DeepRP: Bottleneck Theory Guided Relay Placement for 6G Mesh Backhaul Augmentation

Backhaul mesh networks are critical for ensuring coverage and connectivity of high-frequency 6G networks. To maintain high throughput, its architecture needs to be augmented by adding relays. However, how to place relays at appropriate sites poses two challenges: 1) there lacks a theory to capture the relationship between a certain change of network architecture and its throughput gain; 2) selecting the best sites for relays is a complicated combinatorial problem. To tackle the first challenge, this paper first establishes a clique-based bottleneck theory, through which a clique-based bottleneck structure of a given network architecture is constructed to determine the network throughput. Based on this bottleneck structure, clique gradients are then computed to quantify the impact of each clique on the overall network throughput. With the clique-based bottleneck theory, the second challenge is resolved by embedding clique gradients into a deep reinforcement learning (DRL) scheme. Specifically, the DRL actions are masked such that only the relay sites that match the highest clique gradients are selected. This DRL-based relay placement (DeepRP) scheme is evaluated via extensive simulations, and performance results show that it can boost network throughput by more than 50%, which is <inline-formula><tex-math notation="LaTeX">$\text{10.4} \!-\! \text{32.1}\% $</tex-math></inline-formula> higher than those of baseline schemes.