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In this manuscript, we present an energy-efficient alternating optimizationframework based on the multi-antenna ambient backscatter communication (AmBSC)assisted cooperative non-orthogonal multiple access (NOMA) for next-generation(NG) internet-of-things (IoT) enabled communication networks. Specifically, theenergy-efficiency maximization is achieved for the considered AmBSC-enabledmulti-cluster cooperative IoT NOMA system by optimizing the active-beamformingvector and power-allocation coefficients (PAC) of IoT NOMA users at thetransmitter, as well as passive-beamforming vector at the multi-antennaassisted backscatter node. Usually, increasing the number of IoT NOMA users ineach cluster results in inter-cluster interference (ICI) (among differentclusters) and intra-cluster interference (among IoT NOMA users). To combat theimpact of ICI, we exploit a zero-forcing (ZF) based active-beamforming, as wellas an efficient clustering technique at the source node. Further, the effect ofintra-cluster interference is mitigated by exploiting an efficientpower-allocation policy that determines the PAC of IoT NOMA users under thequality-of-service (QoS), cooperation, SIC decoding, and power-budgetconstraints. Moreover, the considered non-convex passive-beamforming problem istransformed into a standard semi-definite programming (SDP) problem byexploiting the successive-convex approximation (SCA) approximation, as well asthe difference of convex (DC) programming, where Rank-1 solution ofpassive-beamforming is obtained based on the penalty-based method. Furthermore,the numerical analysis of simulation results demonstrates that the proposedenergy-efficiency maximization algorithm exhibits an efficient performance byachieving convergence within only a few iterations.

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