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Demonstration of Real-time Spectrum Sensing for Cognitive Radio Zhe Chen, Nan Guo, Member, IEEE, and Robert C. Qiu, Senior Member, IEEE

Abstract—The requirement for real-time processing indeed poses challenges on implementing spectrum sensing algorithms. Trade-off between the complexity and the effectiveness of spectrum sensing algorithms should be taken into consideration. In this paper, a fast Fourier transform based spectrum sensing algorithm, whose decision variable is independent of noise level, is introduced. A small form factor software defined radio development platform is employed to implement a spectrum sensing receiver with the proposed algorithm. To our best knowledge, it is the first time that real-time spectrum sensing on hardware platform with controllable primary user devices is demonstrated. Index Terms—Cognitive radio, spectrum sensing, real-time, demonstration

I. I NTRODUCTION OGNITIVE radio (CR) has been put forward to make an efficient use of the scarce radio frequency spectrum. Spectrum sensing is the cornerstone of CR, which detects the availability of the spectrum for secondary users (SUs). The effectiveness of spectrum sensing largely determines the overall spectrum utilization. A good spectrum sensing algorithm should offer high probability of detection (PD ) at low probability of false alarm (PF A ) for a wide range of signalto-noise ratio (SNR). However, from practical perspective, the algorithm has to be implementation-friendly, including acceptable computational complexity. Less computation means less power consumption, which is especially meaningful for battery-powered devices. There have been some techniques for spectrum sensing [1], [2]. In addition to tremendous efforts on theoretical investigation, work on hardware implementation has been reported as well [3]–[6]. However, to our best knowledge, real-time spectrum sensing on hardware platforms with controllable primary user (PU) devices has not been demonstrated. In this paper, a more sophisticated real-time platform called small form factor (SFF) software defined radio (SDR) development platform (DP) [7] is employed, and real-time spectrum sensing with the presence of controllable PU devices is demonstrated. It is the first time that real-time spectrum sensing on hardware platform with controllable PU devices is demonstrated. The major contributions and features of this paper are listed as follows. Firstly, the platform employed in our research is real-time oriented. Secondly, the spectrum sensing algorithm proposed in this paper is implementation-friendly and its decision variable is independent of noise level. Thirdly, both PD and PF A of the spectrum sensing algorithm are measured

C

on the hardware platform. Finally, a real-time demonstration of spectrum sensing with controllable PU devices is reported. II. T HE FFT-AVERAGING -R ATIO (FAR) A LGORITHM FOR S PECTRUM S ENSING Aiming at hardware friendliness and effective detection, a blind spectrum sensing algorithm, called fast Fourier transform (FFT)-averaging-ratio (FAR), is proposed as follows. The input to the FAR algorithm is a baseband discrete-time signal sampled at frequency fs , and the output is a series of vectors of two-class decisions that represent the availabilities of the channel. The input signal is in real numbers since the phase information is not required here. Firstly, in each time slot, a block of base-band signal samples are segmented into T frames. Denote t-th frame of the input samples by xt (n), n = 0, 1, . . . , N − 1, t = 0, 1, . . . , T − 1, where N is the number of samples in a frame, and T is the number of frames. Then the segmented frames are multiplied by a window function: xw,t (n) = xt (n)w(n) n = 0, 1, . . . , N − 1, t = 0, 1, . . . , T − 1

(1)

After that, FFT is applied to the windowed frame. Note that xw,t (n) are real numbers and the frequency spectrum of xw,t (n): Xt (k) =

N −1 X

xw,t (n)e−j2πkn/N

n=0

k = 0, 1, . . . , N − 1, t = 0, 1, . . . , T − 1

(2)

is symmetric, thus for each frame only N2 + 1 tones of Xt (k), k = 0, 1, . . . , N2 , t = 0, 1, . . . , T − 1, are required (assume N is even). These tones are separated by fs /N Hz. The power spectral density (PSD) calculation follows the FFT operation. Define Pt (k), the PSD of xw,t (n), as: 2

k = 0, 1, . . . , N2 , t = 0, 1, . . . , T − 1 (3) The PSDs of T consecutive frames are used for averaging, yielding: Pt (k) = |Xt (k)| ,

Pavg (k) =

1 T

TP −1

Pt (k),

t=0

k = 0, 1, . . . , N2

(4)

where the factor T1 is not actually required. Let Pm be the mean of Pavg (k) calculated across all frequency tones: N

The authors are with the Department of Electrical and Computer Engineering, Center for Manufacturing Research, Tennessee Technological University, Cookeville, TN 38505, USA. E-mail: {zchen42, nguo, rqiu}@tntech.edu

Pm

2 2 X Pavg (k) = N +2

k=0

(5)

2

1 0.9 0.8 −121 dBm/kHz −124 dBm/kHz −126 dBm/kHz

0.7

PD

0.6

Fig. 1.

The SFF SDR DP and the equipment.

0.5 0.4 0.3

where, again, the factor N2+2 can be dropped. In order to be robust to the noise level, the decision variable r(k) is formed as a ratio: r(k) =

Pavg (k) Pm ,

k = 0, 1, . . . , N2

occupied > < available

α

(7)

where α is a preset threshold. For every N · T samples, N2 + 1 tones over an fs /2 Hz frequency band are scanned. It can be proved that the ratio r(k) is independent of noise level. Alternatively, the decision rule can be written as: Pavg (k) − α · Pm

occupied > < available

0

0.1 0

0

0.2

0.4

(8)

Decision results of channel states from multi-tones can be combined to make a joint decision. For instance, if K is a frequency set of interest, a joint decision for frequency tones k ∈ K can be formed as: ( T {r(k) ≥ α} occupied, if k∈K T (9) JointDecision = available, if k∈K {r(k) < α} T where denotes AND operation.

