A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for fifth-generation (5G) wireless networks. It improves both spectral and energy efficiency by utilizing 30–300 GHz millimeter-wave bandwidth a large number antennas at base station. However, increasing requires radio frequency (RF) chains which results in high power consumption. In order to reduce RF chain's energy, cost provide desirable quality-of-service (QoS) subscribers, this paper proposes an energy-efficient hybrid precoding algorithm mmWave massive networks based on idea selection. The sparse digital problem generated analog codebook. Then, it jointly solved through iterative fractional programming successive convex optimization (SCA) techniques. Simulation show that proposed scheme outperforms existing schemes effectively system performance under different operating conditions.