Shuo Li

Indoor optical wireless communication (OWC) offers high-speed and secure transmission, but is vulnerable to blockage under line-of-sight (LOS) conditions. Non-line-of-sight (NLOS) OWC mitigates this by leveraging diffused optical paths, yet introduces a challenging channel environment with severe multipath dispersion, signal attenuation, device nonlinearity, and inter-symbol interference (ISI)—especially at high data rates. These factors significantly degrade performance and limit the effectiveness of traditional equalizers. This letter proposes a physics-informed neural network (PINN)-based equalizer designed to address the challenges of NLOS indoor OWC. Compared to conventional equalizers, the PINN equalizer improves the bit error rate (BER) by 24.6% to 70.87% under varying data rates and silicon photomultiplier (SiPM) bias currents. Compared to an equalizer based on a vanilla recurrent neural network (RNN), our proposed PINN equalizer reduces the training time by 8.21% to 52.22%. Experimental results confirm the potential of PINN for efficient and robust NLOS indoor OWC.

In Multi-access Edge Computing (MEC) environments, session continuity during user mobility remains a pressing challenge due to decentralized infrastructure and high-throughput, latency-sensitive applications. Existing mobility protocols often rely on stateful mechanisms or centralized control, leading to increased signaling overhead, limited scalability, and vulnerability to performance degradation in dynamic networks. This paper introduces the Server Search and Select Algorithm Protocol (SSSAP), a lightweight, UDP-based handover protocol tailored for MEC deployments. The protocol is an extension of our previous work on a handover Server Search and Selection Algorithm (SSSA). SSSAP enables seamless session redirection through a three-phase signaling scheme (pre-handover, handover initiation, and handover termination), preserving service continuity without coupling session state to transport layers. The protocol’s design features extensible headers for multi-metric evaluation and future security adaptation while maintaining minimal dependency on intermediary control nodes. Through extensive simulation and testing, we have validated the SS-SAP efficiency across user equipment nodes and MEC servers. Results demonstrate high handover success rates, low-session setup delays, and balanced server load distribution. SSSAP achieves superior performance in mobility robustness, packet loss mitigation, and integration simplicity. The research outcomes position SSSAP as a scalable and application-agnostic mobility protocol for MEC systems, especially in vehicular and high-mobility scenarios.

Driven by massive investments and consequently significant progresses in optical computing and all-optical signal processing technologies lately, this paper presents a new architectural paradigm for next-generation optical transport network, entitled \textit{optical computing-communication integrated network}, which is capable of providing dual services at the optical layer, namely, computing and communication. This approach seeks to exploit the potential for performing optical computing operations among lightpaths that traverse the same intermediate node. \textit{Optical-layer intelligence concept} is thus introduced as the capability to perform computing / processing at the lightpath scale to achieve greater spectral and/or computing efficiency. A case study focusing on optical aggregation operation is introduced, highlighting the key differences between optical computing-communication integrated network and its current counterpart, optical-bypass ones. A mathematical formulation for optimal designs of optical-aggregation-enabled network is then provided and performance comparison with traditional optical-bypass model is drawn on the realistic NSFNET topology.