Energy-Aware 6G Networks

• Energy-Aware 6G Networks are wireless architectures that optimize energy consumption through AI-driven sensing, analytics, and control to support green connectivity.
• They employ layered designs that combine intelligent sensing, deep learning, and dynamic resource allocation for real-time energy management.
• Advanced techniques including reinforcement learning, semantic communications, and adaptive hardware protocols enable significant energy savings and operational scalability.

Energy-aware 6G networks are architected to optimize energy consumption across all layers of wireless communication infrastructure, leveraging AI, heterogeneous access paradigms, advanced optimization techniques, and real-time context-awareness. The focus is to deliver green and sustainable connectivity for massively diverse and demanding applications, including the Internet of Everything (IoE), immersive reality, and autonomous systems, while respecting practical constraints on device energy, network resource efficiency, and operational scalability.

1. Shifting Paradigms in Energy-Aware 6G Design

Energy-aware 6G system design diverges from previous generations by embedding energy management as a primary architectural principle, not merely as an afterthought or a trade-off with performance. At the foundational level, a layered AI-driven architecture is proposed, comprising:

• Intelligent Sensing Layer: Collects real-time environmental and spectrum usage data with the goal of minimizing redundant sensing operations, thus curbing unnecessary sensor and network activity (Yang et al., 2019).
• Data Mining and Analytics Layer: Applies methods such as Principal Component Analysis (PCA) and ISOMAP for feature reduction and anomaly detection, reducing datastream size and the energy required for both computation and transport.
• Intelligent Control Layer: Integrates deep learning, reinforcement learning (RL), and game-theoretical methods for network parameter optimization (e.g., dynamic beamforming, transmission power control), supporting continuous adaptation to minimize energy consumption while maintaining service quality.
• Smart Application Layer: Provides feedback on service-level metrics (energy efficiency, latency, throughput), closing the loop with underlying layers to adjust resource allocation and system behavior (Yang et al., 2019).

These architectural advances are reconceptualized at the system scale in full-spectrum, cell-free, airborne, and non-terrestrial architectures, supporting energy-harvesting, wireless power transfer (WPT), and passive backscatter communication to realize self-sustaining operations for devices beyond the reach of wired power sources (Hu et al., 2020).

2. Advanced Energy Management Techniques

AI and ML are central to advanced energy management across several axes:

• Deep/Reinforcement Learning for Resource Adaptation: Recurrent neural networks (RNNs) capture RF nonlinearities (notably in PA arrays) for optimizing transmit power in massive MIMO without performance loss; RL agents deployed at mobile edge computing (MEC) nodes and in UAV mobility controllers dynamically allocate resources and schedule handovers to minimize energy cost under real-time constraints (Yang et al., 2019).
• Spectrum and Network Slicing via Deep Learning: Large-scale neural models (e.g., deep CNNs) enable intelligent spectrum sensing, facilitating adaptive allocation of spectrum and hardware resources in a way that precisely matches energy profiles to application needs (Yang et al., 2019).
• Semantic Communications: AI-based semantic extraction and reconstruction using autoencoders and classifiers ensure that only the essential “meaningful” parts of data are transmitted, yielding substantial energy savings for edge and core processing, especially for high-volume media streams (Saadat et al., 29 Jan 2024).
• Network Slicing Optimization with ML/NLP: ML-native agents and contrastive learning techniques orchestrate slice construction, real-time reallocation, and anomaly detection to prevent over-provisioning and address emergent inefficiencies dynamically (Moreira et al., 17 May 2025).
• Energy-Aware Load Balancing/Cell Switching: Both lightweight (Q-learning) and deep RL schemes are shown to achieve significant energy reductions through optimized cell activations, antenna tilt, and transmit power control in terrestrial and non-terrestrial environments, maintaining QoS for heterogeneous user sets (Koç et al., 20 Feb 2024, Tran et al., 20 Aug 2024).

