arXiv subjects

Ridong Li

Publications and source records attributed to Ridong Li.

3 recordsLinked to original sources

arXiv2026

Synergizing Global Pattern Learning and Time Order Characterization in Mobile Channel Prediction: An RWKV-Based Approach

Owing to the potential to reduce pilot overhead and mitigate channel aging, channel prediction is emerging as an important research topic in wireless communications. Meanwhile, deep neural networks are becoming a foundational technology for high-precision prediction thanks to their excellent non-linear representation capabilities. In this paper, we conceive a task-driven prediction network, which aims to deeply synergize the following two functions: learning global patterns for shareable features across adjacent time slots and structurally encoding time order to characterize the inherent causality within the channel dynamics. To implement channel prediction accuracy, we employ RWKV (receptance weighted key value) as network backbone and adapt it to the task's specific characteristics, utilizing its deep interleaved learning architecture to extract global patterns across multiple channel samples and leveraging its unique exponential decay to characterize temporal order. These task-driven unique designs significantly improve the learning efficiency of prediction network. Comprehensive experimental evaluations demonstrate the superiority of the proposed method over current data-driven methods, such as long short-term memory and Transformer, in the channel prediction task, including 1.84~4.29 dB gains in normalized mean squared error and 2.6~10.5 percentage point gains in cosine correlation.

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arXiv2025

Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.

eess.SP↗

arXiv2025

Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily on the scenario-dependent absolute data-label representations. Here, we propose analogical learning (AL), a learning framework that explores the inherent invariance of the underlying physical processes across scenarios, to improve the cross-scenario generalization. Specifically, we introduce the physical concepts of reference frames and relativity into the neural modeling. The resultant framework explicitly employs intra-scenario data-label pairs as reference anchors and enforces the network to mediate its data-to-label transformation through data-domain relative metrics that factor out the scenario-dependent variations. We instantiate AL with Mateformer, a bipartite Transformer-based neural architecture. Each layer of the auxiliary Transformer extracts certain feature space of the current data, while the corresponding layer of the primary Transformer computes attention among the data feature space and then use it as a relativity metric to weight the current label feature to synthesize the next label feature and, ultimately, the final prediction. We apply AL to intelligent wireless localization, a representative multi-scenario learning task. Across synthetic, real-world, and city-scale datasets, AL enables robust cross-scenario transfer and multi-scenario joint learning, achieving wavelength-scale localization accuracy that matches or surpasses state-of-the-art methods. This physics-inspired learning framework provides a promising alternative for other cross-scenario learning tasks and applications.

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