Resilience-by-Design in 6G Networks: Literature Review and Novel Enabling Concepts

TL;DR

The paper tackles the growing need for resilient 6G networks by proposing resilience-by-design ($P1$–$P3$: protective design measures, self-awareness, and reconfiguration) as an end-to-end framework applicable across electronics, physical channels, network components, networks, services, and cross-infrastructure considerations. It synthesizes existing resilience literature, defines a unified resilience concept, and details layer-specific realizations to embed resilience from design through operation and evolution. Concrete 6G use-cases (cloud-based monitoring, autonomous driving, automated factories, and XR) illustrate how RBD can be instantiated in practice, while highlighting design trade-offs between resilience, complexity, and cost. The work also identifies open problems for future research, including tooling for resilient electronics, secure autonomic control planes, scalable cross-layer resilience measurement, and policy/social-legal considerations, aiming to influence standardization and the Open6GHub effort. Overall, the framework provides a structured path to embed end-to-end resilience in 6G networks from the ground up, enabling dependable operation of critical services in a highly interconnected digital society.

Abstract

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, health-care, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these digital services, whether due to human or technical failures, natural disasters, or terrorism, would significantly impact citizens' daily lives. Hence, 6G networks need not only to provide high-performance services but also to be resilient in maintaining essential services in the face of potentially unknown challenges. This paper provides a general review of the state of the art on resilient systems, definitions, concepts, and approaches. Moreover, it introduces a comprehensive concept, i.e., resilience-by-design (RBD), in three different levels for designing resilient 6G communication networks, summarizing our initial studies within the German Open6GHub project. First, we outline the general RBD enabling principles and discuss their related sub-categories. Next, adopting an interdisciplinary approach, we propose to embed these principles across all 6G layers/perspectives including electronics, physical channel, network components and functions, networks, services, and cross-layer and cross-infrastructure considerations and discuss their challenges. We further elaborate the RBD principles and their realizations along with several 6G use-cases. The paper is concluded by presenting a comprehensive list of open problems for future research on 6G resilience.

Figures (7)

• Figure 1: A resilient 6G must not only provide reliable and high-quality services under the normal system operation, but also be able to maintain a minimum service requirement when facing challenges. This figure illustrates four 6G application scenarios, namely smart factory, smart city, autonomous driving, and cellular communication network, and correspondingly a few potential challenges in each scenario.
• Figure 2: Self-awareness capability of RBD systems.
• Figure 3: Proposed RBD concept for 6G illustrating (from left to right) cyber vs. physical resilience, resilience for relevant different layers, cross-layer and cross-infrastructure considerations, and key principle features of 6G resilience, namely protective design measures, self-awareness and reconfiguration. The local system states $S_i$ and the available operational modes $M_j$ are shown. In the normal state, $S_0$, the system operates on the normal mode $M_0$. During a challenge, the system state is changed to $S_1$ or $S_2$, where the system selects a new operational mode, i.e., $M_1$, $M_2$, or $M_3$. It can also switch among modes to recover from the challenge. The system will learn potential system states, such as $S_3$, using the knowledge obtained during the challenge, and it will evolve by developing a new operational mode, denoted as $M_4$.
• Figure 4: Cloud-based distributed monitoring network as a 6G use-case.
• Figure 5: Autonomous driving as a 6G use-case.