EWA-DA-based resource allocation method for 5G networks

In 5G networks, most D2D work focuses on optimizing system throughput without addressing economic constraints. This article encourages users to exchange resources and apply honest bidding tactics to attain high auction efficiency. This study describes an experience-weighted attraction double auction (EWA-DA) technique for allocating resources in 5G D2D networks. The suggested method classifies mobile users into seller cellular users (SCU) and buyer cellular users' (BCU). The SCU transmits the beacon packet, and the nearby cellular users create a D2D group in response to the beacon considered BCU. Then, the bid values of the buyer and seller cellular users are transmitted to the base station side. The BS uses an experience-weighted attraction-learning model to obtain accurate bidding prices. Lastly, an ask-to-mean-value double auction model was utilized instead of accurate bid values. Extensive simulation results demonstrate that the suggested strategy delivers excellent auction efficiency and fair earnings for both seller and buyer users. In addition, we identified that the participating users accomplished truthful bidding and used fewer resource blocks than existing techniques. The throughput maximization, economic issues and truthful bidding are the essential parameters for resource sharing between the cellular user (CU) and Device-to-Device (D2D) users in 5G networks. However, most of the literature on D2D focuses on optimizing system throughput without considering the economic issues and truthful bidding. Therefore, we are focusing on financial matters and truthful bidding along with system throughput optimization. This article also encourages users to trade their resources and achieve high auction efficiency with honest bidding strategies. This article presents an experience weighted attraction-double auction (EWA-DA)-based resource allocation method for 5G networks. The proposed method divides the cellular users into seller cellular users (SCU) and buyer cellular users (BCU). The SCU broadcasts the beacon packet, and the proximity cellular users responded immediately to the beacon considered BCU and formed a D2D group. The buyer and seller cellular users send their bid values to the base station side. The BS applies an experience weighted attraction-learning model for getting truthful bidding values. Finally, an ask to mean value double auction model was used over the truthful bid values. Extensive simulation results show that the proposed method achieves high auction efficiency and truthful profits for seller and buyer cellular users. With the obtained results, it was observed that the system throughput increased comparatively. We also observed that the participant users have achieved truthful bidding and utilized fewer resource blocks than existing methods image