Abstract
The natural acoustic system used by marine mammals and the artificial sonar system used by humans coexist in the underwater cognitive sonar communication networks (CSCN). They share the spectrum when they are in the same waters. The CSCN detects the natural acoustic signal depending on cooperative spectrum sensing of sonar nodes. In order to improve spectrum sensing performance of CSCN, the optimization of cooperative spectrum sensing and data transmission is investigated. We seek to obtain spectrum efficiency maximization (SEM) and energy efficiency maximization (EEM) of CSCN through jointly optimizing sensing time, subchannel allocation, and transmission power. We have formulated a class of optimization problems and obtained the optimal solutions by alternating direction optimization and Dinkelbach’s optimization. The simulation results have indicated that SEM can achieve higher spectrum efficiency while EEM may get higher energy efficiency.
Highlights
1 Introduction Cognitive radio (CR) can improve spectrum utilization greatly through letting secondary user (SU) to access the idle spectrum licensed to the primary user (PU) [1]
The performance of energy detection will decrease if the PU is in fading or shadowing path, which is called “hidden terminal problem.”
We investigate the joint parameter optimization of cooperative spectrum sensing and data transmission for multichannel cognitive sonar communication networks (CSCN)
Summary
Cognitive radio (CR) can improve spectrum utilization greatly through letting secondary user (SU) to access the idle spectrum licensed to the primary user (PU) [1]. Our goal is to maximize the spectrum efficiency of the SU by jointly optimizing sensing time, subchannel allocation, and transmission power, subject to the constraints that the detection probability total power of SU n is is above the lower limit below the maximal power. 1: Initialize q(k) = 0 where the iteration index k = 1, and the estimation error δ; 2: With given q(k), use the ADO to obtain the solution τ (k), an,l (k) , pn,l (k) to the following equivalent optimization problem: F(q(k)) = max ηSE τ , an,l , pn,l −q(k)ET τ, an,l , pn,l | τ , an,l , pn,l ∈ S ; 3: If F(q(k)) ≤ δ, go to step (5), otherwise, go to step (4); 4: Let q(k+1). As ET (τ , {an,l}, {pn,l}) is a linear positive function, we can solve the optimization problem (21) by the Dinkelbach optimization [14]
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