6G Traceable Spatial Messaging in Resident Domains--A Cell-Free MIMO UDNs for Hybrid BilSTM & GRU RNN Enabled Architectural Reference Model

In the era of 6G, a game-changing approach will redefine the communications and networks depending on the required services. A large volume of geo-tagged data can be fundamental to providing applications of location based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and reports its up-to-date query result. In order to support new requirements and services, mature technologies are needed to embed such as, Artificial intelligence (AI) and Machine Learning (ML).
There are several algorithms for text classification, ranging from ML to Deep Learning (DL). Since the advent of the high-end computational facility (HiPC), numerical crunching has become much easier with lesser computational time. This has paved way for evolution of Complicated Network Architecture (CAN) which can be trained to achieve
Higher Accuracy, Precision and Recall (HiAP&R). A cumulative performance of HiAP&R proportionately affects the F1 score based on which the performance of the Neural Network (NN) model can be assessed. The present work attempts to explore the proposed neural network, Hybrid RNN model with two BiLSTM layers and two GRU layer
and compare the performance with other hybrid models. We exposed our results for Unsupervised Learning (UL) includes Hierarchical Clustering (HC), Partitioned Clustering (PC), Association Rule Mining (ARM), and Dimensionality Reduction (DR). Supervised Learning (SL) is comparatively explored to cover decision tree, K-nearest neighbouring, and Support Vector Machine (SVM). 6G use cases, requirements, and key enabling techniques are discussed in Reinforcement Learning (RL), both Model-Based Approaches (MBA) and Model-Free Approaches (MFA) are investigated. Deep learning is improvised to tailor 6G communication Social Media Data Volume (SMDV) from a perceptron to neural networks, convolutional neural networks, and recurrent neural networks. The GloVE dataset is employed to train the models, and their performance is evaluated by accuracy, precision, recall and F1-score. The performance of the proposed models is compared with other models by using
F1 score. We expect that our results are useful for researchers and technicians seeking an optimal, sub-optimal, or trade-off solution for each B5G communications and 6G networks problem using AI techniques. E.g., 6G use case proposed here AI with GRU and RCNN trade-offs makes communications and networks design and management smarter and safer. Key question is not about whether but when and how to implement AI in 6G communication systems.