Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI

Area of Science:

• Wireless communication systems engineering
• Signal processing for telecommunications
• Information theory and coding

Background:

• Massive MIMO is crucial for 5G, enhancing energy efficiency (EE) and spectral efficiency (SE).
• Physical layer multicast offers efficient point-to-multipoint transmission, a focus for next-gen networks.
• Accurate channel state information (CSI) is vital for massive MIMO, but perfect instantaneous CSI is challenging.

Purpose of the Study:

• To investigate energy efficiency-spectral efficiency (EE-SE) trade-offs in massive MIMO multicast transmission using statistical CSI.
• To optimize system resource efficiency (RE) for a balanced EE-SE performance.
• To develop a practical transmission strategy using readily available statistical CSI.

Main Methods:

• Formulated eigenvectors of resource efficiency (RE) optimization multicast covariance matrices in closed form.
• Demonstrated that optimal RE multicast precoding in massive MIMO downlink occurs in the beam domain.
• Proposed an iterative power allocation algorithm using quadratic transform for adjustable EE-SE trade-offs.

Main Results:

• Optimal RE multicast precoding is simplified to a resource-efficient power allocation problem in the beam domain.
• The proposed iterative power allocation algorithm achieves adjustable and reasonable EE-SE trade-offs.
• Numerical simulations confirm the near-optimal performance and effectiveness of the statistical CSI-assisted RE maximization.

Conclusions:

• Statistical CSI is a practical approach for massive MIMO multicast transmission design.
• The proposed method effectively maximizes resource efficiency, balancing energy and spectral efficiency.
• This research contributes to the development of more efficient 5G and future wireless systems.