in 2018.
# Description
Using **MATLAB** we simulated a digital communication system using the **Alternate Mark Inversion (AMI)** encoding technique. Our goal was to test the **bit-error-rate(BER)** performance on the receiver and compare it with the ΒΕR-SNR theoretical curve.
## Alternate Mark Inversion (AMI)
AMI is a synchronous clock encoding technique that uses bipolar pulses to represent logical 1. The next logic 1 is represented by a pulse of the opposite polarity. Hence a sequence of logical 1s are represented by a sequence of pulses of alternating polarity.
![tttt](https://user-images.githubusercontent.com/59124127/106363476-74db2b80-6331-11eb-8034-719324213b2a.png)
###### *Example of AMI coding and the corresponding clock signal*
## Bit Error Rate (BER)
Bit error rate (BER) is defined as the percentage of bits that have errors relative to the total number of bits received in a transmission. BER is usually expressed as 10 to a negative power. The BER is calculated by comparing the transmitted sequence of bits to the received bits and counting the number of errors.
## Signal-to-Noise Ratio
Signal-to-noise ratio (SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to the noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise.
## Additive White Gaussian Noise (AWGN)
Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics:
**Additive** because it is added to any noise that might be intrinsic to the information system.
**White** refers to the idea that it has uniform power across the frequency band for the information system. It is an analogy to the color white which has uniform emissions at all frequencies in the visible spectrum.
**Gaussian** because it has a normal distribution in the time domain with an average time domain value of zero.
## Root-raised-cosine filter (RRC)
A root-raised-cosine filter (RRC), sometimes known as square-root-raised-cosine filter (SRRC), is frequently used as the transmit and receive filter in a digital communication system to perform matched filtering. This helps in minimizing **intersymbol interference (ISI)**. The combined response of two such filters is that of the raised-cosine filter.
To have minimum ISI (Intersymbol interference), the overall response of transmit filter, channel response and receive filter has to satisfy Nyquist ISI criterion. Raised-cosine filter is the most popular filter response satisfying this criterion.
# System Overview
![wwww](https://user-images.githubusercontent.com/59124127/106365315-6abf2a00-633d-11eb-8395-4614fe921822.jpg)
# Results
1. SNR = 6
![111](https://user-images.githubusercontent.com/59124127/106365101-e91acc80-633b-11eb-808f-0f86d1be532f.png)
2. SNR = 9
![222](https://user-images.githubusercontent.com/59124127/106365131-20897900-633c-11eb-9a1d-19a6ee432a64.png)
3.SNR = 12
![333](https://user-images.githubusercontent.com/59124127/106365143-41ea6500-633c-11eb-83e4-44d317e5603a.png)
## BER-SNR diagram
![Final](https://user-images.githubusercontent.com/59124127/106365349-a823b780-633d-11eb-9250-927ba7d3bb18.png)
# Conclusion
The BER-SNR diagram satisfyingly approaches the theoretical BER-SNR diagram .
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matlab中奈奎斯特图代码-BER-calculation-in-AMI-coding:AMI中的BER计算
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matlab中奈奎斯特图代码AMI中的BER计算 该项目在2018年的集成通信系统课程中进行。 描述 使用MATLAB,我们使用替代标记反转(AMI)编码技术模拟了数字通信系统。 我们的目标是测试接收机的误码率(BER)性能,并将其与withER-SNR理论曲线进行比较。 交替标记反转(AMI) AMI是一种同步时钟编码技术,使用双极性脉冲表示逻辑1。下一个逻辑1由相反极性的脉冲表示。 因此,逻辑1的序列由极性交替的脉冲序列表示。 AMI编码和相应时钟信号的示例 误码率(BER) 误码率(BER)定义为具有错误的比特相对于传输中接收的比特总数的百分比。 BER通常表示为10到负功率。 通过将发送的位序列与接收到的位进行比较并计算错误数来计算BER。 信噪比 信噪比(SNR或S / N)是科学和工程学中使用的一种度量,用于将所需信号的电平与背景噪声的电平进行比较。 SNR定义为信号功率与噪声功率之比,通常以分贝表示。 比率大于1:1(大于0 dB)表示信号多于噪声。 加性高斯白噪声(AWGN) 加性高斯白噪声(AWGN)是信息理论中用来模仿自然界中许多随机过程的影响的基本噪声模型。 修饰
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