Experimental Investigation of Energy, Bandwidth and Modulation on Spectrum
Sensingby
Jems Pradhan
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Major Professor :Dr.Yanxiao Zhao Committee Members :Dr. Dimitrios E Anagnostou, Dr. Cassendra Degen
Master of Science in Electrical Engineering South Dakota School of Mines and Technology Rapid City, July 6, 2015
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OUTLINE
• BACKGROUND
• Energy-and-Bandwidth Spectrum Sensing (EBSS)
• Two-Stage Spectrum Sensing• Conclusion • Future Work
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Static and Dynamic Spectrum PolicySpectrum Sensing Methods
Experimental VerificationSensing performance comparison with energy detection
Theoretical Analysis Experimental Analysis
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Dynamic Spectrum Access
Dynamic Exclusive Use Model Open Sharing Mode Hierarchical Access
Model
Spectrum Property Rights
Dynamic Spectrum Allocation Spectrum Underlay Spectrum Overlay
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• Static Spectrum Access Policy results underutilization of available spectrum.
• Dynamic Spectrum Access(DSA) is a promising solution.
Frequency
Power
Time
BUSY IDLE BUSY IDLE BUSY
Secondary User(SU) can use this IDLE channel via Dynamic Spectrum Access
• Spectrum Sensing to detect the IDLE and BUSY channel.• Detect the incoming PU or SU occupied by SU in the idle channel.
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Spectrum Sensing Techniques:
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• Energy Detection Method: widely used
• Other Sensing Methods:
• Matched Filter Detection
• Waveform Based Sensing
• Radio Identification Based Sensing
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Energy Detection Method:
ri= signal received by user Secondary User i ni = Additive White Gaussian Noise s= signal that Primary User transmits
λE= predefined Energy Threshold
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Frequency (Channel)
Ener
gy L
evel
-------------------------------------------
Signal
NoiseEnergy Threshold
PROBLEM STATEMENT
Drawback of Energy Detection Method: What if the noise has an energy level greater than λE ?
Noise but considered as signal according to energy detection
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Energy-and-Bandwidth Spectrum Sensing (EBSS):
λB = pre-defined bandwidth thresholdB = bandwidth of the received signal
• Energy of the received signal should be higher than the energy threshold .
• Bandwidth of the received signal should be higher than the bandwidth threshold.
(r > λE) AND (r > λB) =
False, if H0
True, if H1
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Principle of Energy Detection and EBSS
Frequency (Channel)
Ener
gy L
evel
Figure: Energy Detection Method
-------------------------------------------
Energy Threshold
False Alarm
Signal
Noise
Frequency (Channel)
Ener
gy L
evel
Figure: Energy-and Bandwidth Detection Method
-------------------------------------------
Energy Threshold
Signal
Noise
Noise
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SENSING PERFORMANCE
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• Sensing Performance Metrics– Probability of false alarm: Channel sensed busy when no signal is present– Probability of miss detection: Channel sensed idle when signal is present
P1f is the Probability of False alarm for Energy Detection Method
P1m is the Probability of Miss Detection for Energy Detection Method
P1f = Pr(r > λE / H0)
P1m = Pr(r < λE / H1)
P2f is the Probability of False Alarm for EBSS
P2m is the Probability of Miss Detection for EBSS
P2f = Pr[(r > λE) AND (b> λB) / H0]
P2m = Pr{NOT[(r > λE) AND (b> λB)] / H1}
= Pr{[(r < λE) OR (b < λB)] / H1}
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GNU Radio and USRP
• GNU Radio Software is a free and open source software for Software Defined Radio.
• USRP N200.
• Motherboard (XILINX Spartan 3A-DSP 1800).• Daughter board XCVR2450.• Operating frequency range of XCVR2450 2.4-2.5GHz and 4.95-5.85GHz
which is an ISM band.• Antenna used is the VERT2450 vertical antenna with dual band (2.4 -
2.5 and 4.9 - 5.9 GHz).
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Received Signal LNA IF
DAUGHTER BOARD
ADC DDC
MOTHERBOARD
HOST COMPUTER GNU SOFTWARE
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EXPERIMENTAL RESULTS
Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP trying to receive 2.4 GHz Signal when no signal was sent from the transmitter .
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP Device receiving 2.4 GHz signal when 2.4 GHz was sent from the transmitter.
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP trying to receive 2.42 GHz Signal when no signal was sent from the transmitter .
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP Device receiving 2.42 GHz signal when 2.42 GHz was sent from the transmitter.
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP Device receiving 2.45 GHz signal when no signal was sent from the transmitter.
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Ener
gy le
vel (
dB)
Frequency (GHz)
Figure: USRP Device receiving 2.45 GHz signal when 2.45 GHz was sent from the transmitter.
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP Device receiving 5.1 GHz signal when no signal was sent from the transmitter.
