Energy-efficient transmit antenna selection with Fast-ABC-Boost – Nature

Executive Summary

This report details an investigation into energy-efficient antenna selection for Multiple-Input Multiple-Output (MIMO) systems, a critical challenge for advancing sustainable digital infrastructure in line with the UN’s Sustainable Development Goals (SDGs). The high power consumption of large-scale MIMO systems, foundational to 5G and 6G networks, poses a significant barrier to achieving SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). To address this, an antenna selection technique based on the Fast-Adaptive Base Class-Boost (Fast-ABC-Boost) machine learning algorithm is proposed. This method treats antenna selection as a multi-class classification task to maximize Energy Efficiency (EE). Simulation results demonstrate that the Fast-ABC-Boost approach significantly outperforms contemporary methods, including Deep Reinforcement Learning (DRL) and Cyclic Binary Particle Swarm Optimization (CBPSO), in EE performance while maintaining feasible computational complexity. This innovation offers a viable pathway toward greener communication technologies, supporting SDG 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization.

Introduction: Aligning Wireless Innovation with Sustainable Development Goals

The evolution towards 5G and 6G wireless networks relies heavily on Multiple-Input Multiple-Output (MIMO) technology to meet the demand for high spectral efficiency. However, the associated increase in hardware and power consumption directly conflicts with global sustainability objectives. The development of Green Communication techniques is therefore paramount to ensure that technological progress supports, rather than undermines, key SDGs.

• SDG 7 (Affordable and Clean Energy): The deployment of numerous radio frequency (RF) chains in massive MIMO systems leads to substantial energy use. Antenna selection is a promising strategy to mitigate this, enhancing the overall energy efficiency of the network.
• SDG 9 (Industry, Innovation, and Infrastructure): Building resilient and sustainable infrastructure requires innovative solutions that reduce the environmental footprint of technology. This research contributes by developing an intelligent system for resource management in wireless networks.
• SDG 12 (Responsible Consumption and Production): By optimizing energy use at the component level, this approach promotes more sustainable production and consumption patterns within the telecommunications industry.

Traditional optimization methods for antenna selection often suffer from high computational costs or suboptimal performance. Recent machine learning (ML) techniques have shown promise, yet challenges related to complexity and generalization persist. This report proposes a novel antenna selection strategy using Fast-ABC-Boost, a state-of-the-art multi-class classifier, to provide a robust and efficient solution for maximizing energy efficiency, thereby creating a tangible link between advanced wireless technology and sustainable development.

Key Contributions

1. The antenna selection problem is framed as a multi-class classification task, solved using the Fast-ABC-Boost algorithm to maximize energy efficiency, offering a practical alternative to complex optimization methods.
2. An innovative input feature based on the eigenvalues of the channel correlation matrix is designed to effectively capture system power consumption characteristics.
3. Extensive simulations validate the proposed scheme’s superiority over current ML (DRL) and traditional optimization (CBPSO) techniques in both EE performance and computational complexity, proving its value for sustainable network deployment.

System Model for Sustainable Communications

The study considers a downlink multi-user MIMO system designed for energy efficiency. The objective is to select an optimal subgroup of transmit antennas that maximizes the ratio of achievable data rate to total power consumption. This directly addresses the core challenge of balancing performance with sustainability.

System Parameters

• Base Station (BS): Equipped with a large number of antennas but a smaller number of RF chains to reduce power consumption.
• Energy Efficiency (EE) Metric: The primary performance indicator, defined as the total sum-rate (bits/sec) divided by the total power consumption (Watts). The power model includes transmit power, power consumed by active RF chains, and static circuit power.
• Optimization Goal: To identify the antenna subgroup that maximizes the EE metric under a given transmit power budget, contributing to the efficient use of energy resources as targeted by SDG 7.

Methodology: A Machine Learning Approach for Enhanced Energy Efficiency

To solve the antenna selection problem, this report leverages a sophisticated machine learning framework, Fast-ABC-Boost, which builds upon the principles of boosting algorithms. Boosting combines multiple “weak” classifiers (regression trees) to form a powerful “committee” for making highly accurate decisions. This approach is particularly suited for complex classification tasks where robustness and efficiency are critical for achieving sustainability targets.

