Efficient Resource Management in 6G for Sustainable Spectrum Allocation Using Dual‐Channel Temporal Convolutional Frequency‐Separated Attention Network with Generation Rights Trading Blockchain

ABSTRACT The need for effective spectrum allocation to guarantee highly reliable, minimal‐latency, and sustainable connections has increased due to the exponential expansion of wireless communications and the introduction of 6G technologies. In order to cope with these challenges, the proposed research is Efficient Resource Management in 6G to Sustainable Spectrum Allocation using a Dual‐Channel Temporal Convolutional Frequency‐Separated Attention Network with Generation Rights Trading Blockchain (ERMSA‐6G). This objective is achieved by means of a layered network model that combines spectrum sensing, dynamic allocation and security. The Communication Layer is based on a Generation Rights Trading Blockchain (GRT‐ BC ) with a smart contract algorithm to allocate resources transparently, and the Security Layer uses an Ultra‐Lightweight Encryption Algorithm (ULwEA). This is improved by the use of the Dual‐Channel Temporal Convolutional Network (DCTCN) of short‐ and long‐term demand trends, and Frequency‐Separated Attention Network (FSAN) of fine‐tuning attention modeling, collectively to create the Dual‐Channel Temporal Convolutional Frequency‐Separated Attention Network (DCTCN‐FSAN). Hyperparameters are further tuned using Cleaner Fish Optimization (CfO), and verified results are stored on GRT‐ BC . Experimental results demonstrate that ERMSA‐6G achieves a 99.18% spectrum efficiency, 99.44% blockchain security, a 29.8‐dB signal‐to‐interference‐plus‐noise ratio (SINR) margin, and an 85‐ms consensus delay, while reducing transaction and servicing latency to 2060 ms. Its statistical tests prove that it is significantly better than the current techniques. To sum up, ERMSA‐6G provides a secure, transparent, and sustainable framework of 6G spectrum allocation, which is more effective than the state‐of‐the‐art techniques and guarantees scalability, reliability, and efficiency in future wireless networks.