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Frequency (GHz)
Ener
gy le
vel (
dB)
Figure: USRP Device receiving 5.1 GHz signal when 5.1 GHz signal was sent from the transmitter.
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Performance Evaluation using Probabilities of False Alarm and Miss Detection of theoretical and practical energy detection
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Performance Evaluation using Probabilities of False Alarm and Miss Detection
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• Theoretical analysis of Energy-and-Bandwidth Spectrum Sensing.
• Experimental Analysis of Energy-and-Bandwidth Spectrum Sensing.
• Analysis of Performance Metrics using Probability of False Alarm and Probability of Miss Detection.
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Question : Is the signal detected is originated from a PU or an SU ?
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Two-Stage Spectrum Sensing Approach
• Utilizes Energy and Bandwidth of EBSS and also utilizes the Modulation to
distinguish channel from three states : H0(idle), H1(occupied by PU) and
H2(occupied by SU).
False, if H0
(r > λE) AND (b> λB) =
True, if H1 or H2
• H1 or H2 is detected based on the modulation scheme.
• PU and SU operate using different modulation scheme QPSK and BPSK respectively .
• SU with two demodulators to detect both QPSK and BPSK.
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Mathematical model BPSK and QPSK
SBPSK (t) = m(t) cos(2πfct + θ)
Figure: Block Diagram of BPSK Modulator
SQPSK (t) = AcmI(t) cos(2πfct) + AcmQ(t) sin(2πfct)
Figure: Block Diagram of QPSK Modulator
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Square Law
Device
Band Pass Filter
Frequency Divider
Recovered Signal
Received Signal
Integrate and Dump
Circuit
Bit Sychronizer
m(t) cos(2πfct+θ)cos2(2πfct+θ) cos(4πfct+2θ) cos(2πfct+θ)
m(t) cos(2πfct+θ)
m(t) cos2(2πfct+θ)
Figure: Block Diagram of BPSK Carrier Recovery Circuit.
S(t) = Ac2[mI
2(t) cos2(2πfct)] – 2 Ac mI(t) cos(2πfct)* Ac mQ(t) sin (2πfct)* + Ac
2[mQ2(t) sin2(2πfct)]
S(t) = Ac mI(t) cos(2πfct)
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EXPERIMENTAL SETUP II
Figure: Experimental Setup to analyze the effects of different modulation scheme.
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Figure: PU and SU using different demodulation techniques varying the data rate.
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• Analysis Based on packet by packet by considering frequency, data rate and modulation. SU uses BPSK and PU uses QPSK in all the experiments conducted hereafter.
Figure: A SU receiver (USRP N200) receiving BPSK signals at 2.47 GHz
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Figure: A PU receiver (USRP N200) receiving QPSK signals at 5.1 GHz
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Figure: SU receiving signals at 2.47 GHz modulated using BPSK when PUsending signals at 2.47001 GHz using QPSK
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Figure: An SU receiver receiving BPSK signals at 2.45 GHz when there is another SU transmitter at 2.45 GHz using BPSK.
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Figure: USRP N200 receiving signals at 2.47 GHz modulated using BPSKwhen there is another transmitter at 2.47 GHz using QPSK
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Figure: USRP N200 receiving signals at 2.49 GHz modulated using BPSKwhen there is a transmitter at 2.49 GHz using BPSK.
Figure: USRP N200 failing to receive signals at 2.49 GHz using QPSK whenthere is a transmitter at 2.49 GHz using BPSK
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PU Tx QPSK
Modulated
SU Tx BPSK
Modulated
SU Rx Equipped with
QPSK + BPSKDemodulators
Detected PU
Detected SU
Block Diagram of Proposed SU Receiver
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Figure: USRP N200 successfully receiving signals at 2.45GHz using QPSK when there is a PU transmitter receiver pair at 2.45 GHz using QPSK
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CONCLUSION
• A new approach of spectrum sensing EBSS is proposed and experimentally verified.
• Spectrum performance has been drastically improved using EBSS compared to traditional energy detection method.
• A new method is proposed to identify the PU and SU signal using Energy, Bandwidth and Modulation Sensing Approach.
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FUTURE WORK
• Sensing performance of EBSS will be analyzed theoretically.
• SU equipped with two demodulators and aware of the modulation implemented by PU and SU.
• Experiments are only conducted on ISM bands more experiments could be conducted on different frequency range.
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PUBLICATIONS• Zhao, Y., Pradhan, J., Huang, J., & Sun, H. (2014, October). Experimental
approach: energy-and-bandwidth spectrum sensing using GNU radio and USRP. In Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems (pp. 174-179). ACM.
• Zhao, Y., Pradhan, J., Huang, J., Luo, Y., & Pu, L. (2015). Joint energy-and-bandwidth spectrum sensing with GNU radio and USRP. ACM SIGAPP Applied Computing Review, 14(4), 40-49
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