TY - JOUR
T1 - Efficient spectrum availability information recovery for wideband dsa networks
T2 - A weighted compressive sampling approach
AU - Khalfi, Bassem
AU - Hamdaoui, Bechir
AU - Guizani, Mohsen
AU - Zorba, Nizar
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - There have recently been research efforts that leverage compressive sampling to enable wideband spectrum sensing recovery at sub-Nyquist rates. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar primary user traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different bands could present different occupancy patterns. In fact, applications of similar types are often assigned spectrum bands within the same block, dictating that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum and exploit its inherent block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ℓ(1) -minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches, while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a smaller number of sensing measurements when compared to the state-of-the-art approaches.
AB - There have recently been research efforts that leverage compressive sampling to enable wideband spectrum sensing recovery at sub-Nyquist rates. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar primary user traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different bands could present different occupancy patterns. In fact, applications of similar types are often assigned spectrum bands within the same block, dictating that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum and exploit its inherent block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ℓ(1) -minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches, while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a smaller number of sensing measurements when compared to the state-of-the-art approaches.
KW - Wideband spectrum sensing
KW - compressive sampling
KW - heterogeneous wideband spectrum occupancy
UR - http://www.scopus.com/inward/record.url?scp=85041205356&partnerID=8YFLogxK
U2 - 10.1109/TWC.2018.2789349
DO - 10.1109/TWC.2018.2789349
M3 - Article
AN - SCOPUS:85041205356
SN - 1536-1276
VL - 17
SP - 2162
EP - 2172
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 4
ER -