Cognitive Radio Research at the University of Twente

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1 Cognitive Radio Research at the University of Twente M.S. Oude Alink, D.H. Mahrof, A. Ghaffari, S. Subhan E.A.M. Klumperink, M.C.M. Soer, A.B.J. Kokkeler, B. Nauta Integrated Circuit Design / Computer Architecture for Embedded Systems CTIT Research Institute, University of Twente, Enschede, The Netherlands November 24, 2010 Mark Oude Alink

2 Cognitive Twente 2/65 Integrated Circuit Design (CMOS) IC design techniques to realize portable, fast and energy efficient communication systems. Computer Architecture for Embedded Systems Perform research on energy-efficient dependable architectures for networked embedded systems, by combining computer architectures, systems software, networking, and tools. Signals and Systems Design and implement digital radio-frontend functionality; focus on providing proof-of-principle algorithm testing in real-world test-beds

3 Why CMOS? 3/65 Optimized for digital circuits: Moore s Law Cheap if produced in large numbers Analog and digital combined: small & low-power Modern Equipment DSP for communications (equalization, demodulation,...) MP3, video,... Also challenges No Moore s Law for analog Lower supply voltage...

4 Contents 4/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

5 5/65 Static Frequency Allocation

6 6/65 Our data shows there is a looming crisis. We may not run out of spectrum tomorrow or next month, but it s coming and we need to do something now. Chairman of FCC, 2009

7 Spectrum Scarcity 7/65 [Swisscom]

8 Dynamic Spectrum Access 8/65 [Swisscom]

9 Cognitive Radio 9/65 Definition (IEEE Standard ) (a) A type of radio in which communication systems are aware of their environment and internal state and can make decisions about their radio operating behavior based on that information and predefined objectives. (b) Cognitive radio [as defined in item a)] that uses software-defined radio, adaptive radio, and other technologies to adjust automatically its behavior or operations to achieve desired objectives.

10 10/65 Cognitive Radio as Secondary User

11 Applications 11/65 Increased data rates Broadband wireless access (IEEE ) MHz (TV-bands) Range: 17-30km Other

12 Contents 12/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

13 Hardware is Challenging 13/65 Flexible radio parameters Frequency Bandwidth Modulation Emission masks Software Radio Can we do it? ADC DSP Yes we can! If we have a software radio, but... Typical power range in TV-band: -100dBm to 0dBm P RF =0 dbm 1mWatt 600mV pk-pk in 50Ω Typical supply voltage: V DD =1.2V

14 ADC Bandwidth versus SNDR 14/65 BW [Hz] 1,E+11 1,E+10 1,E+09 1,E+08 1,E+07 1,E+06 1,E+05 1,E+04 1,E SNDR [db] ISSCC VLSI ISSCC 2009 VLSI 2009 Cognitive Radio Jitter=1psrms (6GHz and 100dB) Jitter=100fsrms [Murmann, 2009]

15 Downconversion / filtering required 15/65 RF BB LO Resolution (bit) mW 1 µw 1 kw 1 W downconversion filter 1kHz 1MHz 1GHz Signal Bandwidth (Hz)

16 Why Filters? 16/65 Filter ω BB non-linearity! RF ω 2ω 3ω 4ω 5ω Filter Base Band (BB) BB RF LO Digital Synthesizer ω 3ω 5ω ω 3ω 5ω But: good RF filters are costly and inflexible

17 Traditional Radio 17/65 Old FM-Radio: tune with variable-plate capacitor More integrated solutions Fixed frequency band(s) One SAW-filter per band Each SAW-filter same size and cost as IC CMOS-implementation Limited Q=f c /bandwidth < 15 f c tune range~20%

18 For a Cognitive Radio 18/65 Many fixed filters required

19 Solution Directions 19/65 Make system more linear so no/less filtering needed Multipath Polyphase Technique [Shrestha, JSSC2006] Harmonic Rejection Mixing [Ru, JSSC2009], [Moseley, ISSCC2009], [Ru, JSSC2010] Select band with lowest linearity requirement [Mahrof, DySPAN2010] Spectrum sensing with high linearity and sensitivity [Oude Alink, DySPAN2010] Flexible integrated RF filter [Ghaffari, RFIC2010] Beamforming / Nulling / Adaptive cancellation /...

