Spectrum sensing is one among the major functions of Cognitive radio systems. Meanwhile, wideband spectrum sensing is a challenging issue with these systems. Conventional wideband techniques require analog-to-digital converters operating at Nyquist sampling rates. Sub-Nyquist sampling or compressed sensing techniques, however, require low rate analog-to-digital converters. In order to avoid interference to primary user transmissions, the cognitive users should perform continuous spectrum sensing and identify the active primary bands even at low-signal-to-noise ratios. In this paper, we propose a sub-Nyquist wideband spectrum sensing technique using multicoset sampling and revised orthogonal matching pursuit (OMP). Conventional orthogonal matching pursuit algorithm uses n iterations to recover n-sparse signal. Extending the iterations beyond n further improves the performance, for instance, extended OMP (OMP α) and sparsity unaware OMP (OMP ∞) achieves a better detection capability by increasing the number of iterations beyond n. We analyse the performance of multicoset sampling based wideband spectrum sensing with various OMP algorithms.

1.
FCC, ET Docket No 03-222 Notice of proposed rule making and order (December 2003).
2.
I.
Mitola
, J. and
J.
Maguire
, G. Q., “
Cognitive radio: making software radios more personal
,”
IEEE Personal Commun. Mag.
,
6
(
4
),
13
18
, (
1999
).
3.
S.
Kumar
,
M.
Kaur
,
N. K.
Singh
,
K.
Singh
and
P. S.
Chauhan
, “
Energy detection based spectrum sensing for gamma shadowed α–η–μ and α–κ–μ fading channels
,”
AEÜ Int. Journal of Electronics and Communications
,
93
,
26
31
(
2018
).
4.
L.
Yang
,
Z.
Chen
and
F.
Yin
, “
Cyclo-energy detector for spectrum sensing in cognitive radio
,”
AEÜ Int. Journal of Electronics and Communications
,
66
(
1
),
89
92
(
2012
).
5.
S.
Kapoor
,
S. V. R. K.
Rao
, and
G.
Singh
, “
Opportunistic spectrum sensing by employing matched filter in cognitive radio network
,” in
Proc. Int. Conf. Commun. Syst. Netw. Technol.
(
2011
), pp.
580
583
.
6.
Z.
Quan
,
S.
Cui
,
A. H.
Sayed
, and
H. V.
Poor
, “
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
,”
IEEE Trans. Signal Processing
,
57
(
3
),
1128
1140
(
2009
).
7.
B.
Farhang-Boroujeny
, “
Filter bank spectrum sensing for cognitive radios
,”
IEEE Trans. Signal Processing
,
56
(
5
),
1801
1811
(
2008
).
8.
Z.
Tian
and
G.
Giannakis
, “
A wavelet approach to wideband spectrum sensing for cognitive radios
,” in
Proc. IEEE Cognitive Radio Oriented Wireless Networks and Communications
(
2006
), pp.
1
5
.
9.
G.P.
Aswathy
,
K.
Gopakumar
, “
Sub-Nyquist wideband spectrum sensing techniques for cognitive radio: A review and proposed techniques
,”
AEU - International Journal of Electronics and Communications
,
104
,
44
57
, (
2019
).
10.
Z.
Tian
and
G.
Giannakis
, “Compressed sensing for wideband cognitive radios,” in
Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (2007)
, Vol.
4
, pp.
IV
1357
–IV-1360.
11.
M.
Mishali
and
Y.C.
Eldar
, “
Blind multiband signal reconstruction: Compressed sensing for analog signals
,”
IEEE Trans. Signal Process.
,
57
(
3
),
993
1009
, (
2009
).
12.
R.
Venkataramani
and
Y.
Bresler
, “
Optimal sub-Nyquist non-uniform sampling and reconstruction of multiband signals
,”
IEEE Trans. Signal Processing
,
49
,
2301
2313
, (2001).
13.
I.
Tropp
, “
Greed is good: algorithmic results for sparse approximation
,”
IEEE Trans. on Information Theory
,
50
(
10
), pp.
2231
2242
, (
2004
).
14.
J.
Wang
and
B.
Shim
, “
How many iterations are needed for the exact recovery of sparse signals using orthogonal matching pursuit?
” available at: http://arxiv.org/pdf/1211.4293v2.pdf
15.
S. K.
Sahoo
and
A.
Makur
, “
Signal recovery from random measurements via extended orthogonal matching pursuit
,”
IEEE Trans. Signal Processing
,
63
(
10
),
2572
2581
(
2015
).
16.
Aswathy
G. P
and
K.
Gopakumar
, “
Wideband Spectrum Sensing using Modulated Wideband Converter by Revised Orthogonal Matching Pursuit,” in
Proc. IEEE International Conference on Control, Power, Communication and Computing Technologies
(
2018
), pp.
179
184
.
17.
Aswathy
G. P.
and
K.
Gopakumar
, “
Cognitive Radio Network with Wideband Spectrum Sensing and Reliable Data Transmission
,” in
Proc. IEEE Recent Advances in Intelligent Computational Systems
(
2018
), pp.
70
74
.
18.
M.
Rashidi
,
K.
Haghighi
,
A.
Owrang
and
M.
Viberg
, “
A wideband spectrum sensing method for cognitive radio using sub-Nyquist sampling
”, in
Proc. IEEE Digital Signal Processing and Signal Processing Education Meeting
(
2011
), pp.
30
35
.
19.
C. B. Ali
Wael
,
N.
Armi
,
B. P.A.
Rohman
and
T.
Miftahushudur
, “
Performance Analysis of sub-Nyquist sampling for wideband spectrum sensing in Cognitive Radio
,” in
Proc. IEEE International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications
(
2016
), pp.
152
156
.
20.
P.
Pudil
,
J.
Novovicova
and
J.
Kittler
,
Pattern Recognition Letters
,
15
,
1119
1125
(
1994
).
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