In a recent work, I proposed that Non-Terrestrial Networks (NTN) should be active participants in 6G sustainability governance rather than mere coverage extensions (ntn-sustainability-control). While that framework successfully uses a TinyML engine to monitor and maintain sustainability KPIs, NTN presents its own unique challenges — specifically bandwidth constraints and volatile channel conditions.
Countering these bottlenecks, I propose ALP (Adaptive Little Language Protocol): a unified, AI-inherent symbolic communication framework. ALP abstracts communication into high-density symbols, ensuring robust intent delivery that remains resilient against both restrictive throughput and signal deterioration.
Unlike traditional semantic communication that relies on continuous latent embeddings, ALP uses discrete symbolic abstractions to maintain reliability even under lossy conditions.
---
config:
layout: elk
---
%%{init: {'themeVariables': { 'fontSize': '12px' }}}%%
flowchart TB
subgraph ALP_Engine_Tx["ALP Engine Tx"]
LibTx[("ALP Symbol Library")]
ALP_Generator["ALP Generator"]
end
subgraph ALP_Engine_Rx["ALP Engine Rx"]
LibRx[("ALP Symbol Library")]
ALP_Reconstructor["ALP Reconstructor"]
CH[/"Context / History"/]
end
LibTx --> ALP_Generator
LibRx --> ALP_Reconstructor
CH --> ALP_Reconstructor
Input(["Input: Text / Voice / Video"]) --> ALP_Generator
ALP_Generator --> ALP_Symbols("ALP Symbols")
ALP_Symbols --> Channel["Transmission Channel"]
Channel --> ALP_Reconstructor
ALP_Reconstructor --> Reconstructed_Msg(["Reconstructed Meaning"])
ALP has two key components:
- ALP Generator: Handles the semantic encoding of inputs into a compact, adaptive symbolic "little language."
- ALP Reconstructor: Performs context-aware decoding to restore meaning, leveraging historical data to mitigate information loss in volatile environments.
-------------------------------------------------------------------------------------
| Message (O: Original, R: Re-constructed) | Ch health % | Saving % | Recovery % |
-------------------------------------------------------------------------------------
| O: how was the meeting? | 60 | 92 | 50 |
| Tx tokens: 1 - <alp_9> | | | |
| Rx tokens: 1 - <alp_9> | | | |
| R: how was it | | | |
| ---------------------------------------- | ------------ | ---------- | ---------- |
| O: ready food at home | 50 | 83 | 100 |
| Tx tokens: 2 - <alp_2> <alp_4> | | | |
| Rx tokens: 2 - <alp_2> <alp_4> | | | |
| R: home keep food ready | | | |
| ---------------------------------------- | ------------ | ---------- | ---------- |
| O: planning trip to goa | 50 | 78 | 67 |
| Tx tokens: 3 - <alp_5> <alp_6> <alp_8> | | | |
| Rx tokens: 3 - <alp_5> <alp_6> | | | |
| R: planning short trip [goa] | | | |
| ---------------------------------------- | ------------ | ---------- | ---------- |
| O: emergency! help!! | 10 | 91 | 100 |
| Tx tokens: 1 - <alp_0> | | | |
| Rx tokens: 1 - <alp_0> | | | |
| R: emergency need help | | | |
| ---------------------------------------- | ------------ | ---------- | ---------- |
| O: on the way home | 10 | 80 | 33 |
| Tx tokens: 2 - <alp_1> <alp_2> | | | |
| Rx tokens: 2 - | | | |
| R: [on my way home] | | | |
-------------------------------------------------------------------------------------
Note:
ALP ensures high Recovery % despite token loss (visible in the Tx vs. Rx mismatch). Text within
[]highlights the system's ability to "fill in the gaps" using semantic context/history when the channel degrades.
ALP demonstrates:
- Significant Bandwidth Efficiency: Achieves over 80–90%+ reduction in data overhead compared to standard telemetry/packet transmission.
- Superior Channel Resilience: Maintains high semantic recovery and intent integrity even under severely impaired or high-loss conditions.
- Graceful Non-linear Degradation: Unlike traditional protocols that "cliff-edge" (fail completely), ALP degrades gracefully, preserving core meaning as signal quality drops.
- Historical Contextual Intelligence: Leverages sequence history to enable successful reconstruction from partial or entirely missing symbols.
Unlike traditional estimation, ALP uses bit-level encoding:
- Standard text ≈
8 bits per character - ALP symbol ≈
log2(N)bits (whereNis the symbol library size)
Example:
N = 4096 symbols → 12 bits per symbol
This enables ultra-aggressive throughput conservation, independent of textual representation
- Phrase → symbol mapping
- Context/History-aware reconstruction
- Channel degradation simulation
- Bandwidth vs recovery evaluation
- Heuristic phrase matching (not learned yet)
- Small symbol library
- Limited dataset
- Learned symbol mapping (LLMs / embeddings)
- Voice, video integration
- Adaptive symbol optimization
- Integration with 6G control layers
Install pre-requisites
pip install -r requirements.txt
Run the demo
python -m scripts.demo
ALP marks a fundamental shift from transmitting data representations to transmitting symbolic intent. It establishes a blueprint for the future AI-native 6G and NTN communication protocols, where meaning precedes the bitstream.
Sonu Sonkar
Researching AI-embedded communication systems for 6G & NTN
This project is licensed under the MIT License - see the LICENSE file for details.
This research work is an independent initiative. I welcome discussions regarding institutional partnerships, pilot deployments, or research grants to advance this ALP into a next-generation integrated semantic and intent-centric communication protocol for 6G and NTN.