In this article, the authors proposed a novel blockchain-oriented location privacypreserving (BoLPP) for the Cooperative Spectrum Sensing (CSS) in 6G networks. In order to attain the sustainability of privacy and security for 6G wireless networks, it is a great challenge in this sensing as it faces various malicious attacks while the secondary user (SU) is active. To tackle these issues, the authors proposed a novel framework for blockchainoriented Cognitive Radio Networks (CRNs) for CSS using an energy detection technique. Moreover, the authors implemented another novel paradigm BoLPP, to attain the privacy of SUs location for CSS in 6G networks. This approach preserves the SUs' location and makes the BoLPP framework immune to all malicious attackers. The simulation results have been undergone based on the performance metrics such as response time, consistency, probability of false alarm, frame loss (%), average network throughput, energy efficiency, and security. The outcomes reveal that the proposed scheme achieves high security, privacy, energy efficiency, average network throughput, and low probability of false alarm and frame loss (%) when compared with the existing frameworks such as Friend or Foe (FoF) and Tidal Trust Algorithm (TTA) mechanisms. It is observed that the proposed BoLPP mechanism provides better security and privacy in 6G wireless networks.

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future 6G intelligent wireless networks.Load balancing presents an efficient, cost-effective method to improve the data process capability, throughput, and expand the bandwidth, thus enhancing the adaptability and availability of networks.Hence a load balancing algorithm based on Long Short Term Memory (LSTM) deep neural network is proposed through which the base stations coverage area changes according to geographic traffic distribution, catering the requirement for future generation 6G heterogeneous network. The LSTM models performance is evaluated by considering three different scenarios, and the results were presented.Load variance coefficient (LVC) and load factor (LF) are introduced and validated over two wireless network layouts (WNL) to study the Quality of Service (QoS) and load distribution. The proposed method shows a decrease of LVC by 98.311% and 99.21% for WNL1, WNL2 respectively.

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