20 Contents 20/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

21 Mahrofet al, On the Effect of Spectral Location of Interferers on Linearity Requirements for Wideband Cognitive Radio Receivers, DySPAN (2010) Choose Smart Operating Frequency 21/65

22 In-Band Interference 22/65 Large number of interferers Treat intermodulation products as noise (Marshall, DySPAN 2008) Low number of strong interferers White spots are classified into IM3-spots and IM3-free spots Spectral sensing: find IM3-free spots Relaxed linearity requirement

23 Crossmodulation 23/65 CR operates in IM3-Free Spot no problem? Distortion from crossmodulation! CR is always limited by XM3

24 Wideband Signals (e.g. OFDM) 24/65 Intermodulation smears out 66% of power in center channel Even less available IM3-free spots

25 Linearity Requirements 25/65 P IIP3 XM3 = 4 dbm P IIP3 = 40 dbm P IIP3 = 39 dbm P IIP3 = 36 dbm Not possible in P IIP3 = 33 dbm current receivers Assume P TV =-10dBm Assume for demodulation, required Signal-to-Distortion-Ratio = 10 db

26 Contents 26/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

27 Oude Alink et al, A 50MHz-to-1.5GHz Cross-Correlation CMOS Spectrum Analyzer for Cognitive Radio with 89dB SFDR in 1MHz RBW, DySPAN (2010) High-Linearity Spectrum Sensing 27/65 Detect IM3-free spots higher linearity required Traditional trade-off G>1 NF: IIP3: G>1 NF: IIP3: G<1 NF: IIP3: But then we fail to detect weak signals SFDR: difference between maximum and minimum signal power that can be detected at the same time

28 SFDR 28/65 Input of Spectrum Analyzer (SA) (= output of ideal SA) NF=0dB, IIP3=+ dbm, RBW=100kHz, att.=...db Difference = 80dB

29 SFDR 29/65 Output of SA: non-linearity is dominant NF=20dB, IIP3=+10dBm, RBW=100kHz, att.=0db

30 SFDR 30/65 Output of SA: noise is dominant NF=20dB, IIP3=+10dBm, RBW=100kHz, att.=48db Each db of attenuation: -Noise floor up by 1dB -Distortion peaks down by 2dB

31 SFDR 31/65 Output of SA: noise / non-linearity equally dominant NF=20dB, IIP3=+10dBm, RBW=100kHz, att.=28db 2 SFDR = log10 = 3 ( IIP3 NF 10 RBW + 174) 76dB

32 Energy Detection 32/65 SA: measure power/energy in freq-band Cognitive Radio: decide whether band is free Decide between H 0 : noise H 1 : signal + noise No knowledge of signal required works for any band Simple, low computational complexity Minimum SNR required

33 Crosscorrelation 33/65 Crosscorrelation Two receive paths Uncorrelated sources Energy detection with less noise SNR improved!

34 Prototype Implementation 34/65 Matching / Attenuation I/Q-Mixers IF amplifiers ADC + DSP

35 Mixer-First Architecture 35/65 Gain at IF instead of RF More linear (feedback) Increases NF use XC Passive hard-switching mixers [Soer, ISSCC2009] Extremely linear Unmatched use resistors in front Also use for attenuation! Network 1 Network 2

