Chapter 4 Simulation Results
4.2 Performance – Bit Error Rate
In our discussion, correlations between transmit antennas and receive antennas are assumed independent, and each transmit and receive antenna pair has the same channel model. In the BER simulations, indoor channel model [28] is adopted, because both EWC 802.11n and 802.11a assume similar indoor wireless applications, and the simulated channel is generated by a hand-written program using Jake’s model. Besides, the correlation between any of the two taps of the models is small. As shown before, a simulated packet consists of the preamble part and 6 data symbols. In our simulation, perfect channel state information (CSI) is assumed.
The first simulated channel, as listed in Table 4.3, is measured in a typical old office environment where partitions are often made of bricks. The longest tap has a delay of 127ns, which is about 2.5 samples for 802.11a system. The delay is so small that a very large coherent bandwidth is expected.
Table 4.3 Indoor channel model [28] with short delays, office environment Tap
No.
Delay (ns)
Power (dB)
Amplitude Distribution
Doppler Spectrum
1 0 0 Rayleigh Classical/Flat
2 36 -5 Rayleigh Classical/Flat
3 84 -13 Rayleigh Classical/Flat
4 127 -19 Rayleigh Classical/Flat
Figure 4.4 shows performances of various techniques, where 2x2 means that there are 2 transmit and 2 receive antennas. ML denotes ML detection, BLAST denotes V-BLAST detection, Shen’s denote Shen’s method, Li’s denotes Li’s method [22] in section 3.2, improved Li’s denotes our improved Li’s method in section 3.2, ML1 denotes L=1 in section 3.1.1, ML2 denotes L=2 in section 3.1.1, ML3 denote L=3 in section 3.1.1, ML4 denote L=4 in section, ML_ext denote the new extended method (detect two values) at a time in section 3.1.2. In Figure 4.5, 2x3 means that there are 2 transmit and 3 receive antennas, similarly for Figure 4.6. ML1 to ML4 are compared separately in Figure 4.13.
It is obvious that the proposed method (L=1) Nt =Nr, has better performance than V-BLAST, with little increase in complexity. And in the figures there is no performance difference between V-BLAST algorithms and comparison algorithms. The condition is also observed in Figure 4.5 and Figure 4.6. Figure 4.7, 4.8, and 4.9 are for the case of 3 transmitter antennas 3, 4, and 5 receiver antennas, respectively.
In each figure the performance of the V-BLAST, Shen’s method, Li’s method, and our improved Li’s method are the same. The reasons are that algorithm of the Li’s method in section 2.3.3.2.1 [21] channel response H are the same in Step 1 and Step 2.
The difference is just the part of data detection is recomputed, but H are the same. And in [21] the approach is fit to use in ideal detection and cancellation. When the error propagation exists the performance of every detected layer are very close. Hence Li’s method has no effect in enhancing performance here. The performances of Shen and Li’s and our improved Li’s method are the same. And its reason and Li’s method are the same.
Figure 4.4 BER performance versus SNR of various detection techniques (2x2), office environment
Figure 4.5 BER performance versus SNR of various detection techniques (2x3), office environment
Figure 4.6 BER performance versus SNR of various detection techniques (2x4), office environment
Figure 4.7 BER performance versus SNR of various detection techniques (3x3), office environment
Figure 4.8 BER performance versus SNR of various detection techniques (3x4), office environment
Figure 4.9 BER performance versus SNR of various detection techniques (3x5), office environment
Figure 4.10 BER performance versus SNR of various detection techniques (4x4), office environment
Figure 4.11 BER performance versus SNR of various detection techniques (4x5), office environment
Figure 4.12 BER performance versus SNR of various detection techniques (4x6), office environment
Figure 4.13 BER performance versus SNR of our various detection techniques and V-BLAST (4x4), office environment
Figure 4.14 BER performance versus SNR of our various detection techniques and V-BLAST (4x5), office environment
Figure 4.15 BER performance versus SNR of the proposed detection techniques and V-BLAST (4x6), office environment
The second simulated channel is measured in an airport representing a typical large hall area. The channel has a few very long delay paths which indicate bad channel conditions and is harmful to communication. After simulation, the results in large hall are similar to results in office environment.
Table 4.4 Indoor channel model [28], large hall environment Tap
No.
Delay (ns)
Power (dB)
Amplitude Distribution
Doppler Spectrum
1 0 0 Rayleigh Classical
2 174 -8 Rayleigh Classical
3 274 -15 Rayleigh Classical
4 560 -18 Rayleigh Classical
Figure 4.16 BER performance versus SNR of various detection techniques (2x2), large hall environment
Figure 4.17 BER performance versus SNR of various detection techniques (2x3), large hall environment
Figure 4.18 BER performance versus SNR of various detection techniques (2x4), large hall environment
Figure 4.19 BER performance versus SNR of various detection techniques (3x3), large hall environment
Figure 4.20 BER performance versus SNR of various detection techniques (3x4), large hall environment
Figure 4.21 BER performance versus SNR of various detection techniques (3x5), large hall environment
Figure 4.22 BER performance versus SNR of various detection techniques (4x4), large hall environment
Figure 4.23 BER performance versus SNR of various detection techniques (4x5), large hall environment
Figure 4.24 BER performance versus SNR of various detection techniques (4x6), large hall environment
Figure 4.25 BER performance versus SNR of the proposed detection techniques and V-BLAST (4x4), large hall environment
Figure 4.26 BER performance versus SNR of the proposed detection techniques and V-BLAST (4x5), large hall environment
Figure 4.27 BER performance versus SNR of the proposed detection techniques and V-BLAST (4x6), large hall environment
Chapter 5 Conclusion
In this thesis, many algorithms based on V-BLAST are introduced and simulated, and new algorithms for MIMO OFDM detection are proposed, followed by their investigations and verifications in terms of complexity and BER performance by testing EWC 802.11n systems. Although ML algorithm results in the best performance, it demands the highest computation cost. Therefore, we combine ML and V-BLAST methods, and reduce the complexity of ML detection by utilizing known detected values.
Since designs of detection methods are trade-off problems between cost and performance, complexity and performance analysis helps a lot to decide a suitable design.
In complexity analysis, the proposed methods (L=1) is roughly equal to V-BLAST method but its performance is better than V-BLAST method. Usually a detected layer will produce error and propagate to the following layers. The proposed methods have the better performance and less error propagation problem than other algorithm compare in the simulation. Testing 802.16e is considered as future work. It is also future work to reduce the complexity of pseudoinverse.
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