The coordinated deployment of multiple Base Stations (BS) and tuning of antenna configuration plays a crucial role in ensuring high-quality communication services, especially in the context of dense 5G BS deployment in megacities. However, traditional optimization methods, such as heuristics and Reinforcement Learning (RL), face challenges in addressing such problems involving the coordination of hundreds of BSs due to their limitations in handling the complexity and scale of large-scale scenarios. To address these challenges, this article proposes the Hierarchical Multi-Agent Proximal Policy Optimization with Representation Learning (HMAPPO-RL). By employing a hierarchical structure, we effectively decouple the optimization problem into two sub-problems: BS deployment and antenna parameter tuning. Different from the step-by-step method of optimizing the BS location and antenna, HMAPPO-RL achieves joint optimization of the two problems through an ingenious interactive mechanism, fully considering the mutual influence of the BS location and antenna. To address the large-scale challenge posed by hundreds of BSs, we utilize the upsampling and downsampling mechanisms of the UNet network to integrate global and local information from large-scale state information for performance enhancement. Since complex environmental information will cause great difficulties for the agent to evaluate the state value in large-scale scenarios, we add a representation learning module to enhance the accuracy of the agent’s state value estimation. The experiments using a precise mobile network simulator demonstrate the superiority of the proposed HMAPPO-RL, offering a comparative analysis with existing state-of-the-art methods. HMAPPO-RL achieves a coverage rate of 91.66% and an average throughput of 4,983,537 bit/s. These results represent improvements of 3.62% and 6.75% in coverage rate and throughput, respectively, when compared with the MAPPO algorithm.

Simulation-based optimization has emerged as a crucial methodology in the field of mobile network optimization, addressing the need for dynamic and predictive network management. To address the scarcity of open-source mobile network simulators for advanced research, we developed GenNet—a generative AI-driven mobile network simulator. GenNet can create virtual replicas of mobile users, base stations, and wireless environments, utilizing generative AI methods to simulate the behaviors of these entities under various network settings with high accuracy. GenNet features a tailor-made API explicitly designed for reinforcement learning environments, enabling researchers to finely adjust network parameters such as tilts, azimuth, and transmitting power. Extensive experiments have utilized GenNet to benchmark multi-objective optimization algorithms, with a focus on enhancing network coverage, throughput, and energy efficiency, thereby validating its effectiveness as a robust platform for advancing network optimization techniques. Through this innovative tool, we aim to empower researchers and practitioners to identify and implement the most effective approaches for network optimization, paving the way for future advancements in mobile network management.

With the rapid advancement of communication technology and the exponential growth of mobile users, improving network coverage quality and throughput has become increasingly important. In particular, large-scale Base Station (BS) cooperative optimization has become a highly significant topic. BSs can adjust various parameters for high-quality communication, but automating this optimization remains challenging due to environmental sensitivity and interdependencies. Traditional methods for network optimization are constrained by the intricate nature of real-world environments. Further, Reinforcement Learning (RL) techniques, which are effective for configuration policies, encounter difficulties in intricate, high-dimensional wireless communication networks, especially in multi-agent cooperative optimization. To overcome these challenges, this article proposes the Enhanced Multi-Agent Proximal Policy Optimization (EMAPPO), which utilizes the capabilities of the UNet network to extract multi-spatial relationships among a massive number of network elements and employs the DiffPool network to efficiently depict the impact of large-scale action coordination among massive agents on coverage performance. To facilitate evaluation in communication optimization, we further introduce a high-fidelity digital twin-driven mobile network. Extensive experiments validate the effectiveness and superior performance of EMAPPO by utilizing the network digital twin. The results demonstrate significant improvements in signal coverage rate and network throughput compared to the competing methods.

With the exponential growth of mobile users, ensuring high-quality network coverage has become paramount. Large-scale mobile networks consist of numerous base stations (BSs), each with adjustable parameters such as angles and beam widths. Automatically optimizing network coverage can be difficult due to environmental factors and the interdependence of the adjustable parameters. Due to the inherent uncertainties and unpredictable nature of large-scale wireless networks, traditional methods such as heuristics and meta-heuristics lack the adaptability and scalability required to cope with their dynamic environment. To address these challenges, we propose utilizing digital twin and reinforcement learning (RL) techniques within mobile networks characterized by multiple collaborating agents. We initially introduce DT-SimNet, a digital twin-enabled mobile network simulator to facilitate optimization evaluation. DT-SimNet can efficiently simulate communication behaviors of network elements within a complex environment while revealing user mobility patterns. Moreover, to address challenges arising from multifaceted relationships among users, BSs, and the parameters across BSs, we introduce an innovative strategy named Optimized Multi-Agent Proximal Policy Optimization with Self-supervised Prediction (OMAPPO-SSP). Compared to MAPPO, which leads to limited applicability and inferior performance due to the dynamic characteristics of 5G networks, this approach leverages network structure optimization and a self-supervised prediction mechanism, employing multi-agent reinforcement learning (MARL) principles to enhance efficiency. By harnessing collaborative neural networks, OMAPPO-SSP facilitates the explicit learning of behavioral interactions among all BSs, enabling effective decision-making in environments characterized by intricate spatial relationships, dynamic user behaviors, and diverse interactions. Extensive experiments are conducted to validate the efficiency and effectiveness of the OMAPPO-SSP. Within the target area, OMAPPO-SSP achieves a coverage ratio of 94.66% and an average throughput of 89746 bits per second (bps), demonstrating significant improvements compared to competing methods.