A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

Automated summary

In this paper, a robust adaptive target power allocation strategy is proposed for cognitive nonorthogonal multiple access (NOMA) networks, which allows single-station communication to achieve energy efficiency or high throughput by introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor. Different cognitive users can choose different communication targets, and different quality of service (QoS) can be selected by the same cognitive user at different times. With imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case and solved using the dual decomposition method. Simulation results demonstrate the strategy's good adaptive selectivity and robustness.