The proposed AI-driven data-analytics-based spectrum allocation (ADASA) algorithm effectively improved spectrum utilization in heterogeneous wireless networks in simulation results.
An AI-driven data-analytics-based spectrum allocation algorithm improves spectrum utilization in simulated heterogeneous wireless networks.
Rapidly growing wireless networks are facing spectrum shortages, so how to improve spectrum utilization becomes critical. The rise of artificial intelligence (AI) technologies can provide a more intelligent and effective strategy for realizing cognitive wireless communication to improve spectrum utilization. Therefore, this article uses AI technology for data analytics, and combines cognitive technology to perform dynamic spectrum allocation. In terms of data analytics, AI technology is utilized in both feature extraction and data dimensionality, and the data correlation calculation between users. Then the data analytics results are applied to the spectrum allocation. Combined with deep learning, an AI-driven data-analytics-based spectrum allocation (ADASA) algorithm is proposed. ADASA enables the adaptive adjustment of the allocation parameters according to the network environmental status when allocating spectrum to users. Finally, the simulation results prove that the proposed ADASA algorithm can effectively improve the spectrum utilization in heterogeneous wireless networks.
Lin et al. (Sat,) conducted a other in Spectrum shortages in heterogeneous wireless networks. AI-driven data-analytics-based spectrum allocation (ADASA) algorithm was evaluated on Spectrum utilization. The proposed AI-driven data-analytics-based spectrum allocation (ADASA) algorithm effectively improved spectrum utilization in heterogeneous wireless networks in simulation results.