In large-array millimeter-wave (mmWave) systems, hybrid multi-user precoding is one of the most attractive research topics. This paper first presents a low-dimensional manifolds architecture for the analog precoder. An objective function is formulated to maximize the Energy Efficiency (EE) in consideration of the insertion loss for hybrid multi-user precoder. The optimal scheme is intractable to achieve, so that we present a user clustering hybrid precoding scheme. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. We discuss the effect of non-ideal factors on the EE performance. Through proper user clustering, the hybrid multi-user precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions.
Sum Rate Maximization for Multi-User Wireless Powered IoT Network With Non-Linear Energy Harvester: Time and Power Allocation