3. Energy-Aware Network Architectures and Technologies

Recent research establishes several novel architectural building blocks and technologies for energy-aware operation:

• Cell-Free/Airborne Access and Holographic Environments: Distributed units (DUs), coordinated by AI-enabled control units, and intelligent holographic surfaces (IRS/ RIS) dynamically steer beams and control propagation environments with minimal radiative loss (Hu et al., 2020, Yang et al., 2021).
• High Altitude Platform Stations (HAPS): HAPS serve as super macro base stations, supporting sleep modes for terrestrial BSs; simulation shows up to 41% energy savings during off-peak periods and a strong correlation with spatial deployment and user indoor ratios (Song et al., 2023).
• Disaggregated Fog/Edge Computing: Server disaggregation (DS) enables finer granularity over resource activation, with up to 18% reduction in fog computing power consumption relative to monolithic server architectures. Intelligent application placement using MILP/HEEDAP or RL further improves energy-delay trade-offs (Ajibola et al., 2021).
• Integration of Terrestrial/Non-Terrestrial Networks: Multi-tier cell-switching frameworks combine TN, UAVs, HAPS, and satellites, enabling dynamic traffic offloading and context-aware choice of network layer to achieve both energy and latency targets (Ozturk et al., 14 Aug 2025).
• Network-Controlled Repeaters (NCRs): By introducing low-power NCRs with decoupled backhaul and access links, significant gNB power reductions are realized, yielding up to 2.1-fold EE gains in select coverage regions while maintaining SE (Azzino et al., 27 Sep 2024).

Technology/Architecture	Energy Efficiency Mechanism	Quantitative Results
HAPS (as SMBS)	Traffic offload/enabling BS sleep modes	Up to 41% energy savings (night), 29% (avg. week) (Song et al., 2023)
DS Fog Computing	Server component pooling/consolidation	Up to 18% TFPC reduction; RTT increase marginal (Ajibola et al., 2021)
Semantic Communications	Semantic extraction, joint user-edge opt.	Notable total energy decrease over baselines (Saadat et al., 29 Jan 2024)
NCR-Enabled Topologies	gNB/NCR coordinated parameter optimization	30–60% EE improvement (particular topologies) (Azzino et al., 27 Sep 2024)

Quantitative metrics as reported; values context-dependent and subject to scenario assumptions described in each primary source.

4. Key Models, Metrics, and Optimization Formulations

Energy-aware 6G research leverages a diverse set of metrics and optimization frameworks:

• Energy Efficiency (EE) is formally $EE = \frac{R}{P}$, where $R$ is the achieved data rate and $P$ is total consumed power (Yang et al., 2019).
• Integrated Relative Energy Efficiency (IREE) incorporates both capacity and spatial traffic matching:

$$\eta_{IREE} = \frac{\min\{C_{Tot},D_{Tot}\}\,[1-\xi(C_T, D_T)]}{P_T}$$

where $C_{Tot}$ and $D_{Tot}$ are total capacity and demand, and $\xi(C_T, D_T)$ is the JS divergence between offered capacity and spatial traffic patterns, enforcing fairness (Yu et al., 21 May 2024).

• Emissions-Per-Bit as a sustainability metric:

$$C^\beta_k(t) = \frac{c_k(t)}{T \cdot \sum_{u \in \mathcal{M}_k} R_u(t)}$$

explicitly capturing the conversion from resource consumption to environmental footprint (Thomas et al., 2 Sep 2025).

• Multi-Objective Optimization Problems coordinate energy, delay, and emission targets through mixed-integer nonlinear programs, multi-objective RL (MORL), and alternating policy improvement—embedding constraints on battery, grid, and renewably sourced energy (Thomas et al., 2 Sep 2025, Wang et al., 29 Apr 2024).
• Practical Heuristics (e.g., HEEDAP) and relaxation/convexification methods enabling scalable resource allocation under strict energy-delay constraints (Ajibola et al., 2021, Saadat et al., 29 Jan 2024).

