Introduction

Modern wireless communication is evolving rapidly. New demands for higher speed, better efficiency, and lower power usage are pushing the boundaries of network design. One of the most promising technologies at the forefront of this change is the combination of SWIPT (Simultaneous Wireless Information and Power Transfer) and NOMA (Non-Orthogonal Multiple Access). Together, they create a system that can transmit data and energy at the same time to multiple users sharing the same spectrum.

While this combination is powerful, it introduces a major challenge: how to efficiently manage resources. That’s where game theory steps in. This mathematical approach can help solve complex problems involving multiple players (or users) competing for limited resources. In this article, we’ll explore the basics of SWIPT-NOMA networks, the resource allocation challenges they face, and how game theory can offer solutions for optimal performance.

Understanding SWIPT-NOMA Networks

Before diving into resource allocation strategies, let’s understand what SWIPT and NOMA are and how they work together.

SWIPT is a technique that allows devices to harvest energy from radio frequency (RF) signals while simultaneously receiving data. This is especially useful for small, low-power devices like sensors or smart gadgets in the Internet of Things (IoT). It helps reduce the need for batteries or frequent charging.

NOMA, on the other hand, allows multiple users to share the same frequency-time resources by assigning different power levels. Instead of separating users into different channels, NOMA lets them overlap. A receiver uses a method called Successive Interference Cancellation (SIC) to separate the data streams.

When combined, SWIPT and NOMA create a network where:

• Multiple users share the same frequency
• Some users harvest energy from the RF signals
• Others decode data directly or perform both actions

This setup leads to higher spectrum efficiency and supports a large number of devices. However, it also complicates how resources like power and bandwidth should be allocated.

Resource Allocation Challenges

Optimizing SWIPT-NOMA networks means solving several difficult challenges:

1. Power Allocation:
Each user requires a different amount of power depending on its distance from the base station and whether it needs to harvest energy or decode data. Allocating just the right power levels to each user is critical. Too little power might result in data loss, while too much could waste energy.

2. User Scheduling:
In a real network, many users compete to use the same resources. Scheduling which users are active at any given time is a balancing act. It must consider channel conditions, energy needs, and fairness.

3. Interference Management:
With multiple users sharing the same channel, interference becomes a serious problem. Especially in NOMA, users with weaker channels are affected more. Managing interference without degrading the signal quality is essential.

4. Energy Harvesting Constraints:
For users that rely on energy harvesting, the amount of energy received must be enough to power their operations. Ensuring that these energy needs are met while still supporting strong data rates is a tricky balancing act.

These challenges are interlinked, and improving one aspect might worsen another. That’s why smart and adaptive solutions are needed—this is where game theory becomes a powerful tool.

Game Theory in Resource Allocation

Game theory is a branch of mathematics that studies strategic interactions between players. In wireless networks, players can be base stations, users, or even network slices. Each player wants to optimize their own performance, such as maximizing data rate or energy efficiency.

There are several types of games used in SWIPT-NOMA networks:

1. Non-Cooperative Games:
Each user acts independently, trying to optimize its own payoff (for example, higher data rate or more harvested energy). The solution is a Nash Equilibrium, where no user can improve their outcome by changing their strategy alone. These games help design algorithms where users adjust power levels without central coordination.

2. Stackelberg Games:
These involve a leader (usually the base station) and multiple followers (users). The leader moves first, setting power levels or prices, and the users respond. This model is useful for managing energy pricing or resource auctions in SWIPT-NOMA systems.

3. Cooperative Games:
Here, users form coalitions and share resources to improve overall outcomes. These games work well in distributed networks where users can cooperate to improve energy harvesting or reduce interference.

4. Repeated Games and Learning-Based Models:
In dynamic environments, strategies may evolve over time. Repeated game models and machine learning techniques can help networks adapt to changing conditions, such as user mobility or varying channel quality.

Applications of Game Theory

Game theory can be applied in many ways to optimize SWIPT-NOMA networks:

Power Control:
Using non-cooperative games, each user adjusts its power level based on channel feedback. Over time, users reach a stable power distribution that avoids excessive interference and maximizes overall efficiency.

User Pairing and Scheduling:
Game-theoretic models can help group users with complementary channel conditions. For example, a strong user can be paired with a weak one in NOMA, ensuring fairness and higher data rates. Scheduling decisions can also be made dynamically using Stackelberg games or auction-based models.

Energy Pricing and Trading:
In networks where users harvest and share energy, Stackelberg games allow the network operator to set energy prices. Users then decide how much to harvest or trade, creating a balanced energy distribution system.

Interference Mitigation:
Cooperative games can help users agree on transmission patterns that minimize interference. This is particularly useful in dense environments or when multiple cells overlap.

Adaptive Resource Management:
Learning-based games or reinforcement learning methods can be combined with game theory to handle unpredictable environments. The network “learns” from past interactions and adjusts strategies accordingly.

Challenges and Future Directions

While game theory offers powerful tools, there are still challenges in real-world applications:

Complexity:
Many game-theoretic models require heavy computation, which may not be suitable for real-time applications or low-power devices.

Scalability:
As the number of users grows, the size of the game increases, making it harder to find fast and stable solutions.

Incomplete Information:
In practice, users may not have perfect knowledge of the network or other users’ strategies. Dealing with partial information requires advanced learning and prediction techniques.

Security and Fairness:
Game models need to be designed with fairness and security in mind to prevent selfish behavior or malicious attacks.

Future Directions Include:

• Using deep learning to enhance decision-making in game-based models
• Developing lightweight algorithms for low-power IoT devices
• Creating hybrid frameworks combining game theory with AI and optimization tools
• Testing in real-world testbeds to validate the practicality of these models

Conclusion

SWIPT-NOMA networks represent a major step forward in wireless communication by enabling simultaneous data and energy transmission for multiple users. However, managing these networks efficiently is not easy. Game theory offers a structured and effective way to handle resource allocation, ensuring better power control, user fairness, and overall network performance.

By applying models like non-cooperative, Stackelberg, or cooperative games, network designers can address complex challenges in power sharing, interference control, and energy management. Though there are still hurdles to overcome, the integration of game theory with SWIPT-NOMA technology holds great promise for the future of sustainable and efficient wireless communication.

With continued research and innovation, we can expect smarter, faster, and greener networks—powered not just by signals, but by strategic thinking.