Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation (1903.10611v2)

Abstract: Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements.

• The paper demonstrates that fully centralized MMSE processing (Level 4) achieves superior spectral efficiency compared to other configurations and traditional cellular systems.
• It reveals that local processing with LSFD (Level 3) offers improvements over simpler centralized decoding (Level 2) yet falls short of the optimal Level 4 performance.
• The findings underscore the importance of advanced centralized strategies for unlocking the full potential of Cell-Free Massive MIMO in future beyond-5G networks.

Analysis of Cell-Free Massive MIMO Implementation Approaches

The paper "Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation" by Emil Björnson and Luca Sanguinetti presents a comprehensive examination of Cell-free Massive MIMO systems, evaluating their potential to surpass the capabilities of conventional Cellular Massive MIMO networks. The authors aim to identify effective configurations and processing strategies that can enhance the competitiveness of the Cell-free approach.

Key Findings

The paper provides a detailed evaluation of four distinct approaches to implementing Cell-free Massive MIMO systems:

1. Level 4 - Fully Centralized Processing: This approach utilizes a centralized CPU that aggregates the pilot and data signals from all access points (APs) and performs centralized MMSE processing. The analysis shows that this configuration, when used with MMSE processing, achieves superior spectral efficiency (SE) compared to other levels, and can outperform Cellular Massive MIMO systems.

2. Level 3 - Local Processing with Large-Scale Fading Decoding (LSFD): APs locally estimate the channels and preprocess the signals before sending them to the CPU. The CPU then performs joint detection based on large-scale channel statistics. While this method facilitates improved SE over Level 2, it remains less efficient than a fully centralized approach.

1. Level 2 - Simple Centralized Decoding: APs perform local channel estimation and signal preprocessing. The CPU averages these results without utilizing channel statistics, reducing the fronthaul load but at the cost of lower SE compared to Levels 3 and 4.
2. Level 1 - Small-Cell Network: This represents a fully distributed approach where each AP independently serves UEs with local channel estimates. This setup resembles small-cell networks and offers reduced spectral efficiency relative to the centralized levels.

Numerical Results and Comparisons

The numerical results demonstrate that the Level 4 implementation with MMSE processing consistently offers the highest SE across various user conditions. Levels 1-3 showed a performance deficit; particularly, the MR (Maximum Ratio) combining technique, prevalent in early literature, was found to be suboptimal for Cell-free systems.

In revisiting the debate between Cell-free Massive MIMO and small cells, the paper reveals that small cells can appear favorable when suboptimal MR processing is employed in Cell-free systems. However, the application of MMSE processing to Cell-free setups, particularly centralized implementations, provides a clear performance advantage, emphasizing the need for advanced processing strategies.

Implications and Future Directions

The implications of these findings extend beyond the direct competition between network architectures. The results suggest a shift towards centralized processing in future network designs, potentially influencing the evolution of network infrastructure, particularly for beyond-5G deployments. Non-linear processing techniques, such as successive interference cancellation, were analyzed but found to offer limited additional benefits due to the naturally favorable propagation conditions in Cell-free systems.

Future developments in this domain could focus on enhancing Levels 2 and 3, possibly integrating more sophisticated non-linear processing techniques. The exploration of fronthaul optimizations, including serial fronthaul arrangements, presents an avenue to balance the communication overhead with computation capabilities.

In summary, this paper's assessment of the various levels of Cell-free Massive MIMO implementation underscores the significance of MMSE processing within centralized frameworks to unlock the full potential of these networks, offering insights that could drive future advancements in wireless communications design.