arXiv subjects

Arnav Mathur

Publications and source records attributed to Arnav Mathur.

2 recordsLinked to original sources

arXiv2026

Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence

Sixth-generation (6G) wireless networks are envisioned as AI-native systems in which semantic communication - transmitting task-relevant meaning rather than raw bits - moves beyond Shannon's classical bit-pipe model. Large language models (LLMs) dominate semantic encoding but are unsuitable for 6G user equipment and IoT devices, given prohibitive memory, energy, and latency costs. Tiny language models (TinyLMs) - compressed via TinyML techniques into kilobyte-to-megabyte memory and milliwatt power budgets - are the missing bridge between LLM-level semantic encoding and 6G edge hardware, yet no prior work systematically maps TinyML techniques onto semantic communication architectures for this purpose. This survey closes that gap through a two-axis taxonomy connecting six compression families (quantization, pruning, knowledge distillation, low-rank adaptation, neural architecture search, hybrid pipelines) to five semantic communication architectures (end-to-end joint source-channel coding, split learning, federated learning, knowledge-graph-assisted, and multi-task/cross-modal communication), synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier. Representative results include a CNN-Transformer encoder achieving 22 dB PSNR at 33.33% semantic-representation size reduction; a symbolic protocol machine reducing a neural MAC protocol from 4.55 MB to 1 KB (99.98% smaller) with zero performance loss; federated bidirectional knowledge distillation converging under joint model-and-data heterogeneity where FedAvg-style averaging underperforms; and knowledge-graph-assisted probability graphs cutting transmission energy by 65%. The survey identifies nine open research challenges for TinyLM-enabled 6G semantic communication, including two not previously articulated in the literature.

eess.SP↗

arXiv2026

Bridging the Semantic Gap in 6G: Tiny Language Models Under the Latency-Accuracy-Size Trilemma

Sixth-generation (6G) wireless networks are expected to serve as AI-native infrastructure, transmitting meaning rather than mere bits -- a shift that makes semantic communication the central paradigm for next-generation connectivity. Deep learning-based semantic encoders show compelling gains in bandwidth efficiency; however, their dependence on large transformer models with hundreds of millions of parameters is at odds with the sub-millisecond latency, microjoule energy budgets, and kilobyte memory footprints of the constrained IoT and edge devices that will dominate 6G endpoints. Tiny language models (t-LMs) -- compact, quantised, task-specialised models deployable on microcontrollers, mobile system-on-chips, and edge accelerators -- are the enabling technology for closing this gap. This review provides a unified treatment of (i) the theoretical foundations of semantic information, covering semantic entropy, channel capacity, and rate-distortion theory; (ii) a two-axis taxonomy of t-LM-based semantic communication systems across five architecture classes and six compression paradigms; (iii) a survey of model compression techniques -- quantisation, pruning, knowledge distillation, low-rank adaptation, split computing, and neural architecture search -- through the lens of semantic quality preservation; and (iv) semantic-aware resource allocation frameworks for 6G multi-user networks. Evidence across the surveyed literature shows that compression can reduce semantic encoder size by up to 99.98% while preserving task accuracy, that split computing achieves device-side encoders with as few as 640 parameters, and that knowledge graph integration cuts transmission energy by 65%. Seven open challenges are identified, spanning theoretical gaps, system design, knowledge-base management, post-quantum security, and hardware co-design, with a 3GPP standardisation roadmap toward IMT-2030.

eess.SP↗