Embodied Edge Intelligence Meets Near Field Communication: Concept, Design, and Verification

TL;DR

This paper proposes integrating embodied edge intelligence with near-field communication to enhance real-time AI processing on robots, addressing spectral efficiency, security, and interference challenges through novel joint optimization techniques.

Contribution

It introduces the NEEI paradigm combining EEI and NFC, along with innovative planning, beam-focusing, and collaborative navigation methods for efficient resource utilization.

Findings

Proposed radio-friendly embodied planning improves communication efficiency.

View-guided beam-focusing enhances near-field communication performance.

Experimental results demonstrate the superiority of the proposed techniques over benchmarks.

Abstract

Realizing embodied artificial intelligence is challenging due to the huge computation demands of large models (LMs). To support LMs while ensuring real-time inference, embodied edge intelligence (EEI) is a promising paradigm, which leverages an LM edge to provide computing powers in close proximity to embodied robots. Due to embodied data exchange, EEI requires higher spectral efficiency, enhanced communication security, and reduced inter-user interference. To meet these requirements, near-field communication (NFC), which leverages extremely large antenna arrays as its hardware foundation, is an ideal solution. Therefore, this paper advocates the integration of EEI and NFC, resulting in a near-field EEI (NEEI) paradigm. However, NEEI also introduces new challenges that cannot be adequately addressed by isolated EEI or NFC designs, creating research opportunities for joint optimization of both functionalities. To this end, we propose radio-friendly embodied planning for EEI-assisted NFC scenarios and view-guided beam-focusing for NFC-assisted EEI scenarios. We also elaborate how to realize resource-efficient NEEI through opportunistic collaborative navigation. Experimental results are provided to confirm the superiority of the proposed techniques compared with various benchmarks.