5. AI/ML Enhancements and Operational Automation

AI and ML are extensively integrated throughout 6G energy-aware ecosystems:

• Deep/Double Dueling Q-Learning, GNN State Encoding: Address complex, high-dimensional resource allocation challenges by modeling spatio-temporal dependencies in large-scale networks (Shokrnezhad et al., 10 Feb 2024).
• Federated Multi-Agent DRL: Decentralized learning (e.g., DRQN agents) operates under privacy and communication constraints, enabling energy-aware task offloading, spectrum access, and CPU scaling with provable gains over centralized or heuristic policies in ultra-dense edge scenarios (Andong et al., 12 Sep 2025).
• Contrastive and Anomaly Learning for Slicing: CL discriminates normal and abnormal resource usage patterns, informing dynamic orchestration and anomaly mitigation during in-service operation of network slices (Moreira et al., 17 May 2025).
• Intent-Driven Network Management with RL: High-level service intents (formulated in standard templates) are decomposed into actionable optimization tasks, with DQN-based automation for real-time trade-offs among energy, throughput, and latency (Wang et al., 29 Apr 2024).

6. System-Level Strategies and Sustainable Operation

Sustainable 6G deployment requires cross-layer strategies that combine technological, architectural, and operational perspectives:

• Energy-Information Function Exposure and Utilization: Real-time monitoring (EIFs) of per-BS and per-slice energy enables network controllers to allocate resources on a basis of renewable supply or user-configurable energy profiles, with service-level classification (e.g., “Super Green”, “Red”) guiding trade-offs (Kamran et al., 14 Sep 2025).
• Dynamic Hardware Adaptation: Enhanced DTX/DRX and deep sleep protocols are dynamically managed by AI models, based on load and predicted traffic, to further extract energy savings (Kamran et al., 14 Sep 2025).
• Standardization and Interoperability: Multiple international bodies (3GPP, ITU, IEEE) are driving standards for energy metrics, exposure, and architecture, including EIFs, renewable energy integration, slice-aware control, and ML system energy rating (Kamran et al., 14 Sep 2025).

System Strategy	Mechanism	Standard/Reference
EIF-based Allocation	Scheduling on renewable-heavy BSs	3GPP, IEEE (Kamran et al., 14 Sep 2025)
Hardware Sleep States	Adaptive DTX/DRX, deep sleep	ITU, 3GPP (Kamran et al., 14 Sep 2025)
Service-Aware Security	Context-sensitive, energy-optimized auth.	IEEE P1959 (Kamran et al., 14 Sep 2025)

7. Future Directions and Open Challenges

Despite significant progress, several unresolved issues remain:

• Scalable Real-Time Energy Prediction: Accurately forecasting renewable generation and energy availability at fine time and spatial scales remains difficult (Kamran et al., 14 Sep 2025).
• Computation/Overhead Management: AI/ML methods must be energy-conscious themselves; excessive computation for optimization can erode intended energy savings—especially relevant for edge and battery-powered nodes (Yang et al., 2019, Kamran et al., 14 Sep 2025).
• User Consent and Quality Tuning: Mechanisms for dynamic user consent management—in adjusting QoS to meet energy budgets—must be supported in a transparent and privacy-preserving manner (Kamran et al., 14 Sep 2025).
• Standardization and Integration: Harmonizing energy models, metric definitions, and control procedures across diverse vendors and international standards to support interoperability, efficiency, and scalability presents an ongoing challenge (Kamran et al., 14 Sep 2025).

Overall, the evolution towards energy-aware 6G networks is characterized by holistic, multi-layered, and context-driven design principles that align energy efficiency with the imperatives of sustainability, adaptability, and service quality. Explicit integration of AI-managed architectures, dynamic adaptation mechanisms, and cross-layer optimization methodologies is essential for meeting the unprecedented energy and sustainability challenges of the 6G era.