Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020
With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity communication is regarded as a potential solution. It can use unlicensed bands to forward content to users under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition, deep learning-based methods almost need a lot of training time. Based on real-time requirements of the network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing (MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation.
Keywords: Opportunistic networks | MEC | Offloading | Content caching
(ReLBT): A Reinforcement learning-enabled listen before talk mechanism for LTE-LAA and Wi-Fi coexistence in IoT
(ReLBT): گوش دادن با قابلیت یادگیری تقویت قبل از مکانیسم گفتگو برای LTE-LAA و همزیستی Wi-Fi در اینترنت اشیا-2020
The emergence of Internet of Things (IoT) has increased number of connected devices and consequently transmitted traffic over the Internet. In this regard, Long Term Evolution (LTE) is growing its utilization in unlicensed spectrum as well, and Licensed Assisted Access (LAA) technology is one of the examples. However, unlicensed spectrum is already occupied by other wireless technologies, such as Wi-Fi. The diverse and dissimilar physical layer and medium access control (MAC) layer configurations of LTE-LAA and Wi-Fi lead to coexistence challenges in the network. Currently, LTE-LAA uses a listen-before-talk (LBT) mechanism, and Wi-Fi uses a carrier sense multiple access with collision avoidance (CSMA/CA) as a channel access mechanism. LBT and CSMA/CA are moderately similar channel access mechanisms. However, there is an efficient coexistence issue when these two technologies coexist. Therefore, this paper proposes a Reinforcement Learning-enabled LBT (ReLBT) mechanism for efficient coexistence of LTE-LAA and Wi-Fi scenarios. Specifically, ReLBT utilizes a channel collision probability as a reward function to optimize its channel access parameters. Simulation results show that the proposed ReLBT mechanism efficiently enhances the coexistence of LTE-LAA and Wi-Fi as compared to the LBT, thus improves fairness performance.
Keywords: Wi-Fi | Unlicensed band | LTE-LAA | LTE-LAA WiFi coexistence | Listen before talk (LBT)