Intelligent resource allocation maintains a better quality of service among devices in next-generation heterogeneous network infrastructures (NG-HetNetIs). NG-HetNetIs include industry 5.0 enabled infrastructures like Internet of Things (IoT), cognitive radio (CR) enabled B5G and 6G networks, unmanned aerial vehicles (UAVs), wireless sensor networks (WSNs) and autonomous vehicles (AVs). Digital twin (DT) joins hand with cognitive radio and resource aggregation technologies to provide the integrated framework for intelligent resource allocation in NG-HetNetIs. In NG-HetNetIs, the obtained statistics of measured radio activity as prior information play an instrumental role in enabling optimized resource allocation using context awareness. Unfortunately, the already available static approaches are inefficient to replicate (DT) the radio activity in a heterogeneous radio environment. To address the issue, static implementation framework is extended as dynamic radio activity characterization framework (DRAC) to have context awareness in NG-HetNetIs. The proposed DRAC replicates (DT) the wide sense stationarity of time and carrier aggregated radio activity due to its exploitation of more localized temporal and spectral information in NG-HetNets. The obtained localized statistics using DRAC can be exploited as appropriate prior knowledge and test statistics during the spectrum sensing phase of NG-HetNetIs for intelligent resource allocation instead of a single statistic obtained by the static approach.

peer reviewed