Fast-ABC-Boost Framework

The Fast-ABC-Boost algorithm enhances the well-regarded ABC-Boost method by significantly reducing its computational complexity without sacrificing classification accuracy. This efficiency is crucial for real-world deployment in energy-conscious systems.

• It adaptively selects an optimal base class at each boosting step to minimize training loss.
• It employs a “warm-up” phase using the robust LogitBoost algorithm to establish a stable model foundation.
• It intelligently reduces the search space for the base class, striking an optimal balance between performance and computational cost, making it a sustainable choice for network operations.

Antenna Selection Process

The proposed strategy involves a three-stage process to integrate the Fast-ABC-Boost model into the MIMO system for sustainable operation:

1. Training Data Preparation:
   • Input features are generated from the largest eigenvalues of the channel correlation matrix, effectively representing the system’s power characteristics.
   • These features are paired with class labels (optimal antenna subgroups) determined via an exhaustive search based on the maximum EE criterion. This creates a high-quality dataset for training the classifier.
2. Model Training:
   • The Fast-ABC-Boost classifier is trained on the prepared dataset. The model learns the complex relationship between channel characteristics and the most energy-efficient antenna configuration.
3. Optimal Antenna Subgroup Selection:
   • During live operation, the trained model receives real-time channel information as input and swiftly predicts the optimal antenna subgroup that maximizes energy efficiency. This dynamic optimization ensures continuous alignment with SDG 12 by minimizing resource waste.

Performance Evaluation and Discussion

Simulation experiments were conducted to validate the effectiveness of the proposed Fast-ABC-Boost method. Its performance was benchmarked against a contemporary ML method (DRL) and a traditional optimization algorithm (CBPSO). The results unequivocally demonstrate the superiority of the proposed approach in advancing the goals of sustainable digital infrastructure.

Key Findings

• Superior Energy Efficiency: Across various transmit power levels, the Fast-ABC-Boost method consistently achieved higher EE than both DRL and CBPSO. This highlights its potential to significantly reduce the operational energy footprint of wireless networks, a direct contribution to SDG 7 and SDG 13.
• Optimal Resource Utilization: The analysis showed that selecting a subset of antennas (e.g., 4 or 6 out of 16) yields higher EE than using all available antennas. This confirms that intelligent resource management is crucial for sustainable performance, reinforcing the principles of SDG 12.
• Scalability and Robustness: The proposed method maintained its superior performance as the number of users in the system increased, demonstrating its robustness and suitability for dense, real-world deployment scenarios envisioned for future sustainable cities (SDG 11).
• Computational Efficiency: The Fast-ABC-Boost method exhibited a computational complexity comparable to CBPSO and was approximately 10 times lower than that of DRL. This low complexity makes it a practical and economically viable solution for green innovation in the telecommunications industry (SDG 9).

Conclusion and Future Outlook

This report has successfully demonstrated that the Fast-ABC-Boost algorithm provides a highly effective and computationally efficient solution for energy-efficient antenna selection in MIMO systems. By treating the problem as a multi-class classification task, the proposed method outperforms existing techniques, offering a clear path toward developing greener and more sustainable wireless communication networks.

The findings directly support the achievement of multiple Sustainable Development Goals by promoting energy efficiency (SDG 7), fostering sustainable innovation (SDG 9), enabling responsible resource consumption (SDG 12), and contributing to climate action (SDG 13). The Fast-ABC-Boost method stands out as a compelling technology for the next generation of wireless systems, where performance must be co-optimized with environmental responsibility. Future work will focus on extending this sustainable model to more complex multi-cell and multicasting scenarios.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article’s focus on developing energy-efficient technologies for wireless communication systems connects to several Sustainable Development Goals (SDGs). The primary connections are with goals related to energy, innovation, sustainable infrastructure, and climate action.

