Shushi Gu

Stimulated Brillouin scattering (SBS), a third‐order nonlinear optical effect, arising from photon‐phonon interactions, exhibits one of the lowest thresholds among nonlinear processes and has recently emerged as a powerful mechanism in integrated photonics. Owing to its unique opto‐acoustic coupling, on‐chip SBS enables a variety of functionalities, including narrow‐linewidth lasers, microwave photonic filtering, optical delay, optical amplification, optical recurrent neural networks, optical storage, phonon lasing, and ultra‐high‐purity RF signal generation. Furthermore, the integration of SBS with Kerr nonlinearity, topological photonics, and exceptional points has enabled new phenomena such as broadband platform solitons, robust directional Stokes light transmission, and high‐precision gyroscopes. Since its first demonstration in chalcogenide waveguides, on‐chip SBS has been realized in diverse material platforms, including silicon, silicon nitride, rare‐earth‐ion‐doped silicon nitride, and lithium niobate, each requiring distinct characterization approaches. In this review, we introduce the fundamental principles and classifications of SBS, followed by a comprehensive overview of seven representative experimental Configurations for on‐chip SBS characterization. Their operating principles, advantages, limitations, and applicable scenarios are systematically compared. Finally, we discuss emerging opportunities for SBS in topological photonic platforms toward robust and quantum‐coherent photon‐phonon functionalities.

Reliable image transmission is pivotal for lunar IoT networks, providing visual information for scientific exploration and real-time navigation. However, the moon’s harsh environment, i.e., lack of atmosphere, low surface conductivity, and rugged terrain, induces abnormal signal attenuation and packet losses, significantly degrading image transmission quality. This paper presents the Lunar Adaptive Image Transmission System (LAITS), which integrates the Terrain-Aware Field Strength Prediction (TAFSP) framework with the Hierarchical RaptorQ Redundancy Optimization (HRRO) coding algorithm. The TAFSP framework provides field strength predictions for channel packet loss rate estimation, by leveraging high-resolution lunar elevation data to formulate a radio propagation loss model. The HRRO algorithm optimizes coding efficiency and enables low-overhead transmission, by dynamically adjusting RaptorQ’s data segmentation based on the predictions of channel packet loss rate and priorities of image data. Simulations demonstrate that while maintaining image transmission quality above the 25 dB PSNR threshold, LAITS achieves a coverage rate of 85% in flat terrain with a 4 km radius at 440 MHz, 915 MHz, and 2400 MHz, and achieves coverage rates of 85% at 440 MHz, 70% at 915 MHz, and 56% at 2400 MHz in rugged terrain with the same radius.

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we first introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on Earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user could access one or more satellites at the same time. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic wrap-around fashion. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections given the satellite constellation, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to select user groups and construct request patterns, such that the connections between users and satellites involved could be decoupled. Moreover, the gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results show the superior performance of the proposed scheme and converse bound.

The LEO satellite data center (LSDC) is becoming a key application paradigm for the future satellite Internet with global information fusion and big data analysis, which consists of numerous satellite storage nodes forming distributed storage systems. Satellite on-board disks inevitably fail due to space radiation and battery depletion, resulting in data loss. Erasure coding (EC) can guarantee data reliability at the expense of heavy repair traffic. To mitigate this challenge, multiple repair data flows can be aggregated progressively into a single data flow during transmission based on the reconstruction matrix of EC. This repair mechanism of storage-transmission integration can effectively accelerate data repair. In this paper, we employ storage-transmission integration to LSDC and propose the erasure coding aggregation repair (ECAR). Firstly, ECAR models repair data flows in LSDC as a staged tree graph, called multi-weight path tree (MWPT). The edge weights in MWPT are metricized by multiple factors affecting the repair performance, including transmission time, propagation delay and energy cost. Then, we give a delay-sensitive energy-efficient MWPT (DEMWPT) construction method for single stripe repair, which reduces the energy cost while guaranteeing the minimum repair delay. We also propose the pipeline erasure coding aggregation repair (PECAR) for multiple stripe repair, which further reduces the repair delay by decentralizing the stripe synchronization process to aggregation nodes. Finally, we design a storagetransmission integrated simulation platform for LSDC and deploy the prototype of MWPT in it. The experimental results show that DE-MWPT can reduce the repair delay by 7.3% and energy cost by 6% compared with the existing aggregation repair policies, while PECAR can further reduce the repair delay by 43.9% compared to its ECAR counterpart

