a multi-rate queue management for delay-constrained non-orthogonal multiple access (noma) based secure cognitive radio network

In this article, we consider a secure cognitive radio network (crn) deploying nonorthogonal multiple access (noma). a mixed delay constrained multicast and unicast traffic is received by the intended crn receivers, while keeping the traffic secret from the eavesdroppers. physical layer security (pls) is deployed to secure the confidential messages of both primary user (pu) and secondary user (su). a queue management policy (qmp) is proposed to enhance the quality of service (qos) of crn. the proposed qmp adaptively uses all or some of the su’s resources towards the transmission of the pu’s packet, this decision being based on the packet’s delay experienced in the pu queue. exact delay distribution of pu traffic is derived via a novel multiregime markov fluid queue (mrmfq) model. thanks to the closed form expressions, the proposed qmp is optimized to provide the highest attainable throughput for su, while satisfying pu’s qos constraints. it is shown via numerical examples that noma based crn consistently outperforms orthogonal multiple access (oma) based counterpart. we also show that the performance improvements gained by the proposed qmp depends on the intensity of pu traffic as well as the channel conditions. moreover, a heuristic suboptimal parameter selection procedure with significantly lesser computational complexity is proposed for less capable devices.

A Multi-Rate Queue Management for Delay-Constrained Non-Orthogonal Multiple Access (NOMA) based Secure Cognitive Radio Network

In this article, we consider a secure cognitive radio network (CRN) deploying nonorthogonal multiple access (NOMA). A mixed delay constrained multicast and unicast traffic is received by the intended CRN receivers, while keeping the traffic secret from the eavesdroppers. Physical layer security (PLS) is deployed to secure the confidential messages of both primary user (PU) and secondary user (SU). A queue management policy (QMP) is proposed to enhance the quality of service (QoS) of CRN. The proposed QMP adaptively uses all or some of the SU’s resources towards the transmission of the PU’s packet, this decision being based on the packet’s delay experienced in the PU queue. Exact delay distribution of PU traffic is derived via a novel multiregime Markov fluid queue (MRMFQ) model. Thanks to the closed form expressions, the proposed QMP is optimized to provide the highest attainable throughput for SU, while satisfying PU’s QoS constraints. It is shown via numerical examples that NOMA based CRN consistently outperforms orthogonal multiple access (OMA) based counterpart. We also show that the performance improvements gained by the proposed QMP depends on the intensity of PU traffic as well as the channel conditions. Moreover, a heuristic suboptimal parameter selection procedure with significantly lesser computational complexity is proposed for less capable devices.