[简介] 针对传统大模型推理在长上下文场景及新兴智能体时代推理中所面临的键值（KV）Cache膨胀、本地显存容量受限及跨节点通信开销高等问题，提出一种基于光输入/输出（OIO）拉远且以内存为中心的新型存算解聚合架构。该架构在物理层面实现计算与存储资源的解耦，支持二者的独立伸缩、池化共享与按需协同；在计算侧将Prefill与Decode阶段划分为异构计算簇，在存储侧构建基于异质材料的全局内存池，并借助近存计算单元对集合通信原语进行卸载。基于8个异构计算处理单元（8XPU）与8个内存处理单元（8MPU）全互联配置的系统级仿真分析表明，该架构在长上下文及高并发推理场景下，可有效降低首token时延与逐token时延，减少数据搬移开销，提升系统吞吐率，并在满足同等服务水平协议（SLA）的条件下显著降低对计算资源的需求，验证了其在智算基础设施中的潜在应用价值。

[简介]随着人工智能与产业数字化的深入发展，智算中心互联及高品质企业上云的业务需求日益增长，传统城域光网络在带宽、时延、架构及业务感知能力等方面面临严峻挑战。分析了城域光网络关键技术的发展现状，提出了“极简、扁平、融合”的城域光网络演进架构，阐述了细粒度光传送网（fgOTN）作为小颗粒高品质专线承载及智算业务接入技术在下一代OTN演进中的关键地位，探讨了fgOTN的典型应用场景，并对下一代城域光网络的发展方向进行思考和展望。

[Introduction] Fluid antenna systems (FAS) have emerged as a revolutionary technology for next-generation wireless networks, offering continuous spatial degrees of freedom. However, deploying FAS on high-speed mobile embodied intelligent agents induces severe Doppler shifts, leading to an overhead catastrophe when traditional full-port scanning is applied within the sharply reduced channel coherence time. To overcome this bottleneck, this paper proposes a prediction-aided adaptive communication architecture with a local window. Rather than relying on rigid global scanning or highly vulnerable single-point prediction, our mechanism constructs a dynamic local sounding window centered on upper-layer prediction results. By exploiting the inherent spatial channel correlation of FAS, this approach effectively bridges prediction deviations and achieves an optimal balance between pilot overhead and spatial diversity gain. Specifically, we establish a rigorous cross-layer quantitative analysis model for long-term expected throughput. This model integrates a nonlinear bounded hit probability function and a novel gain bridging model based on the zero-order Bessel function of the first kind. Based on this framework, we formulate a discrete optimization problem subject to strict timing constraints to maximize the expected transmission capacity. To solve this non-convex problem efficiently, we design a two-stage low-complexity algorithm that combines offline spatial expectation decoupling with online discrete zeroth-order gradient optimization. This algorithm completely decouples real-time computational complexity from the total number of antenna ports, enabling microsecond-level adaptive tracking of the optimal window. Extensive simulation results demonstrate that the proposed mechanism can adaptively adjust the sounding window scale in response to real-time moving velocity and prediction accuracy. Consequently, it effectively mitigates the pilot overhead catastrophe in highly dynamic scenarios and delivers significantly superior throughput performance compared to traditional fixed-window and global scanning schemes.

[Introduction] This paper investigates joint resource allocation for movable-antenna (MA) -assisted secure integrated data and energy transfer (IDET) systems with passive eavesdroppers. A field-response-based MA channel model is adopted to capture the impact of antenna positions on legitimate, energy-transfer, and eavesdropping links, while energy signals are reused as artificial noise for simultaneous wireless power transfer and secrecy enhancement. A max-min secrecy-rate problem is formulated by jointly optimizing information beamformers, energy beamformers, and MA positions under energy-harvesting, power, movable-region, and antenna-spacing constraints. To address the resulting nonconvexity, an iterative algorithm based on semidefinite relaxation, successive convex approximation, and alternating optimization is developed. Simulation results demonstrate that the proposed scheme improves the minimum secrecy rate compared with fixed-position antenna benchmarks while satisfying the energy requirements of energy receivers.