TY - JOUR
T1 - Energy Efficiency Analysis of Collaborative Compressive Sensing Scheme in Cognitive Radio Networks
AU - Kishore, Rajalekshmi
AU - Gurugopinath, Sanjeev
AU - Muhaidat, Sami
AU - Sofotasios, Paschalis C.
AU - Dianati, Mehrdad
AU - Al-Dhahir, Naofal
N1 - Funding Information:
Manuscript received November 25, 2018; revised March 16, 2019; accepted April 29, 2019. Date of publication July 8, 2020; date of current version September 9, 2020. This work was supported in part by Khalifa University under Grant KU/FSU-8474000122 and Grant KU/RC1-C2PS-T2/8474000137. This work will appear in part in [1]. The associate editor coordinating the review of this article and approving it for publication was F. Bader. (Corresponding author: Paschalis C. Sofotasios.) Rajalekshmi Kishore is with the Department of Electrical and Electronics Engineering, BITS Pilani (K. K. Birla Goa Campus), Goa 403726, India (e-mail: [email protected]).
Publisher Copyright:
© 2015 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and then formulate an optimization problem to determine the optimal values of parameters which maximize the energy efficiency of the CCCS scheme. The maximization of energy efficiency is proposed as a multi-variable, non-convex optimization problem, and we provide approximations to reduce it to a convex optimization problem. We highlight that errors due to these approximations are negligible. Subsequently, we analytically characterize the tradeoff between dimensionality reduction and collaborative sensing performance of the CCCS scheme, i.e., the implicit tradeoff between energy saving and detection accuracy. It is shown that the resulting loss due to compression can be recovered through collaboration, which improves the overall energy efficiency of the system.
AB - In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and then formulate an optimization problem to determine the optimal values of parameters which maximize the energy efficiency of the CCCS scheme. The maximization of energy efficiency is proposed as a multi-variable, non-convex optimization problem, and we provide approximations to reduce it to a convex optimization problem. We highlight that errors due to these approximations are negligible. Subsequently, we analytically characterize the tradeoff between dimensionality reduction and collaborative sensing performance of the CCCS scheme, i.e., the implicit tradeoff between energy saving and detection accuracy. It is shown that the resulting loss due to compression can be recovered through collaboration, which improves the overall energy efficiency of the system.
KW - Achievable throughput
KW - collaborative compressive sensing
KW - energy consumption
KW - energy efficiency
KW - spectrum sensing
UR - http://www.scopus.com/inward/record.url?scp=85090232901&partnerID=8YFLogxK
U2 - 10.1109/TCCN.2020.3007901
DO - 10.1109/TCCN.2020.3007901
M3 - Article
AN - SCOPUS:85090232901
SN - 2332-7731
VL - 6
SP - 1056
EP - 1068
JO - IEEE Transactions on Cognitive Communications and Networking
JF - IEEE Transactions on Cognitive Communications and Networking
IS - 3
M1 - 9136806
ER -
