Junyi Jiang | Searcharxiv

arXiv2025

A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization

The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025. To understand the practical and fundamental values of 6G and facilitate its standardization, it is crucial to explore the theoretical limits of spectrum, energy, and coverage efficiency considering practical hardware and signaling constraints. In this paper, we present a mean-field-approximation-based investigation on two out of six use case scenarios defined by IMT-2030, namely, massive communication and ubiquitous connectivity. Being aware of the limitation in interference cancellation owing to constrained cost and hardware complexity, we investigate the spectrum reuse architecture in both usage scenarios. We propose a tractable spectrum reuse with low signaling overhead consumed for channel estimation and channel state information (CSI) feedback. Our analysis indicates that the massive communication over cellular and device-to-device (D2D) networks can benefit from channel orthogonalization, while it is unnecessary to share the CSI of interfering links. Moreover, deploying relays or movable base stations, e.g. unmanned aerial vehicle, yields substantial energy and spectrum gain for ubiquitous connectivity, despite introducing interference. As such, the mean-field-optimization-based evaluation is expected to positively impact 6G and NextG standardization in 3GPP and other standardization bodies.

eess.SP↗

arXiv2024

Large Language Model Simulator for Cold-Start Recommendation

Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue. Current models generate synthetic behavioral embeddings from content features but fail to address the core issue: the absence of historical behavior data. To tackle this, we introduce the LLM Simulator framework, which leverages large language models to simulate user interactions for cold items, fundamentally addressing the cold-start problem. However, simply using LLM to traverse all users can introduce significant complexity in billion-scale systems. To manage the computational complexity, we propose a coupled funnel ColdLLM framework for online recommendation. ColdLLM efficiently reduces the number of candidate users from billions to hundreds using a trained coupled filter, allowing the LLM to operate efficiently and effectively on the filtered set. Extensive experiments show that ColdLLM significantly surpasses baselines in cold-start recommendations, including Recall and NDCG metrics. A two-week A/B test also validates that ColdLLM can effectively increase the cold-start period GMV.

cs.IR↗