Prof. Brian L. Evans
Dept. of Electrical and Computer Engineering
Wireless Networking and Communications Group
The University of Texas at Austin, Austin, Texas
Lead graduate students: Mr. Aditya Chopra, Mr. Kapil Gulati and Mr. Marcel Nassar
Current collaboration with Dr. Nageen Himayat, Mr. Kirk Skeba, and Ds. Srikathyayani Srikanteswara at Intel
Past collaboration with Ms. Chaitanya Sreerama, Dr. Eddie X. Lin, Dr. Alberto A. Ochoa, and Mr. Keith R. Tinsley at Intel
Thursday, December 2, 2010
First, we derive statistical models of additive RFI in Wi-Fi, Wimax, and other common wireless network topologies, and show a 10-100x decrease in bit error rate when using these models for a single carrier system. Then, we describe more recent advances:
Answer #1. The myriad filter for pre-processing has a single parameter whose optimal value is computed as follows:
Here, alpha is the exponent in the Symmmetric Alpha Stable distribution where 0 < alpha < 2, and y is the dispersion parameter (analogous to variance).
Here are the values of derivation of k(alpha) with respect to alpha for selected values of alpha:
alpha k'(alpha) ----- --------- 0.5 0.7698 sqrt(y) + 0.57735 sqrt(y) ln(y) 1.0 y + y ln(y) 1.5 2.31 y^(3/2) + 1.73205 y^(3/2) ln(y)For y = 1, k'(alpha) varies from 0.7698 to 2.31 for 0.5 < alpha < 1.5. In fitting RFI data, we have found that alpha > 0.5. Pertubations in k are particularly severe as alpha -> 0 and as alpha -> 2.
Question #2. How does your approach compare to successive interference cancellation?
Answer #2. Interference cancellation, when possible, will provide the most benefit as it reduces the interference itself in the first place. Our approach on mitigating RFI based on statistical models, aims to improve the communication performance by mitigating the residual interference still present at the receiver. The rational behind our approach is:
The latter is the reason we consider statistical modeling of RFI in different network scenarios (out-of-cell interference in cellular networks and in ad hoc networks with guard zones). Thus our methods can be used in conjunction with other methods to mitigate the residual interference.
Question #3: Lower Kullback-Leibler (KL) divergence does not imply correspondence in tail probabilities. How do we compare the accuracy of the statistical models derived with respect to modeling the tail probabilities?
Answer #3. Accurately modeling tail probabilities is important as the bit error rate performance (or outage probability in networks) depends on the tail probabilities of the interference. We compare the tail probabilities by:
decay rate at a threshold value = - log (tail probability at that threshold) / threshold
Draft of our journal paper for more information. Decay rate is defined in equation (66). Figures 3-8 compare the decay rate of interference.