Improving bit error rate of STBC-OFDM using convolutional and turbo codes over Nakagami-m fading channel for BPSK modulation

Author(s):  
Heba R. Ahamed ◽  
Hassan M. Elkamchouchi
2013 ◽  
Vol 321-324 ◽  
pp. 2877-2882
Author(s):  
Jin Peng Wang ◽  
Qi Ming Zhu ◽  
Fan Cao ◽  
Ping Li ◽  
Nian Yu Zou

In this paper, a Turbo codes and Rake received mixed algorithm in multipath fading environment is proposed to solve the problems in signal reception in a fading environment of the mobile communication system. Compared to the traditional multi-path reception methods, the advantage of the algorithm can significantly improve system performance; reduce the system error rate by approximately 2.5dB. Moreover, the performance of the system will be improved a lot if the antenna power is equal. Firstly a mixed algorithm in AWGN channel and multipath fading channel is introduced and derived theoretically and then a computer simulation is carried out and the impact of various parameters on network performance is anglicized. The results show that the algorithm can effectively reduce the effects of multipath fading environment on the transmission power attenuation, lower bit error rate, and improve the effectiveness to resistance to multipath fading significantly.


2003 ◽  
Vol 57 (6) ◽  
pp. 395-402 ◽  
Author(s):  
Ahmed Bouzidi Djebbar ◽  
Ali Djebbari ◽  
Merahi Bouziani ◽  
Jean Michel Rouvaen

In this paper reduction of errors in turbo decoding is done using neural network. Turbo codes was one of the first thriving attempt for obtaining error correcting performance in the vicinity of the theoretical Shannon bound of –1.6 db. Parallel concatenated encoding and iterative decoding are the two techniques available for constructing turbo codes. Decrease in Eb/No necessary to get a desired bit-error rate (BER) is achieved for every iteration in turbo decoding. But the improvement in Eb/No decreases for each iteration. From the turbo encoder, the output is taken and this is added with noise, when transmitting through the channel. The noisy data is fed as an input to the neural network. The neural network is trained for getting the desired target. The desired target is the encoded data. The turbo decoder decodes the output of neural network. The neural network help to reduce the number of errors. Bit error rate of turbo decoder trained using neural network is less than the bit error rate of turbo decoder without training.


Author(s):  
Akhil Gupta ◽  
Shiwani Dogra ◽  
Ishfaq Bashir Sofi

Background & Objective: In this paper, Multiple Input Multiple Output (MIMO) has been examined in wireless medium by utilizing Spatial Multiplexing procedure for the computation of the Bit Error Rate (BER). MIMO enhance the throughput in wireless medium. Spatial multiplexing builds the limit and link reliability of the MIMO frameworks. Methods: The BER execution of DPSK, Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) in MIMO frameworks in Rayleigh multipath channel is analyzed. Zero forcing algorithms is utilized as a detection technique. A comparison of these modulations is additionally done in Rayleigh fading channel. Conclusion: The execution of transmission modes are assessed by figuring the likelihood of Bit Error Rate (BER) vs. the Signal Noise Ratio (SNR) under the every now and utilized four wireless channel models (Rayleigh, Dent, Jake’s and Okumura).


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