III. I MPLEMENTATION OF S PECTRUM S ENSING ON SFF SDR DP A receiver with the FAR spectrum sensing algorithm is implemented on the SFF SDR DP. Three functional modules, i.e., radio frequency (RF) module, data conversion module, and digital processing module, constitute the SFF SDR DP [7]. The nominal noise figure of the RF module is 5 dB. There are two Xilinx Virtex-4 field-programmable gate arrays (FPGAs) and one Texas Instruments (TI) TMS320DM6446 digital signal processor (DSP) on the SFF SDR DP. The FAR algorithm is implemented on the DSP. In this implementation, the parameters of the FAR algorithm are set to N = 128 and T = 16. For a better resolution in frequency domain, the rectangular window is employed as the window function. IV. E XPERIMENTS AND R EAL -T IME D EMONSTRATION Fig. 1 shows the SFF SDR DP and the equipment. In the experiment, family radio service (FRS) handsets serve as PUs, while the SFF SDR DP acts as an SU. A 2-tone joint detection (9) is employed in performance evaluation.

0.6

0.8

1

P

(6)

Finally, thresholding is applied to r(k) for k = 0, 1, . . . , N2 , and the decisions on channel states are made according to the following rule: r(k)

0.2

FA

Fig. 2.

ROC curves.

A. Performance Evaluation In order to precisely evaluate the performance of the FAR algorithm using real-time signals generated by the PU, a setup with coaxial cable connection is adopted. The FRS signal is measured and recorded using a digital phosphor oscilloscope (DPO), Tektronix DPO72004, and the recorded signals are then transmitted by an arbitrary waveform generator (AWG), Tektronix AWG7122B, to both the SFF SDR DP and a spectrum analyzer (SA), Rohde&Schwarz FSEM20, through attenuators and a power divider. The transmitted FRS signal strength can be adjusted. The maximum PSD of the received signal measured in 1-kHz resolution bandwidth on the SA is used as an indicator in this experiment. Consider an FRS transmitter emitting 1 Watt power 10 miles away from the receiver, then the maximum PSD of the received FRS signal is in the order of -80 dBm/kHz, assuming free space propagation. Using this setup, the receiver operating characteristic (ROC) of the FAR algorithm implemented on the SFF SDR DP is measured, as shown in Fig. 2. It can be observed that the implemented FAR algorithm works well when the received maximum PSD is -121 dBm/kHz or above, and it almost stops functioning as the maximum PSD goes below -126 dBm/kHz. Performance of PD and PF A versus received maximum PSD is given in Fig. 3. One can see that with PF A close to zero, the FAR algorithm on the SFF SDR DP can achieve a high PD when the maximum PSD is -121 dBm/kHz or above, noting that typical PSD of the received FRS signal is higher than our detection limit. B. Real-Time Demonstration In the demonstration, two FRS handsets (PUs) call each other, and one SFF SDR DP running the FAR algorithm senses the spectrum in real-time. The experiment is conducted in an indoor environment. As illustrated in Fig. 4, the separation between the two FRS handsets is 14 feet, and the SFF SDR DP is 7 feet and 14 feet away from the two FRS handsets, respectively. To monitor the received signal, the DPO is used together with the SFF SDR DP, and they both interface the

3

Amplitude of Rx Signal(V)

1 0.9 0.8

0.6 PFA

0.5

0.3 0.2 0.1

Fig. 3.

−0.05

0

5

10

15

20 Time (s)

25

30

35

40

0

5

10

15

20 Time (s)

25

30

35

40

PD

0.4

0

0

Sensed Channel State

Probability

0.7

0.05

−128

−126

−124

−122 −120 −118 Power (dBm/kHz)

−116

−114

Calling each other PU2 (FRS)

14 ft

14 ft PU1 (FRS)

0.6 0.4 0.2 0

−112

Probability of detection and probability of false alarm v.s. power.

1 0.8

Fig. 5.

The received signal and the sensed channel states.

well. Moreover, a real-time demonstration of spectrum sensing has been conducted, and encouraging experimental results have been obtained, suggesting that the FFT-averaging-ratio algorithm is indeed effective. Implementing spectrum sensing on a hardware platform is the first step to establish cognitive radio networks. In the future, cognitive radio networks with 40 nodes will be built and tested.

7 ft

ACKNOWLEDGMENT

SU1 (SFF SDR DP)

This work is funded by National Science Foundation through grants (ECCS-0901420), (ECCS-0821658), and (ECCS-0622125). R EFERENCES

Fig. 4.

A layoff for real-time demonstration.

channel with the same antenna through a power divider. Attenuators may be put at the receiver input to emulate path loss caused by increased distance. The DPO records the channel data while the SFF SDR DP performs spectrum sensing at the same time. Fig. 5 shows the channel waveform recorded by the DPO and the sensed channel states output from the SFF SDR DP, where “1” and “0” indicate the channel is occupied and available, respectively. From the observation, the sensed channel states perfectly match the recorded channel waveforms. V. C ONCLUSION It is the first time that real-time spectrum sensing on hardware platform with controllable primary users has been demonstrated. The FFT-averaging-ratio algorithm for spectrum sensing, designed to compromise between the performance and the implementation complexity, has been proposed. A spectrum sensing receiver with the FFT-averaging-ratio algorithm has been implemented on the small form factor software defined radio development platform and tested as

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