Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 System Design Issues in building a Cognitive Radio Network: IEEE Detection of DTV signals at very low SNR using PN sequences Joint work with Steve Shellhammer (Qualcomm) & Rahul Tandra (U.C. Berkeley) “What I did in my summer internship”

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 Apparent scarcity of spectrum Spectrum use model flawed – allocated but not utilized J. Mitola coins ‘cognitive radio’ -Ph.D. thesis (May 2000) FFC issues NPRM for TV bands (May 2004) is born (Sept 2004) How to protect the ‘incumbent’ ? May 2006: v0.1 of the Draft is shipped Sept. 2006: FCC Docket: Retail by Feb Sensing specs. still not met, broadcasters dissatisfied Unlicensed economic model still attractive –3G auction: $17 Bn, $34 Bn, $46 Bn A brief history of Cognitive Radio and IEEE

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October deployment scenario : Fixed wireless broadband access network operating in TV bands (Channel 2 -51) Cellular system, central BS, CPEs (Consumer Premise Equipment) Targeted at but not limited to rural deployment Strict requirements on protections of existing licensed services

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Incumbent protection numerology ParameterTV broadcasting Part 74 devices Channel detection time< 2 sec Incumbent detection threshold -116 dBm-107dBm Required prob(detection)90% Max. False alarm allowed10%

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Why do we need to detect at such low SNR?

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Why do we need to detect at such low SNR? Shadow faded CPE

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Why do we need to detect at such low SNR? Shadow faded CPE INTERFERENCE

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October ‘Hidden incumbent’ problem An in-band Incumbent appears at a location where BS cannot sense it but CPE can. CPE wishes to inform BS about incumbent but may loose sync to BS due to interference from incumbent BS continues to transmit on the channel occupied by incumbent, causing interference to it.

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October ‘Hidden cell’ problem

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Sharing licensed spectrum Vertical sharing Horizontal sharing

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Horizontal spectrum sharing An unlicensed user must adhere to FCC guidelines for protection of incumbents. But other unlicensed systems on the TV bands do not enjoy the same protection. This means a Cognitive Radio must not only detect, but also classify signals. Simple narrowband power detection [Tandra & Sahai] does not achieve this objective

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 Part II Detection of DTV signals at very low SNR using PN sequences

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Outline of Part II The ATSC standard and PN sequences Optimal & approximate detectors Problem with detector a proposed detector Our detection algorithms Effects of multipath fading on PN correlation Fundamental limits in PN seq. detection Sensitivity to pilot estimate A comparison of PN and pilot detection Pilot spectral line detection

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October ATSC DTV numerology Symbol rate: Msymbols/sec Data Segment SYNC: A ‘1001’ pattern at the beginning of each segment Data Field SYNC: An entire segment containing PN sequences: PN511 + PN63 + PN63 + PN63

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October ATSC framing structure A single VSB Data Segment The Data Field SYNC

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Typical ATSC DTV spectrum Pilot tone 6 Mhz = 1 DTV channel f_ IF = 5.38 Mhz VSB spectrum at IF Nyquist Rolloff =

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Simulation Methodology DTV signal captures (50 DTV signals captured at high SNR, provided to us by MSTV Corp.) + X Scale Gaussian Noise Pull out reqd. # of samples Detection algorithm Threshold calibration Signal at desired SNR Multipath fading, noise Test locations in NY, Washington

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The optimal PN seq. detector Assumption: exactly 1 PN sequence present Generalized Likelihood Ratio Test (GLRT) in favor of H 1 iff: is the MLE given by: GLRT is:

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Approximate detector Neglect the signal in alternate hypothesis (low SNR) is the MLE given by GLRT is: Threshold can be found easily by using: simply correlate and compare max value with 

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October A detector proposed by France Telecom Correlate the received signal, r(n), with the known PN sequence Use a simple low pass filter to estimate the mean and the variance of the correlator output An ATSC DTV is declared detected when (  and c are constants set by the BS)

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The Problem with this Detector The estimate of the mean approach zero even for noise only input This implies that the test statistic can approach zero even for a noise only input signal This results in false alarms. The detector threshold ‘c’ cannot be calibrated for a given false alarm probability p F A.

