On Scalability of FDD-Based Cell-Free Massive MIMO Framework

Area of Science:

• Wireless communication systems
• Signal processing in telecommunications
• Massive MIMO technology

Background:

• Cell-free massive MIMO offers joint services and scalable quality of service for numerous users.
• Research predominantly uses time division duplex (TDD) due to channel reciprocity, while frequency division duplex (FDD) dominates wireless standards.
• FDD systems face scalability challenges in cell-free massive MIMO due to high computational complexity in signal processing.

Purpose of the Study:

• To propose and analyze a scalable cell-free massive MIMO network operating in the FDD mode.
• To leverage angular reciprocity to reduce overhead in FDD cell-free systems.
• To compare the performance of proposed scalable FDD techniques against conventional methods.

Main Methods:

• Developed a scalable FDD cell-free network incorporating angular reciprocity and dynamic cooperation clustering.
• Performed comparative analysis of channel estimation, power allocation, and precoding/combining techniques.
• Derived expressions for scalable spectral efficiency and angle-based precoding/combining schemes.

Main Results:

• The proposed scalable FDD cell-free network outperforms conventional matched filtering schemes.
• Angle-based LP-MMSE in the FDD cell-free network shows a 14.3% improvement in spectral efficiency.
• A 11.11% improvement in energy efficiency is observed with the angle-based LP-MMSE scheme compared to the scalable MF scheme.

Conclusions:

• The proposed scalable FDD cell-free network with angular reciprocity effectively addresses scalability challenges.
• Angle-based precoding and combining schemes offer significant performance gains in FDD cell-free systems.
• The research validates the advantages of the proposed FDD approach for future wireless networks.