1. Narrowband Interference Detection in MB-OFDM UWB Shih-Chang Chen Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan

2. Motivation  UWB coexists with narrowband radios  Required to detect presence of narrowband radios Implement avoidance technique

3. NBI Detections  Strong Interference When close to the transmitter Regard polluted sub-carriers as outliers Employ outlier detection algorithms  Weak Interference Independent detection for each sub-carrier Joint detection for all sub-carriers

4. NBI Detection Algorithms  Outlier Detection Strong interference Low complexity  Random Signal with Unknown Parameters Weak interference Independent detection for each sub-carrier  Model Change Detection Weak interference Joint detection for all sub-carriers

5. Random Signal Detection  Tonal Interference Wi-MAX, sinusoid wave, etc.  Autoregressive Interference General Gaussian random process signal.

6. Random Signal Detection

7.  PDF under H1:  It can be shown that to find the MLE of Po we must minimize:

8. Random Signal Detection  By Neyman-Pearson approach, the detector can be shown that:

9. Tonal Interference  Signal model:

10. Tonal Interference  The covariance of tonal interference is:

11. Autoregressive Interference  Signal model with normalized power:

12. Autoregressive Interference  Can be shown to be:

13. Autoregressive Interference  And we know:  Or,in compact form:

14. Autoregressive Interference  Multiplying both sides of compact form by their transposes and taking expectations, we obtain:

15. Performance analysis

17. Conclusion  Random Signal Detection Tonal Interference. Autoregressive Interference Independent detection for each sub-carrier  Model Change Detection Coming soon.