Since the replacement of sensor nodes batteries is pretty impossible in a cognitive radio network, increasing the lifetime of these batteries leads to increase the lifetime of wireless sensor network. In this paper, we investigated to add the energy efficiency of these batteries. To reach this aim, we set our objective problem as ratio of network throughput and the network total power that must be maximized. At the same time, there are some constraints that must be satisfied. Since the objective function is a fractional nonlinear problem, we used two methods to maximize the function. At first, we used Charnes-Cooper transformation that transforms our objective function into a concave water-filling optimization problem that leads to an optimal solution. In the second way, we have used an iterative method within which particle swarm optimization algorithm is applied. Also, we tried different parameters in simulations and compared the results. The results prove the high capability of the iterative algorithm especially with lots of number of sensor nodes and primary users.

concave fractional program, energy efficiency, particle swarm optimization, power allocation, secondary sensor node, wireless sensor network.