Open AccessJoint relay selection and power allocation for NOMA‐based multicast cognitive radio networks
Author(s) -
Baidas Mohammed W.,
Alsusa Emad,
Hamdi Khairi A.
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0954
Subject(s) - relay , computer science , multicast , transmitter , cognitive radio , quality of service , computer network , broadcasting (networking) , transmitter power output , computational complexity theory , selection (genetic algorithm) , mathematical optimization , joint (building) , power (physics) , telecommunications , algorithm , mathematics , wireless , engineering , architectural engineering , channel (broadcasting) , quantum mechanics , artificial intelligence , physics
In this study, the problem of joint relay selection and power allocation (J‐RS‐PA) for NOMA‐based multicast cognitive radio networks is considered. In particular, the aim is to simultaneously maximise the end‐to‐end SINR/SNR of the primary and secondary transmitter–receiver (TR) pairs, subject to quality‐of‐service (QoS) constraints. Communication between the primary and secondary TR pairs is performed over two‐phases, namely, the broadcasting phase, and the cooperation phase. In the broadcasting phase, the primary and secondary transmitters broadcast their data symbols; while in the cooperation phase, the selected relay forwards the decoded symbols to their intended receivers. However, the formulated J‐RS‐PA problem happens to be non‐convex, resulting in computationally‐prohibitive complexity. Consequently, an optimal low‐complexity two‐stage relay selection and power allocation (TS‐RS‐PA) algorithm is devised, which is based on the solution of linear programming problem reformulations. Simulation results are presented to validate the proposed TS‐RS‐PA algorithm, which is shown to yield the optimal SINR/SNR values in comparison to the J‐RS‐PA scheme, but with lower computational complexity, while satisfying QoS constraints.