Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination (1904.03406v3)

Abstract: Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted in 5G and base stations (BSs) with $M=64$ full-digital transceivers have been commercially deployed in sub-6\,GHz bands. The fast progress was enabled by many solid research contributions of which the vast majority assume spatially uncorrelated channels and signal processing schemes developed for single-cell operation. These assumptions make the performance analysis and optimization of Massive MIMO tractable but have three major caveats: 1) practical channels are spatially correlated; 2) large performance gains can be obtained by multicell processing, without BS cooperation; 3) the interference caused by pilot contamination creates a finite capacity limit, as $M\to\infty$. There is a thin line of papers that avoided these caveats, but the results are easily missed. Hence, this tutorial article explains the importance of considering spatial channel correlation and using signal processing schemes designed for multicell networks. We present recent results on the fundamental limits of Massive MIMO, which are not determined by pilot contamination but the ability to acquire channel statistics. These results will guide the journey towards the next level of Massive MIMO, which we call ``Massive MIMO 2.0''.

• The paper demonstrates that integrating spatial correlation into signal processing significantly boosts spectral efficiency in Massive MIMO systems.
• It employs multicell MMSE combining/precoding techniques to effectively suppress interference beyond conventional single-cell methods.
• The study challenges traditional pilot contamination views by showing that accurate channel statistics can mitigate its impact in practical deployments.

Understanding Massive MIMO 2.0: Spatial Correlation, Interference Suppression, and Pilot Contamination

The paper "Towards Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination" by Luca Sanguinetti, Emil Björnson, and Jakob Hoydis provides a comprehensive examination of the advancements and ongoing challenges in Massive MIMO technology as it progresses towards what the authors denote as "Massive MIMO 2.0." The focus is on bridging the gap between theoretical models and practical deployments, especially by taking into account spatial correlation and interference management.

Key Insights and Advances

Massive MIMO, originally conceptualized by T. Marzetta in 2010, is a cornerstone technology adopted within 5G, with commercial base stations having implementations of 64 fully digital transceivers in sub-6 GHz bands. The rapid advancements in this field have been supported by several simplifying assumptions in the literature, such as spatially uncorrelated channels and single-cell operations without base station cooperation. These assumptions, while analytically convenient, are not reflective of real-world conditions and thus present three significant challenges:

1. Spatial Channel Correlation: Practical channels exhibit spatial correlation due to physical characteristics of the transmission environment and antenna arrays. This correlation must be considered to enhance signal processing.
2. Multicell Processing Gains: Large performance gains can be achieved via multicell processing techniques which navigate interference without base station cooperation.
3. Pilot Contamination: Historically seen as a roadblock due to its interference causing finite capacity limits, pilot contamination must be revisited with a focus on accurate channel statistics acquisition.

Numerical Results and Theoretical Claims

The research provides numerical results which demonstrate that the theoretical upper bounds of spectral efficiency (SE) can be realized in practice by integrating spatial correlation into the signal processing models. This insight challenges the previously held notion that pilot contamination is an immutable barrier in capacity expansion. Indeed, the authors illustrate that by employing multicell MMSE (M-MMSE) combining/precoding methods and capitalizing on correlation properties, pilot contamination can be mitigated, and coherent interference effectively managed.

Theoretical claims proceed to show that spatially correlated fading channels permit potential SE gains, as opposed to the limitations seen in uncorrelated channel models. For instance, while the gains of single-cell MMSE (S-MMSE) are capped by pilot contamination, M-MMSE exploits unique spatial correlation matrices to surpass this limit, offering asymptotically unbounded growth as the number of antennas increases towards infinity.

Implications and Future Paths

This paper has significant implications for both the theoretical understanding and practical deployment of 5G networks and beyond. Massive MIMO 2.0 underscores the necessity of moving past simplified channel models and instead adopting more sophisticated methods that consider real-world correlation and interference dynamics.

With potential applications extending into large intelligent surfaces and post-cellular networks, the pursuit of deeper spatial correlation integration and enhanced interference handling is expected to continue driving the evolution of wireless communication. As the next horizon in wireless technology, sub-THz communications could leverage these advancements to achieve unprecedented data rates, although the physical and computational challenges still require additional solutions.

Conclusion

Overall, the research by Sanguinetti et al. sets a pivotal foundation for the progression towards Massive MIMO 2.0, marking a transition to higher fidelity models and more sophisticated processing techniques that harness the inherent characteristics of real-world wireless channels. The work compels the research community to focus on leveraging spatial channel correlation to not only overcome current limitations but also to pave the way for the next generation of wireless communications technologies. Future research will need to address computational complexity, channel state acquisition, and practical implementation details to fully realize the potential of this transformative technology.