Transformer-Based MCS Prediction for 5G Multicast-Broadcast Services (MBS)

TL;DR

This paper introduces a lightweight Transformer-based model to predict MCS success probabilities for 5G MBS, improving reliability and stability over traditional throughput-focused methods, with real-time inference on smartphones.

Contribution

The paper presents a novel Transformer-based framework with a custom loss function for reliable MCS prediction in 5G MBS, optimized for real-time deployment.

Findings

Achieved 86.89% reliability score in MCS prediction.

Outperformed standard AI baselines with 31.65% reliability.

Inference time under 0.07 ms on smartphones.

Abstract

The deployment of 5G Multicast-Broadcast Services (MBS) is emerging as a critical technology for spectral-efficient UHD content delivery and serving as a promising solution to modernize CATV deployment. However, unlike unicast networks that rely on RLC-AM with HARQ retransmissions, MBS broadcast operates in RLC Unacknowledged Mode (RLC-UM), where the absence of a feedback loop means packet loss is permanent and immediately impacts user QoE. Conventional link adaptation algorithms, designed for unicast, typically aggressively maximize throughput and fail in this risk-intolerant environment, resulting in severe video stalls and rebuffering. To address this, we propose a lightweight Transformer-based framework that predicts the success probability of all 28 MCS indices over an upcoming video segment horizon. Utilizing a unique commercial network dataset with 0.5 ms slot-level granularity, we train our model using a custom Asymmetric Safety Loss function that penalizes channel overestimation to prioritize link stability. Experimental results show that our approach achieves a reliability score of 86.89%, significantly outperforming standard AI baselines optimized for raw throughput (31.65%) while maintaining a safe conservative bias. Furthermore, the model is optimized for real-time applications, demonstrating an inference time of less than 0.07 ms on COTS 5G-era smartphones.