Distributed Edge Intelligence for Efficient Artificial Intelligence Inference over Sixth-Generation Wireless Networks

Vollständiger Abstract

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The recent proliferation of artificial intelligence (AI) applications in mission-critical and latency-sensitive domains, such as autonomous driving, remote healthcare services, smart manufacturing, and immersive extended reality, puts forward requirements on intelligent yet low-latency and scalable computational infrastructures. The underlying reason is that such a transformational paradigm can reduce or circumvent the increasing limitations of traditional cloud-based AI processing, since high communication latency, bandwidth bottlenecks, and lack of context awareness are the bottleneck issues holding back conventional AI processing nowadays. The motivation for such a shift is also advanced by the expectations for sixth-generation (6G) wireless network-based solutions with ultrareliable low-latency communication, integrated sensing and communication, intelligent reflecting surfaces, and native support for AI-native protocols. This survey provides a systematic and critical review of the current status of distributed edge intelligence and focuses on the ability to enable efficient AI inference over 6G wireless networks. The state-of-the-art is categorized across system architectures, distributed learning frameworks, MAC/RAN co-design, resource optimization strategies, and AI computation offloading. Key challenges are identified, including heterogeneity, energy efficiency, context awareness, and service continuity. Furthermore, the most promising future research directions are outlined to achieve fully autonomous, intelligent, and scalable 6G-edge ecosystems able to provide real-time AI services.