36 36/65 Measurement Setup

37 Measurements 37/65 Normalized Measurement Time=2 0 test tone -100dBm RBW=10kHz DC-offset

38 Measurements 38/65 Normalized Measurement Time=2 1 test tone -100dBm RBW=10kHz DC-offset

39 Measurements 39/65 Normalized Measurement Time=2 2 test tone -100dBm RBW=10kHz DC-offset

40 Measurements 40/65 Normalized Measurement Time=2 3 test tone -100dBm RBW=10kHz DC-offset

41 Measurements 41/65 Normalized Measurement Time=2 4 test tone -100dBm RBW=10kHz DC-offset

42 Measurements 42/65 Normalized Measurement Time=2 5 test tone -100dBm RBW=10kHz DC-offset

43 Measurements 43/65 Normalized Measurement Time=2 6 test tone -100dBm RBW=10kHz DC-offset

44 Measurements 44/65 Normalized Measurement Time=2 7 test tone -100dBm RBW=10kHz DC-offset

45 Measurements 45/65 Normalized Measurement Time=2 8 test tone -100dBm RBW=10kHz DC-offset

46 Measurements 46/65 Normalized Measurement Time=2 9 test tone -100dBm RBW=10kHz DC-offset

47 Measurements 47/65 Normalized Measurement Time=2 10 test tone -100dBm RBW=10kHz DC-offset

48 Measurements 48/65 Normalized Measurement Time=2 11 test tone -100dBm RBW=10kHz DC-offset

49 Measurements 49/65 Normalized Measurement Time=2 12 test tone -100dBm RBW=10kHz DC-offset

50 Measurements 50/65 Normalized Measurement Time=2 13 test tone -100dBm RBW=10kHz DC-offset

51 Measurements 51/65 Normalized Measurement Time=2 14 test tone -100dBm RBW=10kHz DC-offset

52 Measurements 52/65 test tone -100dBm RBW=10kHz DC-offset more accurate amplitude estimation NF=17.1dB NF=3.1dB

53 Measurements 53/65 NF reduced: 12dB SFDR improved: 8dB IIP3 improved: 6dB NF reduced: -6+18=12dB SFDR improved: 12dB

54 Contents 54/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

55 On-Chip RF Filter Options 55/65 On-chip filters Low-Q inductors (lossy, bulky, not flexible) Active inductors (noisy, non-linear) Q-Enhanced techniques (compromise) Better Idea: Do not use inductors Switches & capacitors are very linear Switches & capacitors scale with CMOS Ghaffariet al, A Differential 4-Path Highly Linear Widely Tunable On-Chip Band-Pass Filter, RFIC (2010)

56 Idea from 1960: N-Path Filters 56/65 Downconvert & LPF & Upconvert = BPF With square wave clock, harmonics are a problem Use multiple paths p(t) q(t) V in p(t-t/n) q(t-t/n) + V out p(t-(n-1)t/n) q(t-(n-1)t/n) [Franks, ISSCC1960]

57 Simple Implementation 57/65 S11 R S12 Vin S21 R C1 S22 Vout C2 Sn1 R Sn2 Cn Vout S11 Vin R S21 C1 C2 Sn1 Cn

58 Filter with Switches + Capacitors 58/65 C1 C1 average f S RC >> T on f S V in C1 C1 1.5f S 0 V Open switching frequency Short other frequencies

59 Filter Properties 59/65 Only switches + C s High-Q BPF around f c f c flexible via digital clock RC determines Q Larger RC Open circuit at f c Highly linear Low noise

60 Implementation & Measurements 60/65 Property Ghaffari [RFIC2010] Georgescu [2006] Qualkadi [2007] f [MHz] Technology / Area [mm 2 ] 65nm / nm / mm / 1.9 Frequency [GHz] Power [mw] Q NF [db] < IP3 [dbm] ? 1-dB CP [dbm]

61 Contents 61/65 Cognitive Radio Hardware Challenges for Cognitive Radio Linearity Requirements High-Linearity Spectrum Sensing Flexible RF Filtering Conclusions

62 Conclusions 62/65 Statically allocated spectrum is underutilized Cognitive Radio uses available spectrum as SU Direct AD-conversion technologically impossible Filtering & downconversion required Traditional costly, bulky, fixed RF filters not an option Integrated flexible RF filter shown NF still too high to compete with fixed RF filters Work in progress

63 Conclusions 63/65 Desired flexibility requires high linearity Required linearity depends on frequency-band Spectrum sensing is available in CR! Linearity always required due to crossmodulation Spectrum analyzer must be linear & sensitive Attenuation makes spectrum analyzer more linear Crosscorrelation solves problem of increased NF

64 64/65 Questions?

65 65/65 White spaces are the blank pages on which we will write our broadband future Jonathan S. Adelstein, FCC Commissioner, 2009

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