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Test statistic with noise-only input Output of the correlator when only filtered receiver noise is fed as input. Ratio shows false peaks because mean goes very close to zero. Correlator outputDetector output

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Detector operation with DTV signal + Noise (SNR = 0 dB) Output of the correlator over duration of 1 ATSC field when DTV signal + noise is fed as input. The peak indicated the position of the PN sequence. Ratio again shows false peaks because mean goes very close to zero Correlator outputDetector output

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October A simple ‘fix’ Take absolute value of the correlator output before feeding as input to the detector. Mean cannot go arbitrarily close to zero The ratio is random for noise input.

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Modified Detector with noise input Absolute value of the correlator output when only filtered receiver noise is fed as input. Ratio is random Correlator output Detector output

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Modified detector with DTV Signal + Noise (SNR = 0 dB) Absolute value of correlator output over duration of 1 ATSC field when DTV signal + noise is fed as input. The peak indicated the position of the PN sequence. Ratio shows peak at location of PN sequence Correlator outputDetector output

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October A simple test statistic Correlate received signal with the following sequence: [PN511 + PN zeros + PN63] Find the tallest peak in the correlator output Compare its magnitude with threshold

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Performance of a simple correlator

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of multipath Phase reversal 48.4 ms

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of multipath Multiple peaks 6 s6 s ~ 2km dominant echo

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of multipath Multiple peaks 6 s6 s Can we utilize multipath in a positive way ? (Lessons from CDMA ?) ~ 2km dominant echo

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Fundamental limits in PN detection Using a larger received signal for detection does not always improve performance. multipath fadingnoise enhancement Multipath, jitter and small variations in clock frequency cause timing offsets resulting in misaligned peaks by +/-1,2 samples

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Using the Data Segment SYNC The DATA Segment SNYC is a 1001 pattern Very non unique but much more frequent (every 77.3  s=1 seg.) Present in noise and data quite often, but can we use its periodicity? A long sequence of the following form can be used for correlation: [1001 (828 zeros) 1001 (828 zeros) … N times ]

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Performance of ‘1001 correlator’

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Aligning peak polarities helps! (sometimes)

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The issue of Multiple antennas The height and polarity of a PN correlation peak depends on the instantaneous fade. Two or more independent fades would provide different correlation peaks. If multiple antennas can provide statistically independent fading, they can help. A simple system would be to run 2 parallel PN detectors and OR their decisions. i.e. Miss detection = (Miss detection 1) AND (Miss detection 2) PN sequence detection Power detector is affected by shadowing Therefore multiple sensors located in INDEPENDENT shadow fades would help Existence of such independent CPEs not guaranteed (incumbents not happy with the idea) Multiple antenna on a single CPE do not help (same shadow fade) Power detection

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Multiple antennas Running 2 parallel detectors Assumption: Fading processes at two antennas are statistically independent We use fields from widely time-separated part of a DTV signal capture to satisfy independent fade assumption At 600 Mhz, /2 = 25 cm. A practical antenna array can be built.

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of pilot estimation error The pilot frequency is used in downconverting a passband signal to recover the baseband transmit signal. How sensitive is PN detection to the estimate of the pilot frequency? We attempt to measure the effect of pilot estimation error on a PN correlation peak

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of pilot estimation error

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of pilot estimation error At low enough SNR even 0.2% error in pilot frequency estimate can be disastrous for PN correlation Pilot estimation needs to be accurate Why not use the pilot line as a means for DTV signal detection?

