Research on ultra-low ACLP wireless communication systems using multi-dimensional signal processing

00436239 (科研費)南山大学多次元信号処理による超低漏洩電力ワイヤレス通信システムの研究 2018～2021年度科学研究費助成事業 (基盤研究 (B) (一般)) 研究成果報告書33917 (科研費)202218H01434 (科研費)research repor

A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods

Deep Reinforcement Learning based Access Control for Disaster Response Networks

After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (Movable and Deployable Resource Unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In this kind of heterogeneous disaster response networks, it is of great importance to minimize the packet delay from user terminals by performing optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, which enables the smart relay selection and transmitting power control. We evaluate the performance by extensive simulations, and validate the superiority of the proposed mechanism by comparing with baseline schemes.</p