Novel Dynamic Fairness-aware Auction for Enhanced Licensed Shared Access in 6G Networks
A new approach addressing the spectrum scarcity challenge in 6G networks by implementing an enhanced licensed shared access (LSA) framework is considered. The proposed mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism in which the determination of weights is based on the results of the previous auctions. By comparing with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. However, the lack of interference management between incumbents as primary license holders and MNOs as secondary users leads to the reduced willingness of MNOs and incumbents to contribute to the LSA framework. To overcome this, the study suggests using spectrum sensing and integrating UAV-based networks to enhance the efficiency of the LSA system. This research employs two methods to solve the problem, as the first method we propose a novel greedy algorithm, named market share-based weighted greedy algorithm (MSWGA) to achieve better fairness compared with traditional auction methods and in the second approach, we exploit deep reinforcement learning (DRL) algorithms, specifically the deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) methods, to optimize the auction policy and demonstrate its superiority over other methods such as DRL-based solutions. Simulation results show that the DDPG method is better than the SAC, MSWGA, and greedy methods. Moreover, the fairness the index demonstrates a notable enhancement of 15% and 20% with the utilization of the MSWGA, and DDPG methods, respectively, compared with the traditional auction methods.
Here is the code for the paper "Novel Fairness-aware and Dynamic Auction for Enhanced Licensed Shared Access in 6G Networks ", in which three methods are considered, Greedy, DDPG, and SAC. To run the code you need to install; Pytorch, Numpy, OS.