21-06-2013, 02:37 PM
Performance analysis of channel estimation and adaptive equalization in slow fading channel
Performance analysis.ppt (Size: 199 KB / Downloads: 100)
System Model and Main Techniques
Flow chart diagram
Build up our model
Produce data and set parameters
Produce different channels
Channel estimation for flat fading
Equalization for frequency selective fading
Build up our model
Two Scenarios: Urban and Suburb
Suppose in both environments, there are no dominant stationary signal component, such as light-of-sight path , i.e. Rayleigh fading
To simulate GSM:
Carrier frequency: fc = 1.8GHz
Bandwidth of each channel: 200KHz
Symbol period: Ts = 5us for Nyquist pulse
Produce data and set parameters
Support random data and image data
Modulation: Phase shift keying (PSK)
In our simulation, use QPSK
May use Gray coding or not
8% pilot data is inserted preceding source data in each coherence time
Channel estimation for flat fading
Estimate the channel phase for PSK modulation
Use first 8% data to training the detector
Tc/Ts = 1080 @ 20km/hr 86 pilot data
Tc/Ts = 180 @ 120km/hr 14 pilot data
Use the mean of phase shift in the pilot to adjust the received signal phase
Simulation and Experimental Result
For AWGN channel
For slow flat fading channel
For slow frequency selective fading channel
Comparison among three channels
For AWGN channel
1) BER of simulation vs theoretical
The BER performance of simulation result is closely identical to theoretical BER.
2) Image quality of received vs original
the received image is plot at SNR = 5dB, we see there are some random noises in the image. From simulation result, the received image quality is almost the same as original at SNR = 10dB.
3) BER of Image vs random data
The correlation between image pixel does not effect the BER in AWGN channel.
For slow flat fading channel
1) BER of simulation vs theoretical
the BER performance of simulation result is worse than theoretical BER since we do not know exactly the channel phase information
BER performance is improved dramatically in low SNR, while not in high SNR. Since in low SNR, white Gaussian noise dominate the BER error, which can be improved by enhancing SNR; while in high SNR, phase estimation error dominate the BER error, which can not be improved by simply enhancing SNR.
2) BER & constellation of training vs non-training
the constellation is plot at SNR = 25dB, we see both the BER performance and constellation are greatly improved by channel phase estimation.