Wireless Network Design Optimization for Computer Teaching with Deep Reinforcement Learning Application

Computer technology has had a significant impact on the field of education, and its use in classrooms has facilitated the spread of knowledge and has helped students become well-rounded citizens. However, as the number of users accessing wireless networks for computer-based learning continues to grow, the scarcity of spectrum resources has become more apparent, which makes it crucial to find intelligent methods to improve spectrum utilization in wireless networks. Dynamic spectrum access is a critical technology in wireless networks, and it primarily focuses on how users can efficiently access licensed spectrum in a dynamic environment. This technology is a crucial means to tackle the problem of spectrum scarcity and low spectrum utilization. This work proposes a novel approach to address the issue of dynamic channel access optimization in wireless networks and investigates the dynamic resource optimization problem with deep reinforcement learning algorithms. The proposed approach focuses on the optimization of dynamic multi-channel access under multi-user scenarios and considers the collision and interference caused by multiple users accessing a channel simultaneously. Each user selects a channel to access and transmit data, and the network aims to develop a multi-user strategy that maximizes network benefits without requiring online coordination or information exchange between users. What makes this work unique is its utilization of deep reinforcement learning algorithms and a Long Short-Term Memory (LSTM) network that keeps an internal state and combines observations over events. This approach allows the network to utilize the history of processes to estimate the true state, providing valuable insights into how deep reinforcement learning algorithms can be used to optimize dynamic channel access in wireless networks. The work's contribution lies in demonstrating that this approach is an effective means to solve the dynamic resource optimization problem, enabling the development of a multi-user strategy that maximizes network benefits. This approach is particularly valuable as it does not require online coordination or information exchange between users, which can be challenging in real-world scenarios. The proposed approach presents an important contribution to the field of dynamic spectrum access and wireless network optimization. As the demand for computer-based learning continues to increase, the use of intelligent methods to improve spectrum utilization in wireless networks will become even more critical. The findings of this work could have significant implications for the future of computer-based learning and education, enabling more efficient use of wireless networks and the creation of well-rounded citizens.