Performance Analysis of Direct Acyclic Graph-Based Ledgers in Low-to-High Load Regime

Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations

FedSC:A Sidechain-Enhanced Edge Computing Framework for 6G IoT Multiple Scenarios

The imminent deployment of sixth-generation (6G) wireless communication systems promises new opportunities and challenges for model training using data from edge devices in the Internet of Things (IoT). However, current research has yet to fully address the efficiency and scalability challenges arising from the extensive connectivity of edge devices across various scenarios. The presence of malicious devices further intensifies system uncertainty during large-scale data interactions and model training, making it difficult for a single model to effectively manage the complexities introduced by heterogeneous devices and dynamic network conditions. To overcome these challenges, we propose FedSC, an innovative edge computing framework that leverages side-chain technology for efficient edge node management and employs federated learning to enable robust cross-device and cross-scenario model interactions. To accelerate the multi-model aggregation process, we introduce an asynchronous cross-domain iterative algorithm (ACDI) based on smart contracts. Additionally, to mitigate the impact of malicious and inactive nodes, we propose a robust consensus algorithm and a committee mechanism for leader node election based on contribution value. Experimental results demonstrate that the proposed FedSC achieves a 3.2% and 44.23% accuracy improvement on i.i.d. and non-i.i.d. dataset, respectively, along with a remarkable latency reduction of 256.51%, compared to FedAvg. Our work is conducive to the training of multiple models in different IoT scenarios, utilizing substantial amounts of IoT device data and facilitating collaboration between models. Furthermore, it enables the provision of fundamental services to diverse applications in 6G

Joint Optimization Design for Near-Field IRS-Assisted Modular XL-MIMO Systems

Existing research on intelligent reflective surface (IRS) primarily focuses on far-field communication scenarios. Even in studies involving large-scale IRS-assisted systems, the far-field communication assumption remains prevalent, potentially leading to inaccurate evaluations of communication performance. To this end, the paper introduces a modularized extremely large-scale IRS (XL-IRS) and investigates its performance in supporting near-field communications. Specifically, a comprehensive channel model is developed for the near-field communication scenario assisted by the modularized XL-IRS. To maximize the weighted sum rate of the system, an alternating optimization algorithm is proposed to jointly optimize the transmit precoding at the base station and phase shift matrices of the modularized XL-IRS. We subsequently introduce an alternating optimization algorithm that produces a closed-form solution. Simulation results provide strong evidence supporting the proposed alternating optimization algorithm achieves satisfactory coverage and rate performance. Additionally, it can be observed that as the distance between modules increases, the system's weighted achievable sum rate decreases and that near-field users achieve a higher weighted sum rate compared to far-field users.<br/