Machine Learning for Energy Efficiency in SWIPT-NOMA Networks

Introduction

In modern wireless systems, devices need both data and power—but batteries drain fast. Simultaneous Wireless Information and Power Transfer (SWIPT) lets devices harvest energy from radio signals while receiving data. When combined with Non-Orthogonal Multiple Access (NOMA), networks can serve more users with higher efficiency. Yet balancing energy harvesting and reliable data transfer is tricky. This is where machine learning shines. By analyzing patterns and making smart decisions in real time, machine learning can optimize energy efficiency in SWIPT-NOMA networks, ensuring users get the power and information they need. Let’s explore how these techniques work and why they matter.

Understanding SWIPT-NOMA Networks

What Is SWIPT?

SWIPT stands for Simultaneous Wireless Information and Power Transfer. Instead of sending only data, transmitters also send energy. Special circuits in devices split the incoming signal: one part powers the device’s battery, and the other decodes data. This dual role can extend battery life in sensors, IoT devices, and remote gadgets.

Key Benefits and Challenges

• Benefits:
  • Extends device lifetime
  • Reduces need for battery replacements
  • Enables IoT in hard-to-reach areas
• Challenges:
  • Trade-off between harvested energy and data rate
  • Complex hardware design
  • Interference management

What Is NOMA?

Non-Orthogonal Multiple Access (NOMA) allows multiple users to share the same frequency band by superimposing their signals at different power levels. A strong user decodes and strips away the weaker signal before decoding its own. This superposition coding increases spectral efficiency and user capacity.

NOMA Advantages

• Supports more users per channel
• Improves fairness by giving weaker users more power
• Enhances throughput in crowded networks

Energy Efficiency in SWIPT-NOMA Networks

The Dual Goal: Harvest Energy and Decode Data

In a SWIPT-NOMA system, each user must harvest enough energy to stay powered while also decoding its information accurately. The network must allocate power smartly: too much power for harvesting reduces data rate; too little leads to energy shortages.

Power Splitting and Time Switching

Two main strategies help balance harvesting and data decoding:

1. Power Splitting: Each receiver splits the incoming signal by a ratio ρ (rho). A fraction ρ goes to the energy harvester; the rest, 1–ρ, goes to the data decoder.
2. Time Switching: The receiver alternates between harvesting (for τ seconds) and decoding (for T–τ seconds).

Choosing optimal ρ and τ is crucial for overall efficiency.

Key Performance Metrics

• Energy Harvested (EH): Amount of power collected per time unit.
• Data Rate (R): Bits transmitted successfully per second.
• Energy Efficiency (EE): Ratio of data rate to power consumed (e.g., bits per joule).

An effective system maximizes EE, ensuring high data rates without draining resources.

Machine Learning for Energy Optimization

Why Use Machine Learning?

Traditional optimization relies on solving complex equations in real time, which can be slow for dynamic wireless channels. Machine learning (ML) offers:

• Fast Inference: Trained models make quick decisions.
• Adaptability: Models learn from changing channel conditions and user demands.
• Scalability: Easily handles many users and diverse scenarios.

ML Techniques in SWIPT-NOMA

1. Reinforcement Learning (RL):
   • Agents (base stations) learn optimal policies by trial and error.
   • Reward signals combine data rate and harvested energy metrics.
   • Over time, the RL agent converges to power splitting and allocation policies that maximize EE.
2. Deep Neural Networks (DNNs):
   • Supervised learning models predict optimal power-splitting ratios ρ and user power levels from channel state information (CSI).
   • Training data comes from simulated optimal solutions or past network operations.
   • Once trained, DNNs output near-optimal solutions in milliseconds.
3. Federated Learning:
   • Decentralized ML where each user equipment (UE) trains a local model on its own data.
   • Models aggregate at the base station, enhancing privacy and reducing uplink overhead.
   • Useful when user data (e.g., energy needs, channel stats) is sensitive.

Energy-Aware Resource Allocation

Dynamic Power Allocation

Machine learning models help assign transmit power levels to each NOMA user:

• Strong users may need less power for data but can help relay energy to weaker users.
• Weaker users receive more power to maintain fairness and sufficient EH.

By learning from past allocations and outcomes, ML-based schedulers balance this trade-off more efficiently than static rules.

Optimizing Power Splitting

Choosing ρ per user and per time slot ensures the best split between energy harvesting and data decoding:

• An RL agent receives channel gains and battery levels, then selects ρ to maximize a reward function combining EH and R.
• Over iterations, the agent learns when to favor energy (low battery) or data (urgent packets).

Joint User Pairing and Clustering

In NOMA, pairing users with complementary channel conditions boosts performance:

• ML clustering algorithms group users based on channel similarity and energy demands.
• The scheduler then applies power allocation within each cluster to optimize EE.

Performance Evaluation and Case Studies

Simulation Metrics

Researchers evaluate ML’s impact on SWIPT-NOMA networks using:

• Average EE Gain: Percentage improvement over traditional schemes.
• Convergence Time: How quickly RL agents learn good policies.
• Robustness: Performance under sudden channel fades or user mobility.

Sample Study: RL-Based Power Splitting

A study applied Q-learning to a two-user SWIPT-NOMA model:

• Agents achieved 20% higher EE compared to fixed ρ schemes.
• The policy adapted within 100 episodes, maintaining stable performance under varying SNR levels.

Sample Study: DNN for Power Allocation

Supervised DNNs trained on optimal offline solutions showed:

• Over 95% of cases had within 5% of the optimal EE.
• Decision times under 1 ms, suitable for fast‐changing channels.

These results demonstrate that ML methods can approach theoretical optima with practical speed.

Challenges and Future Directions

Data Collection and Labeling

• Challenge: Gathering high-quality training data for DNNs requires running complex solvers offline.
• Solution: Use transfer learning to adapt models trained in simulations to real‐world measurements.

Explainability and Trust

• Challenge: Network operators need to trust ML decisions.
• Solution: Develop explainable AI techniques that clarify why certain power splits or user pairings were chosen.

Integration with 5G and Beyond

• Challenge: Future networks demand ultra-low latency and massive connectivity.
• Opportunity: Edge AI can run ML models close to users, reducing delay and handling localized optimization.

Joint Optimization with Other Layers

• Next Steps: Combine physical-layer ML with higher-layer resource management (e.g., scheduling, routing) for end-to-end EE gains.

Conclusion

Machine learning offers powerful tools for enhancing energy efficiency in SWIPT-NOMA networks. By applying reinforcement learning, deep neural networks, and federated learning, systems can dynamically adjust power allocation, optimize power splitting, and intelligently pair users to balance energy harvesting and data decoding. Simulation studies highlight EE gains of up to 20% and near‐optimal performance with millisecond decision times. Though challenges in data collection and model explainability remain, future research in edge AI and joint-layer optimization promises further breakthroughs. As wireless networks evolve toward 6G and beyond, ML-driven energy-aware resource allocation will be vital for sustainable, high-performance communications.