Empirical Investigation of the Error Correcting Performance of a Binary Convolutional Code – Engineering Assignment Help

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Assignment Task :
Introduction 
In this coursework you will implement (in MATLAB) the encoders and decoders for linear  block and convolutional codes. The implementation will then be used to generate simulation  results assuming BPSK modulation with on Additive White Gaussian Noise (AWGN) channel. 
Although the MATLAB communications toolbox contains several functions for error control  coding, you should not use them for these exercises (to be awarded maximum marks). The  toolbox functions are intended to provide an efficient and simple to use implementation,  without requiring detailed understanding of the algorithms. Instead, we will deliberately take  a direct implementation approach which, will enhance understanding of the concepts and  algorithms covered in lectures. You are required to write .m files for all the steps. 
The coursework consists of 2 main tasks. You are required to submit an individual technical  report containing discussions of your results and implementation choices and all MATLAB codes. You should also include any relevant introduction and description of your algorithms. 
 
Task 1: Empirical investigation of the error correcting performance of a binary  convolutional code 
In this task you need to write a script which performs Monte Carlo simulations to obtain BER  curves in noisy channels: AWGN channel, with BPSK modulation.  
For this task you will use a convolutional code and the probability of error will be estimated  empirically i.e. in the form of BER via Monte Carlo simulations.  
The rate ½ code for this task is given in Figure 1. Assume that the input data sequence k=200  bits (adding 3 terminating bits). Therefore, there code-length is 406. As the decoder you  should implement the Viterbi decoder.  
Encoder
 
Signal
Note that this assumes that symbols are transmitted as ±1(BPSK) (suitably scaled to adjust  for the code rate)  
Task 1.1  
Plot in the same figure: the curve for uncoded BPSK transmission, uncoded BPSK  transmission with Eb/N0 adjusted for the code rate, and the BER plot for your code – Figure 2 shows how your results should be represented. Discuss our results. 
  
1. What is the coding gain at BER=0.01 , 0.001 and 0.0001?  
Error
 
Task 1.2 
Investigate, the impact of “burst noise” and the impact of the random “interleaver” countermeasure. To simulate the “noise burst” assume that 10 consecutive transmitted  symbols will see much higher noise (10dB higher noise energy). For example, the transmitted  symbols (101-110) will be subject to the higher noise level (it will have 10dB lower SNR) compared to all other transmitted symbols (1-100 and 111 – 406). 
 
noise
 
 
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