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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Comeback Kid: Resilience for Mixed-Critical Wireless Netw...
Robert-Jeron Reifert, Stefan Roth, Alaa Alameer Ahmad, Aydin Sez · 2022-04-26 · via cs.IT updates on arXiv.org

The future sixth generation (6G) of communication systems is envisioned to provide numerous applications in safety-critical contexts, e.g., driverless traffic, modular industry, and smart cities, which require outstanding performance, high reliability and fault tolerance, as well as autonomy. Ensuring criticality awareness for diverse functional safety applications and providing fault tolerance in an autonomous manner are essential for future 6G systems. Therefore, this paper proposes jointly employing the concepts of resilience and mixed criticality. In this work, we conduct physical layer resource management in cloud-based networks under the rate-splitting paradigm, which is a promising factor towards achieving high resilience. We recapitulate the concepts individually, outline a joint metric to measure the criticality-aware resilience, and verify its merits in a case study. We, thereby, formulate a non-convex optimization problem, derive an efficient iterative algorithm, propose four resilience mechanisms differing in quality and time of adaption, and conduct extensive numerical simulations. Towards this end, we propose a highly autonomous rate-splitting-enabled physical layer resource management algorithm for future 6G networks respecting mixed-critical quality of service (QoS) levels and providing high levels of resilience. Results emphasize the considerable improvements of incorporating a mixed criticality-aware resilience strategy under channel outages and strict QoS demands. The rate-splitting paradigm is particularly shown to overcome state-of-the-art interference management techniques, and the resilience and throughput adaption over consecutive outage events reveals the proposed schemes contribution towards enabling future 6G networks.