Utility-Based Renewable Energy Allocation Policy for Green Cellular Networks

Table of Contents

1. Introduction

The explosive growth in wireless data demand has led to significant increases in energy consumption and carbon emissions from cellular networks. This paper addresses the challenge of powering these networks with renewable energy sources (e.g., solar, wind), which are inherently intermittent and uneven. The core problem is efficiently allocating a finite amount of harvested renewable energy among users in an Orthogonal Frequency Division Multiple Access (OFDMA) cellular network. The proposed policy uniquely integrates three key factors: total available renewable energy, individual user Quality of Service (QoS) requirements, and real-time channel quality. The objective is to maximize a network-wide utility function, which quantifies user satisfaction, subject to energy constraints. This work positions itself within the "green communication" paradigm, moving beyond pure energy efficiency to intelligent resource management for sustainability.

2. System Model and Problem Formulation

2.1 Network and Energy Model

We consider a single-cell OFDMA network with one base station (BS) powered by a hybrid energy source: the traditional grid and an on-site renewable energy harvester (e.g., solar panels). The BS serves K users. The renewable energy arrives intermittently and is stored in a finite-capacity battery. The available renewable energy for allocation in a given time slot is denoted as $E_{total}$. The channel gain for user $k$ is $h_k$, which is time-varying.

2.2 Utility Function and QoS

The cornerstone of the policy is the utility function $U_k(e_k)$, which maps the amount of renewable energy $e_k$ allocated to user $k$ to a measure of that user's satisfaction. This function is designed to reflect the user's QoS requirement. For example, a delay-sensitive user (e.g., video streaming) might have a sharply increasing utility that saturates quickly, while a best-effort user (e.g., file download) might have a more linear utility. The aggregate network utility is $U_{sum} = \sum_{k=1}^{K} U_k(e_k)$.

2.3 Optimization Problem

The energy allocation problem is formulated as a constrained optimization problem: $$\max_{\{e_k\}} \sum_{k=1}^{K} U_k(e_k)$$ Subject to: $$\sum_{k=1}^{K} e_k \leq E_{total}$$ $$e_k \geq 0, \quad \forall k \in \{1,...,K\}$$ $$R_k(e_k, h_k) \geq R_{k}^{min}, \quad \forall k$$ where $R_k$ is the achievable data rate for user $k$ (a function of allocated energy $e_k$ and channel gain $h_k$), and $R_{k}^{min}$ is the minimum rate required to meet its QoS.

3. Proposed Energy Allocation Algorithm

3.1 Heuristic Algorithm Design

Given the non-convex and combinatorial nature of the problem (especially with discrete subcarrier allocation in OFDMA), the authors propose a low-complexity heuristic algorithm. The algorithm operates in a greedy-like manner:

1. User Prioritization: Users are ranked based on a composite metric combining their channel quality ($h_k$) and the marginal utility gain per unit energy ($\Delta U_k / \Delta e_k$).
2. Iterative Allocation: Starting with the highest-priority user, energy is allocated in discrete steps until their utility gain diminishes or their QoS is satisfied.
3. Constraint Check: After each allocation, the total energy constraint $E_{total}$ is checked. If energy remains, the process continues with the next user.
4. Termination: The algorithm stops when $E_{total}$ is exhausted or all users have been served.

This approach ensures that under scarce energy conditions, users with excellent channel conditions (high energy efficiency) are served first to maximize overall utility.

3.2 Algorithm Complexity

The algorithm's complexity is $O(K \log K)$ due to the initial sorting of K users, followed by a linear allocation pass. This makes it highly scalable and suitable for real-time implementation in network controllers, contrasting with complex dynamic programming or convex optimization solutions proposed in related works like [8].

