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

• URL: http://arxiv.org/abs/2605.16735v1
• Date: Sat, 16 May 2026 01:10:55 GMT
• Title: Transformer-Based MCS Prediction for 5G Multicast-Broadcast Services (MBS)
• Abstract summary: We propose a lightweight Transformer-based framework that predicts the success probability of all 28 MCS indices over an upcoming video segment horizon.<n>We show that our approach achieves a reliability score of 86.89%, significantly outperforming standard AI baselines optimized for raw throughput.<n>The model is optimized for real-time applications, demonstrating an inference time of less than 0.07 ms on COTS 5G-era smartphones.
• Score: 7.094650278617499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G [90.42669277637026]
  Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence.<n>This article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks.
  arXiv  Detail & Related papers  (2026-09-09T01:36:44Z)
• Improving 5G AI-RAN MCS Selection by Predicting Retransmissions [13.3260699145112]
  Link Adaptation (LA) in 5G NR is inherently reactive, relying on channel measurements and HARQ feedback that may become quickly obsolete when the channel changes quickly.<n>In this paper, we improve on this status-quo with NOSTRAdAMUS, a predictive LA framework which adds foresight to existing algorithms without replacing or redesigning them.<n>We benchmark several ML models and show that Gradient Boosting achieves 82.9% accuracy overall with high-confidence interventions that are correct 94.2% of the time, and an latency inference of 5.5 s.
  arXiv  Detail & Related papers  (2026-09-08T18:10:30Z)
• Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine [11.630090481307342]
  This paper investigates lightweight, explainable, and hardware-efficient jamming detection using the Convolutional Tsetlin Machine (CTM)<n>CTM formulates Boolean logic clauses over quantized inputs, enabling bit-level inference and deterministic deployment on FPGA fabrics.<n>We benchmark CTM against a convolutional neural network (CNN) baseline under identical preprocessing and training pipelines.
  arXiv  Detail & Related papers  (2026-03-07T20:54:10Z)
• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
  We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T04:30:35Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management [17.903318666906728]
  Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems.<n>Forecasting of sewer overflow basin filling levels can provide actionable insights for early intervention.<n>We propose an end-to-end forecasting framework that enables energy-efficient inference directly on edge devices.
  arXiv  Detail & Related papers  (2025-08-19T15:06:04Z)
• Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications [5.026196568145574]
  This paper presents the first comprehensive benchmarking of federated learning strategies for throughput prediction in realistic 5G edge scenarios.<n>It is found that FedBN consistently delivers robust performance under non-IID conditions.<n>LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores.
  arXiv  Detail & Related papers  (2025-08-11T21:27:40Z)
• Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach [3.8683028650079625]
  Mobile jammers pose a critical threat to 5G networks, particularly in military communications.<n>We propose an intelligent anti-jamming framework that integrates Multiple Signal Classification (MUSIC) for high-resolution Direction-of-Arrival estimation.<n>Our hybrid approach achieves an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB and up to 99.8% DoA estimation accuracy.
  arXiv  Detail & Related papers  (2025-05-12T20:31:31Z)
• RRC Signaling Storm Detection in O-RAN [0.0]
  Signaling storms caused by abuse of the Radio Resource Control protocol are a critical threat to 5G availability.<n>We develop a threshold-based detection technique that relies on RRC layer features to distinguish between malicious activity and legitimate high network load conditions.
  arXiv  Detail & Related papers  (2025-04-22T09:32:11Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization [78.46088089185156]
  Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. Existing approaches penalize emission delay by manipulating per-token or per-frame probability prediction in sequence transducer models. We propose a sequence-level emission regularization method, named FastEmit, that applies latency regularization directly on per-sequence probability in training transducer models.
  arXiv  Detail & Related papers  (2020-10-21T17:05:01Z)
• Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription [73.66530509749305]
  In this paper, we argue that, even in difficult cases, some end-to-end approaches show performance close to the hybrid baseline. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline.
  arXiv  Detail & Related papers  (2020-04-22T19:08:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.