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Baicheng Chen

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Book Open access Aug 2026

FlowForm: Scalable Passive Metasurface Network for mmWave Coverage Expansion

Millimeter wave (mmWave) networks offer multi-gigabit data rates but suffer from severe path loss and blockage, resulting in spotty coverage. Emerging reconfigurable intelligent surfaces (RIS) can mitigate these challenges, but their reliance on active control channels, power sources, and complex runtime coordination imposes significant hardware and deployment overhead. This paper introduces FlowForm, a system that expands mmWave coverage using networks of passive metasurfaces that require no power, control, or runtime coordination. FlowForm's key innovation is a hierarchical flow topology that organizes passive metasurfaces into major flows (directional relay chains using near-field focusing) and minor flows (wide-area fan beams), enabling multi-hop passive routing and over-the-air combination of analog signals. We develop a theoretical framework establishing the optimality of this topology and a hierarchical optimization algorithm that jointly determines metasurface placement and beam configurations. FlowForm operates transparently with standard mmWave network protocols, managing channel dynamics and multi-user interference through diversity-aware design rather than runtime reconfiguration. Our experimental evaluation across five indoor environments demonstrates up to 94% average rate improvement and 114% coverage expansion using low-cost 3D-printed metasurfaces ($2 per unit), achieving performance comparable to active RIS at orders of magnitude lower cost.

Wuqiong Zhao, Baicheng Chen, Kai Zheng et al. · 0 citations
Jun 2026

DynoPipe: Heterogeneous Edge-Cloud LLM Serving with Dynamically Orchestrated Pipeline Boundaries

Large language model (LLM) deployment at the network edge faces a fundamental paradox: applications require full-scale models for sophisticated reasoning, yet edge devices impose severe resource constraints across computation, memory, and network. Existing approaches fail to effectively orchestrate resources across the edge-cloud continuum, leaving capacity underutilized while struggling with heterogeneous and volatile distributed environments. We present DynoPipe, an adaptive edge-cloud system that addresses these constraints through dynamic pipeline parallelism with shifting computational boundaries. DynoPipe tackles three core challenges: structural heterogeneity causing 94% pipeline idle time, temporal resource volatility invalidating static partitioning, and boundary migration overhead trapping systems in suboptimal configurations. Through boundary-constrained pipeline construction, proactive multi-configuration orchestration, and hierarchical state management, DynoPipe eliminates the memory wall while preserving data locality, achieving $\mathbf{1 0. 1} \times$ throughput improvement over edge-only baselines and $\mathbf{1. 6} \times$ over cloud-only execution, with 99.2% latency reduction.

Yanying Lin, Baicheng Chen, Xinyu Zhang et al. · 0 citations