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lwm

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Hello @oneenooo ,

Thank you for your interest in LWMs! To address your questions:

Can LWM be built on existing LLMs?

LWM 1.0 draws inspiration from BERT (an LLM) and ViT (a vision transformer), adapting their transformer architectures for wireless channel data. Extending this idea to LLMs like GPT or DeepSeek is feasible but requires careful engineering due to the shift from discrete text to continuous wireless data. Key adaptations include:

• Input transformation: Replace tokenization with patch-based projection (like ViT) to map wireless data (e.g., channels) into embedding sequences.
• Training objective: Swap cross-entropy for reconstruction (MSE) or contrastive losses, as wireless data is not token-based.
• Architectural tweaks: Add spatial-frequency-temporal positional encodings and retrain weights.

For example, DeepSeek’s Mixture-of-Experts (MoE) could optimize LWM by routing wireless sub-tasks (e.g., multipath vs. noise analysis) to specialized experts, improving efficiency. GPT’s generative approach could inspire channel prediction or synthesis tasks, but full retraining on wireless datasets is needed. While possible, LWM’s purpose-built design currently outperforms such adaptations.

Can LWM interface with LLMs for downstream tasks?

Yes, interfacing LWM with LLMs is practical and powerful for multi-modal tasks. LWM generates channel embeddings (comprehensive or compressed CLS token), which can pair with LLM text embeddings. Here is how:

• Fusion: Concatenate embeddings, use cross-attention, or apply gated mechanisms to blend modalities.
• Contrastive learning: Align wireless and text embeddings (e.g., via InfoNCE loss) for tasks like channel-text retrieval or guided analysis.
• Downstream: Feed fused embeddings into a task-specific model (e.g., MLP, transformer).

For instance, DeepSeek’s MoE could prioritize relevant text-channel features, while GPT’s generative capabilities could produce textual summaries from LWM embeddings. You can use pre-trained LWM and an LLM, then fine-tune a fusion layer on your task.

Guidelines

• Adaptation: Leverage LLM architectures (e.g., MoE, generative heads) with wireless-specific input and loss adjustments.
• Interfacing: Combine pre-trained LWM and LLM embeddings, using state-of-the-art fusion or contrastive methods.

Let us know if you need further clarification!

Thank you very much for your detailed and timely answer! One question I have now is: Is it possible to use my own generated dataset (e.g., channel data obeys CN (0, 1) distribution and does not take subcarriers into account) instead of DeepMIMO dataset to perform Patch,Embedding and Tokenization in the same way as in the paper? If it is feasible, is it possible to guide me how to proceed?