Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO (1505.01181v2)

Abstract: How would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable uplink energy efficiency (EE) maximization problem and solve it analytically with respect to the density of base stations (BSs), the transmit power levels, the number of BS antennas and users per cell, and the pilot reuse factor. The closed-form expressions obtained from this general EE maximization framework provide valuable insights on the interplay between the optimization variables, hardware characteristics, and propagation environment. Small cells are proved to give high EE, but the EE improvement saturates quickly with the BS density. Interestingly, the maximal EE is achieved by also equipping the BSs with multiple antennas and operate in a "massive MIMO" fashion, where the array gain from coherent detection mitigates interference and the multiplexing of many users reduces the energy cost per user.

• The paper develops a framework using stochastic geometry to optimize energy efficiency in future dense networks, deriving a tractable optimization problem.
• Analytical methods yield closed-form solutions that reveal how system parameters like base station density and transmit power interact with hardware characteristics to affect energy efficiency.
• Small cells improve energy efficiency but saturate, while massive MIMO with coherent detection is shown to maximize it, with the framework accounting for hardware impairments and power consumption.

Overview of "Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO"

This paper addresses the design of future cellular networks optimized for maximal energy efficiency (EE). The authors leverage stochastic geometry to model future networks and derive analytical frameworks and guidelines to enhance energy efficiency in heterogeneous mobile networks. They explore the benefits of dense deployments and compare the effectiveness of small cells versus massive MIMO technologies in achieving energy savings.

The authors develop a tractable model for the typical user in a network employing massive MIMO, consisting of multi-cell, multi-user MIMO setups where base stations (BSs) are equipped with multiple antennas. In these setups, they propose maximizing the EE by considering various parameters including the density of BSs, transmit powers, the number of antennas per BS, and users per cell, including the pilot reuse factor, and evaluating how these affect the network's EE.

Key Contributions

1. Energy Efficiency Maximization Framework: The authors establish a new lower bound on the average uplink spectral efficiency, facilitating the formulation of a tractable energy efficiency optimization problem.
2. Closed-Form Solutions: Through analytical methods, closed-form solutions are derived, revealing the interdependencies between system optimizations and hardware characteristics.
3. Small Cells and Massive MIMO Analysis: It is demonstrated that small cells significantly improve EE. However, this improvement saturates quickly with increasing BS density. The paper highlights that equipping BSs with massive MIMO and enabling coherent detection to mitigate interference maximizes EE.
4. The Role of Hardware Impairments and Power Consumption: The framework includes the effects of hardware impairments and quantifies the contribution of the transmit and circuit power to the EE, thus providing practical insights into power consumption balancing.

Implications and Future Directions

This paper's findings have substantial implications for the design of 5G networks and beyond. The paper demonstrates that network densification through either small cells or massive MIMO provides a pathway to meet the stringent EE demands of the future. However, careful balancing is required as rapid increases in BS density eventually lead to power consumption trade-offs due to hardware constraints.

This research could spur future investigations into optimizing EE from a holistic network view. Future directions may include further exploration into dynamic network configurations based on user distribution and traffic demands, as well as incorporating downlink analysis or hybrid architectures combining different technologies. Additionally, as more realistic and detailed power models evolve, the methodologies outlined here can be further refined to incorporate diverse technical and economic constraints.

In summary, this paper provides a comprehensive theoretical framework and significant insights into designing energy-efficient wireless networks. The analysis of dense networks backed by analytical expressions for EE optimization variables constitutes a foundational advancement towards sustainable wireless communication systems.