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 Part III Spectral line detection of a DTV pilot [Extension of ‘Narrowband pilot energy detection’ done by Rahul Tandra]

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Power spectrum of a DTV signal at -22 dB

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Performance of pilot spectral line detection Signal shows a strong pilot Detector performance is very good even at -25 dB ! strong pilot Pilot energy detection [Tandra]

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Performance of pilot spectral line detection Signal shows a moderately strong pilot Detector begins to fail at -21dB Moderate pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Performance of pilot spectral line detection Pilot is almost completely absent from signal Detector begins to fail at -16 dB Very weak pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Good, moderate and bad pilots

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Doubling the listening time Very weak Pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Doubling the listening time Moderate Pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Some gains from listening longer

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Noise Uncertainty For signals with weak pilots, uncertainty in the noise power estimate can drastically change performance. We assume  = +/- 1 dB. How does performance change?

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Noise Uncertainty Signal shows a strong pilot Detector performance worse by approx 2.5 dB. Detector begins to fail at -24 dB strong pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Noise Uncertainty Signal shows a moderately strong pilot Worse by ~2 dB Detector begins to fail at -17 dB Moderate pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Noise Uncertainty Pilot is almost completely absent from signal Worse by ~2 dB Detector begins to fail at -12 dB Very weak pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Noise Uncertainty Pilot is almost completely absent from signal Worse by ~2 dB Detector begins to fail at -12 dB If we listen for double the time detector begins to fail at -14 dB Very weak pilot

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October A comparison of PN detection and pilot energy detection PN SequencePilot Energy Detection Occurs once in 24.2 msAlways present in time Frequency content over entire DTV spectrum Present at a specific frequency (14 known pilot freq. location) Power in PN sequence is 1/313 rd of the total energy in signal (25 dB below) Power in pilot is 11 dB below avg signal power.  Need to sense for longer to average out noise Need to sense for shorter duration. Cannot improve performance by sensing for infinitely long duration. Noise uncertainty results in an SNR wall effect below which signal cannot be sensed. Requires knowledge of pilot frequencyDoes not require knowledge of PN seq. Achieves signal classification.Does not achieve signal classification

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Proposed dual mode DTV detector Pilot detection detects pilot and triggers PN detection for signal classification (confirms DTV) Multiple antennas (help both pilot and PN) Detection quiet periods are kept small.

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Conclusions and future directions PN Correlation detector performance limited by multipath fading, noise and jitter. Difficult to accurately estimate multipath at low SNR. Multiple antennas on a single detector can help (statistically independent fading processes) Pilot spectral line detection is promising but does not classify signal as DTV. A ‘dual mode’ detector utilizing –Pilot spectral line detection for detection and –PN correlation (with multiple antennas) for classification. can achieve quick and reliable DTV incumbent protection

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The internship experience Worked in the standards division of Corporate R&D Some exposure to systems, IEEE standards procedures (tedious). Focus on practical feasibility. Economic & political side of technology. Overall – a satisfying, busy 3 months (no time to work on WINLAB research simultaneously )

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 Thank you

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October 2006 Backup slides

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Incumbent protection numerology Required prob(detection) = 90 % False alarm allowed = 10%

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Hidden wireless microphones An example of hidden incumbents Microphones are low power devices and can appear and disappear on a finer time scale. Wireless microphones are likely candidates for hidden incumbents. Propagation curves show that it Is virtually impossible for a BS to detect a microphone at the edge of a WRAN cell. Distance from wireless mic (km) Received power (dBm)

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of pilot estimation error

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October The effect of pilot estimation error

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Do multiple antennas help? The height and polarity of a PN correlation peak depends on the instantaneous fade. Two or more independent fade would provide different correlation peaks. If multiple antennas can provide statistically independent fading, they can help. A simple (suboptimal) system would be to run 2 parallel PN detectors and OR their decisions. Therefore: Miss detection = (Miss detection 1) AND (Miss detection 2)

Suhas Mathur, WINLAB, Rutgers University Summer Qualcomm 10 th October Multipath Fading Source: Gorka Guerra, Pablo Angueira, Manuel M. Vélez, David Guerra, Gorka Prieto, Juan Luis Ordiales, and Amaia Arrinda ‘Field Measurement Based Characterization of the Wideband Urban Multipath Channel for Portable DTV Reception in Single Frequency Networks’ IEEE TRANSACTIONS ON BROADCASTING, VOL. 51, NO. 2, JUNE