Satellite networks are emerging rapidly to serve mobile users and vehicles on the ground. To alleviate the downlink transmissions between moving satellites and ground users, [12] proposes to adopt coded caching in satellite networks. One key assumption in [12] is that, the users are assumed to be uniformly distributed on earth, which could be different from the reality. In this paper, we investigate coded caching in satellite networks with non-uniform user distribution. For arbitrary user distribution, we propose a decentralized coded caching scheme such that satellites can cooperatively serve the users. For the converse bound, basing on the concept of decoupling, we employ cut-set technique to characterize a novel lower bound. This converse bound is based on a novel request pattern construction in a cyclic wrap-around fashion which makes the users in a densely distributed region request more files. With the derived lower bound, we also prove that the upper bound of achievable rate is at most a constant gap from it. Numerical results demonstrates superior performance of our proposed algorithm in satellite networks.

With the rapid development of millions more edge base stations (EBSs) and billions of user devices in the future networks, the desire for low-energy system design and transmission strategy will be even more compelling. In this letter, we investigate the energy consumption issue of hierarchical cooperative caching networks (HCCNs) in which both EBSs and users are capable of storing content data in their local caches. A three-stage coded transmission strategy in HCCNs is proposed, and the closed expression of energy consumption of each stage is derived. In addition, we formulate a total energy consumption minimization problem with the constraint of EBSs’ cache sizes, and provide an algorithm to obtain the optimal cache placement matrix. We demonstrate by numerical results that, our optimized three-stage coded transmission strategy can achieve lower energy consumption than various multicast transmission strategies under the different users’ cache sizes.

Traffic load can be largely alleviated by content caching in densely deployed small-cell base stations (SBSs), which however require significant energy expenses for operation. In this letter, we investigate the cooperative activation and caching problem for dense small-cell networks with unknown user demands. A key issue is to make activation and caching decisions, which is proved to be NP-hard even if user demands are perfectly known. While existing works on joint optimization of activation and caching make decisions based on estimated user demands, we consider to learn activation and caching strategy in an online manner. We take into account the cooperation among SBSs. In particular, an active SBS that serves a user should share the observed rewards to the SBSs that are nearer to the user. In this way, an inactive SBS can keep learning the information of its surrounding users, and will become active again when its surrounding users have sufficient requests. The simulation results demonstrate the superior performance of the proposed strategy in terms of delay and energy consumption.

In clustered distributed storage systems (CDSSs), one of the main design goals is minimizing the transmission cost during the failed storage nodes repairing. Generalized regenerating codes (GRCs) are proposed to balance the intra‐cluster repair bandwidth and the inter‐cluster repair bandwidth for guaranteeing data availability. The trade‐off performance of GRCs illustrates that, it can reduce storage overhead and inter‐cluster repair bandwidths simultaneously. However, in practical big data storage scenarios, GRCs cannot give an effective solution to handle the heterogeneity of bandwidth costs among different clusters for node failures recovery. This paper proposes an asymmetric bandwidth allocation strategy (ABAS) of GRCs for the inter‐cluster repair in heterogeneous CDSSs. Furthermore, an upper bound of the achievable capacity of ABAS is derived based on the information flow graph (IFG), and the constraints of storage capacity and intra‐cluster repair bandwidth are also elaborated. Then, a metric termed global repair bandwidth cost (GRBC), which can be minimized regarding of the inter‐cluster repair bandwidths by solving a linear programming problem, is defined. The numerical results demonstrate that, maintaining the same data availability and storage overhead, the proposed ABAS of GRCs can effectively reduce the GRBC compared to the traditional symmetric bandwidth allocation schemes.