• SDG 7: Affordable and Clean Energy

  The central theme of the paper is the maximization of Energy Efficiency (EE). The introduction explicitly states that the high power consumption of MIMO systems “prompts the development of Green Communication techniques to enhance the energy efficiency (EE) of the system.” By developing a method that reduces the energy required to transmit data, the article directly contributes to the goal of more efficient energy use.

• SDG 9: Industry, Innovation, and Infrastructure

  The research presents an innovative technological solution (the Fast-ABC-Boost algorithm) to improve the infrastructure of 5G and 6G wireless networks. This work is a clear example of fostering innovation to build more resilient and sustainable infrastructure. The article aims to upgrade existing communication technology to be more resource-efficient, which is a core aspect of this goal.

• SDG 12: Responsible Consumption and Production

  This goal promotes the efficient use of natural resources. Energy is a critical resource, and the ICT sector is a significant consumer. The proposed antenna selection technique is designed to ensure that energy consumption in wireless networks is minimized, aligning with the principle of achieving more with less and promoting sustainable consumption patterns within the technology industry.

• SDG 13: Climate Action

  By reducing the power consumption of wireless base stations, the technology described in the article helps to lower the overall carbon footprint of the telecommunications industry. Since a significant portion of electricity is generated from fossil fuels, a reduction in energy consumption directly translates to a reduction in greenhouse gas emissions, thereby contributing to climate change mitigation efforts.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s content, the following specific SDG targets can be identified:

1. Target 7.3: By 2030, double the global rate of improvement in energy efficiency.

   The entire paper is dedicated to this target. The objective is to find an “antenna selection technique for energy efficiency (EE) maximization.” The proposed Fast-ABC-Boost method is evaluated against other techniques precisely on its ability to improve EE, as shown in the “Results and discussion” section, directly addressing the need for improved energy efficiency in technology.

2. Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes.

   The article proposes a new, more energy-efficient method for antenna selection in 5G and 6G MIMO systems. This represents an upgrade to communication infrastructure that increases its sustainability and resource-use efficiency (specifically, energy). The development of “Green Communication techniques” is a direct effort to create and adopt more environmentally sound technologies in the telecommunications industry.

3. Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation.

   This article is a product of scientific research that aims to advance the technological capabilities of wireless communication systems. By proposing a novel machine learning-based approach (“this is the first work in literature to conduct antenna selection via Fast-ABC-Boost”), it contributes to the body of knowledge and encourages innovation in the field.

4. Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources.

   The research focuses on minimizing the power consumed by wireless systems, which is a direct contribution to the efficient use of energy resources. The problem is formulated to “search for an EE maximization subgroup” of antennas, which inherently involves optimizing the use of power to achieve the required communication performance.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article explicitly defines and uses several quantitative indicators to measure performance, which can be used to track progress towards the identified targets.

• Energy Efficiency (EE)

  This is the primary indicator used throughout the paper. It is explicitly defined in Equation (11) as the ratio of the system’s sum-rate (R) to the total power consumption (`EE = R / (Px + |sk|Pc + Ps)`). The simulation results presented in Figures 3, 4, and 5 directly plot EE values to demonstrate the superiority of the proposed method. This indicator directly measures progress for Target 7.3 and 12.2.

• Power Consumption

  While not plotted as a standalone metric, power consumption is a key component of the EE calculation and the core problem the article addresses. The introduction mentions the goal of reducing “substantial hardware cost and power consumption.” The components of power consumption are detailed in Equation (11), including transmit power (`Px`), power per RF chain (`Pc`), and static power (`Ps`). Reducing this value is an implied indicator of success for Target 9.4 and 13.2.

• Computational Complexity

  The article explicitly analyzes and compares the computational complexity of the proposed method against others. Table 2, titled “Complexity comparison,” provides the order of complexity for Fast-ABC-Boost, DRL, and CBPSO. The paper notes that “Fast-ABC-Boost is the most efficient method to maximize EE among the three candidates.” This serves as an indicator of resource efficiency, relevant to Target 9.4, as lower complexity means less processing power and energy are needed for the control mechanism itself.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com