Designing Multi-User MIMO for Energy Efficiency: When is Massive MIMO the Answer?

Abstract: Assume that a multi-user multiple-input multiple-output (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bit/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describes how these three parameters affect the total energy efficiency of the system, this work provides closed-form expressions for their optimal values and interactions. In sharp contrast to common belief, the transmit power is found to increase (not decrease) with the number of antennas. This implies that energy efficient systems can operate at high signal-to-noise ratio (SNR) regimes in which the use of interference-suppressing precoding schemes is essential. Numerical results show that the maximal energy efficiency is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve relatively many users using interference-suppressing regularized zero-forcing precoding.

• The paper derives closed-form expressions for optimal antenna count (M), users (K), and transmit power (ρ) to maximize energy efficiency in multi-user MIMO systems.
• It proposes a power model including circuit consumption and shows that maximizing EE requires transmit power to increase with the number of antennas (M).
• Numerical results indicate that massive MIMO configurations with hundreds of antennas are optimal for maximizing energy efficiency in macro-cell deployments.

Energy Efficiency in Multi-User MIMO Systems: Optimal Design Choices

The paper "Designing Multi-User MIMO for Energy Efficiency: When is Massive MIMO the Answer?" explores the optimization of energy efficiency (EE) in multi-user MIMO systems, specifically focusing on the number of antennas at the base station (BS), the number of active user equipments (UEs), and the transmit power. Authored by Emil Bj{\"o}rnson, Luca Sanguinetti, Jakob Hoydis, and Merouane Debbah, this research explores deriving closed-form expressions for these parameters to achieve maximal EE, measured in bit/Joule.

Key Findings

1. Power Consumption Model: The study proposes a comprehensive power consumption model that incorporates not only the transmit power but also accounts for circuit power consumption, which scales with the number of antennas $M$ and users $K$. This nuanced model is essential for realistic EE optimization.
2. Optimal Transmit Power: Contrary to the prevailing assumption that transmit power reduces with increased antenna numbers, the paper demonstrates that, to maximize EE, the transmit power should increase alongside $M$. This result implies that energy-efficient systems could operate effectively in high signal-to-noise ratio (SNR) regimes, where sophisticated interference-suppressing techniques like zero-forcing (ZF) precoding become critical.
3. Closed-form Solutions: The authors derive expressions for the optimal $M$, $K$, and transmit power $\rho$ using ZF precoding and confirm their applicability through numerical simulations for other common precoding schemes. This analytical approach provides valuable insights into how these parameters interact to affect EE.
4. Massive MIMO Configuration: Numerical results indicate that deploying hundreds of antennas to serve a significant number of users is the optimal solution for maximizing EE in macro-cell configurations. This setup leverages the interference-suppression capabilities of massive MIMO, aligning with the conclusions drawn under ideal conditions.

Implications

The implications of this research are both broad and significant. From a practical standpoint, optimizing EE through the careful selection of system parameters can lead to more sustainable network operations with reduced energy consumption costs. Theoretically, the work challenges existing paradigms regarding transmit power in large-scale antenna configurations, advocating for a reassessment of power allocation strategies in massive MIMO architectures.

Future Developments

As circuit technology advances and power consumption components become more efficient, these findings suggest that the transmit power at the EE-optimal point could decrease over time. Additionally, the need for real-time processors capable of supporting fast ZF and regularized ZF (RZF) precoding emphasizes the importance of continued research in hardware development for future wireless communication systems.

In conclusion, the paper offers a substantive contribution to the field by redefining the criteria for energy-efficient design in multi-user MIMO systems. It underscores the strategic importance of massive MIMO configurations and interference management, setting a foundation for future exploration and implementation in the field of green wireless technologies.