PSO-adaptive power allocation for multiuser GFDM-based cognitive radio networks

Dynamic spectrum sensing through accelerated particle swarm optimization

“…where a t i = [a t i,1 · · · a t i,D ] T is the acceleration vector of the ith particle at iteration t. It is worth mentioning that the two terms on the right side of (14), which represent the cognitive and social components [26], respectively, are similar to elastic forces f = k c x, where k c is the elastic constant and x the distance vector from the center of mass (Hooke's law). Therefore, the velocity and position equations, considering also t = 1 in (10) and (11), can now be expressed as…”

“…It is a simple, fast and efficient stochastic swarm intelligence algorithm used in many discrete optimization problems [17]. PSO neither requires a differentiable objective function nor relies on a specific single variable initialization, while it is less complex than other evolutionary optimization methods, e.g., genetic algorithms [12], [14]. These merits render the PSO-based techniques attractive candidates for dealing with dynamic spectrum sensing in CRNs, which may involve non-convex and joint optimization of several parameters at the same time.…”

Analysis and rate optimization of GFDM‐based cognitive radios

“…In the work of Rosas et al, GFDM power allocation in underlay cognitive radio is solved via genetic algorithms. In the work of Dawoud et al, CR resource allocation is done by particle swarm optimization. However, although the optimization problem is not convex due to the interference on subcarriers, the dual Lagrange multiplier method is used as analytical solution in the aforementioned work .…”

“…In the work of Dawoud et al, CR resource allocation is done by particle swarm optimization. However, although the optimization problem is not convex due to the interference on subcarriers, the dual Lagrange multiplier method is used as analytical solution in the aforementioned work . Moreover, the metaheuristic approaches for nonconvex optimization problems, eg, particle swarm optimization, do not guarantee to reach to the global optimum because of the lack of any theoretical basis.…”

“…However, although the optimization problem is not convex due to the interference on subcarriers, the dual Lagrange multiplier method is used as analytical solution in the aforementioned work. 31 Moreover, the metaheuristic approaches for nonconvex optimization problems, eg, particle swarm optimization, do not guarantee to reach to the global optimum because of the lack of any theoretical basis. To the best of our knowledge, no analytical resource allocation strategies to increase the spectral efficiency of GFDM SUs for different receiver structures have been published before.…”