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

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

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

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

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

Random Signal Detection

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

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

Tonal Interference  Signal model:

Tonal Interference  The covariance of tonal interference is:

Autoregressive Interference  Signal model with normalized power:

Autoregressive Interference  Can be shown to be:

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

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

Performance analysis

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