4. Numerical Results and Performance Evaluation

4.1 Simulation Setup

The performance is evaluated via simulation. Key parameters include: a cell radius of 500m, 20-50 users randomly distributed, Rayleigh fading channels, and varying levels of total renewable energy $E_{total}$. The utility functions are defined as sigmoidal for real-time traffic and logarithmic for best-effort traffic, aligning with models used in networking economics.

4.2 Results Analysis

The results demonstrate two key behaviors:

1. Scarce Energy Regime: When $E_{total}$ is very low, the algorithm allocates energy almost exclusively to users with the best channel gains. This sacrifices fairness but maximizes total utility and network efficiency, as serving users with poor channels would waste precious energy.
2. Adequate Energy Regime: As $E_{total}$ increases, the algorithm begins to satisfy the QoS demands of more users, including those with moderate channel quality. The aggregate utility increases and saturates once all users' core QoS needs are met.

The proposed policy is shown to outperform a baseline equal-energy allocation scheme significantly in terms of total utility, especially in energy-scarce scenarios. A key chart would plot Total Network Utility vs. Total Available Renewable Energy, comparing the proposed heuristic against the equal allocation baseline and a theoretical upper bound.

5. Core Insight & Analyst's Perspective

Core Insight: This paper's fundamental contribution is reframing renewable energy allocation from a pure throughput-maximization problem to a utility-driven, QoS-aware resource economics problem. It acknowledges that in a green network, energy isn't just a cost but the primary scarce commodity. The real innovation is tying allocation directly to user-perceived satisfaction (utility) modulated by physical reality (channel state), creating a more holistic and pragmatic control lever for network operators.

Logical Flow: The argument is sound: 1) Renewable energy is finite and intermittent. 2) User demands are heterogeneous. 3) Therefore, intelligent allocation that considers both supply (energy, channel) and demand (QoS) is necessary. 4) A utility function elegantly quantifies the trade-off. 5) A low-complexity heuristic makes it practical. The flow from problem definition to solution is coherent and addresses a clear gap in prior work which often ignored diverse QoS requirements, as the authors correctly point out.

Strengths & Flaws: Strengths: The integration of utility theory is powerful and borrows well from network economics. The heuristic is pragmatic—it accepts that in real-time network control, a good, fast solution is better than a perfect, slow one. The focus on QoS differentiation is critical for modern networks laden with IoT, video, and mission-critical traffic. Flaws: The model is somewhat simplistic. It assumes a single cell, ignoring the potential for energy cooperation between cells via smart grids—a promising area explored by others like Zhou et al. in "Energy Cooperation in Cellular Networks with Renewable Powered Base Stations" (IEEE Transactions on Wireless Communications). The utility functions are assumed known; in reality, defining and learning these functions per service type is a non-trivial challenge. The paper also lacks a robust fairness analysis; the "starve the weak-channel users" strategy under scarcity could be problematic for service-level agreements.

Actionable Insights: For telecom operators, this research provides a blueprint for the software-defined energy controller that will be essential in 5G-Advanced and 6G networks. The immediate step is to prototype this algorithm in a testbed with real solar/wind data. Furthermore, operators should start categorizing their traffic into utility classes. For researchers, the next steps are clear: 1) Incorporate multi-cell coordination and energy sharing. 2) Integrate machine learning to dynamically learn utility functions from user experience data. 3) Expand the model to include energy storage degradation costs. This work, akin to the foundational shift brought by "cycleGAN" in image-to-image translation by introducing cycle consistency, introduces a consistent framework (utility + constraints) for a new class of green resource allocation problems.

6. Technical Details and Mathematical Formulation

The core optimization is defined in section 2.3. The achievable rate $R_k$ for a user on an OFDMA subcarrier is typically given by: $$R_k = B \log_2 \left(1 + \frac{e_k \cdot h_k}{N_0 B}\right)$$ where $B$ is the bandwidth of a resource block, and $N_0$ is the noise spectral density. The utility function for a delay-constrained service can be modeled as a sigmoidal function: $$U_k(e_k) = \frac{1}{1 + \exp(-a(R_k(e_k) - b))}$$ where parameters $a$ and $b$ control the steepness and center of the function, reflecting the QoS threshold. For elastic traffic, a concave logarithmic function $U_k(e_k) = \ln(1 + R_k(e_k))$ is often used.

