arXiv:2607.01921v1 Announce Type: cross Abstract: Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels. To address this, we propose a transmission framework with opportunistic spectrum access, in which the transmitter sends discrete latent representations learned via a vector-quantized variational autoencoder (VQ-VAE) over idle licensed channels using standard digital modulation. The AI-powered receiver is still
The increasing demand for efficient and low-latency communication in AI-driven applications is pushing the boundaries of traditional data transmission methods.
This research outlines a key development for more efficient and resilient communication infrastructure, potentially reducing bottlenecks in AI-powered systems reliant on rapid data exchange.
Communication systems can now potentially move towards integrated source-channel coding and opportunistic spectrum access, leading to faster, more robust AI-centric data transfer.
- · AI developers
- · Telecommunications infrastructure providers
- · Edge computing industries
- · Traditional communication system developers
- · Sectors heavily reliant on high-latency data transfers
More efficient AI model deployment and operation in environments with limited bandwidth or variable spectrum availability.
Accelerated development of real-time AI applications across various sectors, including autonomous vehicles and industrial automation.
Reduced computational and energy overhead for data transmission, indirectly impacting the sustainability of AI operations.
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Read at arXiv cs.AI