Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management

Özlem Tuğfe Demir, Lianet Méndez-Monsanto, Nicola Bastianello, Emma Fitzgerald, Gilles Callebaut

TL;DR

The paper tackles energy minimization in cell-free massive MIMO by introducing a federation-based framework where disjoint AP groups serve UEs on orthogonal resources to curb interference. It develops a detailed end-to-end hardware energy model and formulates a mixed-integer problem to jointly optimize AP–UE association, active CSPs, and federation assignments, aiming to meet downlink rate targets. A divide-and-conquer alternating-minimization algorithm with slack-variable penalties is proposed to solve the optimization efficiently, including a random-activation refinement to enhance feasibility. Through an indoor factory evaluation at 3 GHz, the study shows that federations can yield substantial energy savings, with deployment topology (distributed vs co-located) playing a crucial role depending on rate requirements and feasibility.

Abstract

The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.

Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management

TL;DR

The paper tackles energy minimization in cell-free massive MIMO by introducing a federation-based framework where disjoint AP groups serve UEs on orthogonal resources to curb interference. It develops a detailed end-to-end hardware energy model and formulates a mixed-integer problem to jointly optimize AP–UE association, active CSPs, and federation assignments, aiming to meet downlink rate targets. A divide-and-conquer alternating-minimization algorithm with slack-variable penalties is proposed to solve the optimization efficiently, including a random-activation refinement to enhance feasibility. Through an indoor factory evaluation at 3 GHz, the study shows that federations can yield substantial energy savings, with deployment topology (distributed vs co-located) playing a crucial role depending on rate requirements and feasibility.

Abstract

The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.
Paper Structure (15 sections, 9 equations, 3 figures, 1 table)

This paper contains 15 sections, 9 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. State-of-the-Art
  3. Contributions
  4. Terminology, Concepts, and Models
  5. Hardware and Energy Models
  6. Fronthaul
  7. Power Amplifier
  8. Sampling and Processing
  9. Energy Model
  10. System Model
  11. Federation Assignment: Problem Formulation and Proposed Algorithm
  12. Problem Formulation
  13. Proposed Algorithm
  14. Evaluation in an Industrial Factory Environment
  15. Conclusion

Figures (3)

  • Figure 1: Illustration of the system with two , four and four coordinated by one . Only a subset of resources (a federation) is activated during joint downlink precoding based on the requirements and the propagation channel conditions. The objective is to optimize the total energy expenditure of the network. Two federations are depicted, serving 1 (blue) and 2 (purple).
  • Figure 2: Total power consumption in terms of data rate for different numbers of CSPs and antennas. Each row in the legend corresponds with the same number of total antennas.
  • Figure 3: Power consumption in terms of number of federations for different rate requirements.