Energy Efficient Transmission in Underlay CR-NOMA Networks Enabled by Reinforcement Learning

In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)can simultaneously access to the same primary spectrum band under the interference constraints of the primary user(PU),by employing the non-orthogonal multiple access(NOMA)technique.In the proposed scheme,the optimization of the power allocation is formulated as a non-convex optimization problem.Additionally,the power allocation for different CUs is based on the actor-critic reinforcement learning model,in which the weighted data rate is set as the reward function,and the generated action strategy(i.e.the power allocation)is iteratively criticized and updated.Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning.Furthermore,the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions.According to the simulation results,we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method,while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint.The proposed AGDI control law will be comprised of two control elements.The baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives.The control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized inversion.To boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode principle.In discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction.The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed.Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

Ultra-wideband Frequency Selective Surface for Communication Applications

A low-profile ultra-wideband(UWB)band-stop frequency selective surface(FSS)is designed for S-,C-,X-and Ku-bands communication applications.The FSS is constructed by using square and circular loop elements printed on the top and bottom sides of the RO3210 substrate.The FSS has been designed to reduce the electromagnetic interference(EMI)as well as to mitigate the harmful effects of electromagnetic radiation on the human body caused by different radio devices.The dimension and size of the UWB FSS have been reduced to 0.12λ×0.12λand 90%,respectively,as compared to the reported literature.The other advantages of the proposed FSS are that it is low profile,it has a simplified geometry and it ensures better angular and polarization stability of up to 85◦.The-10 and-20 dB bandwidths of the proposed FSS are 146%(2.0-13.0 GHz)and 80%(4.87-11.42 GHz),respectively.Theoretical results have been obtained using ANSYS HFSS and verified through measured results.