Energy-Efficient Beamforming and Resource Optimization for AmBSC-Assisted Cooperative NOMA IoT Networks

In this manuscript, we present an energy-efficient alternating optimization framework based on the multiantenna ambient backscatter communication (AmBSC)-assisted cooperative nonorthogonal multiple access (NOMA) for next-generation (NG) Internet of Things (IoT)-enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multicluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PACs) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multiantenna-assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in intercluster interference (ICI) (among different clusters) and intracluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF)-based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intracluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the Quality-of-Service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered nonconvex passive-beamforming problem is transformed into a standard semidefinite programming (SDP) problem by exploiting the successive-convex approximation (SCA), as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.