7. Analysis Framework: Example Case

Scenario: A base station has 5 users and $E_{total} = 10$ units of renewable energy.

• User 1 (Video Call): QoS: $R_{min}=2$ Mbps, Channel: Excellent ($h_1$ high), Utility: Sigmoidal.
• User 2 (File Download): QoS: None, Channel: Good, Utility: Logarithmic.
• User 3 (IoT Sensor): QoS: $R_{min}=0.1$ Mbps, Channel: Poor ($h_3$ low), Utility: Step-like.
• Users 4 & 5: Similar mixed profiles.

Algorithm Execution:

1. Calculate priority score for each user (e.g., $h_k \times (\text{marginal utility})$).
2. Sort users: Let's say order is User1, User2, User4, User5, User3.
3. Allocate to User1 until its video QoS is met (cost: 3 units). Utility jumps high.
4. Allocate to User2. Each unit gives a decent utility gain. Allocate 4 units.
5. Remaining energy = 3 units. Allocate to User4 to partially meet its need (cost: 3 units).
6. Energy exhausted. Users 5 and 3 (with poor channel) get zero allocation.

Outcome: Total utility is maximized by satisfying the high-priority, efficient users first. User3 is starved—this is the policy's explicit trade-off under scarcity.

8. Application Outlook and Future Directions

Short-term (1-3 years): Integration into network energy management systems (EMS) for macro and micro base stations. This is particularly relevant for off-grid or rural deployments powered primarily by renewables, as documented in projects by the GSM Association's "Green Power for Mobile" program.

Mid-term (3-5 years): Central to 6G vision of integrated sensing, communication, and energy. Networks will not only consume energy but also manage and distribute it. This algorithm could evolve to control wireless power transfer to IoT devices or manage vehicle-to-grid (V2G) energy flows from mobile network infrastructure.

Future Research Directions:

• AI/ML Integration: Using deep reinforcement learning (DRL) to learn optimal allocation policies in highly dynamic environments without pre-defined utility models.
• Multi-Resource Joint Allocation: Jointly optimizing spectrum, time, and energy resources in a unified framework.
• Market-Based Mechanisms: Implementing a real-time energy market within the network where users/agents bid for renewable energy based on their needs, inspired by blockchain-based microgrid concepts.
• Standardization: Pushing for standardization of energy-aware control interfaces in Open RAN (O-RAN) architectures, allowing third-party energy management applications (xApps).

The convergence of communication networks and energy grids, often called the "Energy Internet," will make such algorithms indispensable.

9. References

1. International Energy Agency (IEA). "Data Centres and Data Transmission Networks." IEA Reports, 2022. [Online]. Available: https://www.iea.org/reports/data-centres-and-data-transmission-networks
2. Z. Zhou et al., "Energy Cooperation in Cellular Networks with Renewable Powered Base Stations," IEEE Transactions on Wireless Communications, vol. 13, no. 12, pp. 6996-7010, Dec. 2014.
3. GSMA. "Green Power for Mobile: The Global M2M Association on Sustainability." GSMA, 2021.
4. O. Ozel et al., "Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies," IEEE Journal on Selected Areas in Communications, vol. 29, no. 8, pp. 1732-1743, Sept. 2011. (Cited as [8] in PDF)
5. J. Zhu et al., "Toward a 6G AI-Native Air Interface," IEEE Communications Magazine, vol. 61, no. 5, pp. 50-56, May 2023.
6. J.-Y. Zhu, T. Park, P. Isola, A. A. Efros. "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks." IEEE International Conference on Computer Vision (ICCV), 2017. (Cited as an example of a foundational framework shift).