Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions

TL;DR

The paper tackles the challenge of enabling efficient semantic communications (SemCom) in 6G under dynamic, resource-constrained conditions. It proposes KB-MANO, a knowledge-base management and orchestration framework built atop Computing-Network Convergence and lifelong learning to refine and distribute semantic KBs with minimal service disruption. The architecture comprises E2E, Domain, and Resource layers that coordinate KB training strategies (centralized, distributed, federated) and CSU-like CNCO-driven resource allocation, with a proof-of-concept demonstrating improved semantic throughput via a semantic-aware radio access scenario. Key contributions include a detailed KB-MANO design, integration with CNCO, a performance evaluation showing resource savings and throughput gains, and a roadmap of future research directions toward sustainable, deterministic, and end-to-end semantic-aware 6G systems.

Abstract

In the context of emerging 6G services, the realization of everything-to-everything interactions involving a myriad of physical and digital entities presents a crucial challenge. This challenge is exacerbated by resource scarcity in communication infrastructures, necessitating innovative solutions for effective service implementation. Exploring the potential of Semantic Communications (SemCom) to enhance point-to-point physical layer efficiency shows great promise in addressing this challenge. However, achieving efficient SemCom requires overcoming the significant hurdle of knowledge sharing between semantic decoders and encoders, particularly in the dynamic and non-stationary environment with stringent end-to-end quality requirements. To bridge this gap in existing literature, this paper introduces the Knowledge Base Management And Orchestration (KB-MANO) framework. Rooted in the concepts of Computing-Network Convergence (CNC) and lifelong learning, KB-MANO is crafted for the allocation of network and computing resources dedicated to updating and redistributing KBs across the system. The primary objective is to minimize the impact of knowledge management activities on actual service provisioning. A proof-of-concept is proposed to showcase the integration of KB-MANO with resource allocation in radio access networks. Finally, the paper offers insights into future research directions, emphasizing the transformative potential of semantic-oriented communication systems in the realm of 6G technology.

Figures (6)

• Figure 1: The anticipated characteristics, QoS/QoE criteria, and illustrative examples of services expected in 6G.
• Figure 2: The elements of semantic encoders and decoders (transcoders) encompassing various tasks, including buffering traffic, synchronizing traffic from diverse sources, splitting or constructing streams, sampling data from distinct streams, extracting or constructing semantics, and performing traffic shaping. Semantic encoders can be implemented as service-specific functional components on user devices or the system's edge computing resources. Conversely, semantic decoders should be implemented at locations where the semantics need to be utilized.
• Figure 3: Forthcoming 6G services facilitated by an integrated cloud-network infrastructure, incorporating technologies such as deterministic networking, time-sensitive networking, and intelligent medium access control.
• Figure 4: The KB-MANO architecture integrated with the CNCO framework (detailed by Shokrnezhad et al.shokrnezhad2024acnc). It is noteworthy that the E2E and domain layers can be instantiated as components within the CNCO E2E and domain orchestrators, respectively. Furthermore, the network and computing resource layers can be realized as virtual functions on network devices and computing nodes.
• Figure 5: The KB refinement training strategies, including centralized, distributed, and federated approaches. It is essential to highlight that the implementation of the semantic encoder can occur independently on user devices or be integrated into network edge devices.