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 multi-antenna ambient backscatter communication (AmBSC) assisted cooperative non-orthogonal multiple access (NOMA) for next-generation (NG) internet-of-things (IoT) enabled networks. Specifically, energy-efficiency maximization is achieved considered AmBSC-enabled multi-cluster IoT NOMA system by optimizing active-beamforming vector and power-allocation coefficients (PAC) of users at transmitter, as well passive-beamforming node. Usually, increasing number in each cluster results inter-cluster interference (ICI) (among different clusters) intra-cluster users). To combat impact ICI, exploit a zero-forcing (ZF) active-beamforming, efficient clustering technique source Further, effect mitigated exploiting policy that determines PAC under quality-of-service (QoS), cooperation, SIC decoding, power-budget constraints. Moreover, non-convex problem transformed into standard semi-definite programming (SDP) successive-convex approximation (SCA) approximation, difference convex (DC) programming, where Rank-1 solution obtained penalty-based method. Furthermore, numerical analysis simulation demonstrates proposed algorithm exhibits performance achieving convergence within only few iterations.