## 2 Literature Review
### Weak Signal Analysis for Emerging Trend Identification  
Emerging technologies in the complex landscape of 6G can have major economic impacts and affect strategic stability. The early detection of new technology trends is critical for strategic planning, as it enables users to identify opportunities and risks quickly and react to them accordingly by formulating appropriate research, development, and innovation strategies [35]. Yet, early identification of emerging technologies in large amounts of patent data remains challenging.  
Early research attempted to use co-word analysis to identify emerging research themes in information security by analyzing patterns and trends [43]. [PERSON] et al. [44] assessed the degree of topic association by considering the strength of topic linkages in multiple networks such as co-word and co-author networks. [PERSON] et al. [35] produced a semantic analysis method to extract terms involving products, functions, and technologies from patents. They constructed the technology road mapping according to the term structure and opinions of domain experts. [PERSON] et al. [45] presented a quantitative approach to discover potential future technological opportunities from the patent-citation network. Additionally, detecting weak signals, trends, and issues in an evolving technology landscape as early as possible has been proven to be an alternative solution for strategic planning [46]. In emerging topic research, weak signals can be regarded as the early stage of emerging topics, with earliness being their primary characteristic [47]. Weak signals are identified as keywords with low frequency but high growth potential. Analyzing weak signals of emerging topics is an early identification task for specific innovative content, which can help to identify emerging topics early and provide valuable insights into their subsequent development and evolution.  
Among past works about weak signal detection, many are keyword-based. Thus, portfolio maps, pioneered by the authors of [48], involve constructing Keyword Emergence Maps (KEMs) and Keyword Issue Maps (KIMs) based on two key metrics: Degree of Visibility (DoV) that quantifies the frequency of a keyword within a document set; and Degree of Diffusion (DoD) that measures the document frequency of each keyword. However, KEMs and KIMs present two major drawbacks: by focusing on keywords only, they can miss the context surrounding a weak signal, and the output is a single snapshot, which does not give clear clues of evolution over time. [PERSON] et al. [49] integrate LDA and Word2 Vec to detect weak signals in weakly structured data. Furthermore, [PERSON] et al. [50] introduce two functions for deep filtering metrics and the evolution of topics using numerous metrics to identify potential weak signals.  
However, traditional topic modeling methods have been useful for weak signal detection but have significant limitations. They rely on pre-set topic numbers, which makes them inflexible and require hard-to-obtain prior knowledge. Also, they cannot use the context-aware embeddings from modern pre-trained models, leading to less detailed analysis. Furthermore, these methods work statically, overlooking how weak signals change over time. In identifying emerging technological trends in 6G, weak signals, as the early signs of core technologies, may emerge or fade quickly over time. Ignoring these changes can cause important trends to be misinterpreted or missed. Therefore, inspired by WISDOM [51], we propose an enhanced DTM technique with a continuous weak signal analysis method to handle emerging trends in the fast-evolving technological landscape of 6G.