论文标题
与NOMA的机器类型通信基于快速授予学习的方法
Fast Grant Learning-Based Approach for Machine Type Communications with NOMA
论文作者
论文摘要
在本文中，我们提出了一个基于非正交的多重访问（NOMA）的通信框架，该框架允许机器类型设备（MTD）访问网络，同时避免交通拥堵。提出的技术是一种两步机制，该机制首先采用快速上行链路拨款安排设备，而无需向基站（BS）发送请求。其次，使用Noma配对以分布式方式使用以减少信号传导开销。由于在大规模场景中在BS上收集信息的能力有限，因此学习技术最适合此类问题。因此，采用多臂强盗学习来安排快速的授予MTD。然后，提出了约束的随机NOMA配对，有助于取消快速上行链路授予方案的两个主要挑战，即主动集预测和最佳调度。使用Noma，由于预测错误，我们能够显着减少资源浪费。此外，结果表明，根据可实现的复杂性，提出的方案可以轻松地获得不切实际的最佳OMA性能。
In this paper, we propose a non-orthogonal multiple access (NOMA)-based communication framework that allows machine type devices (MTDs) to access the network while avoiding congestion. The proposed technique is a 2-step mechanism that first employs fast uplink grant to schedule the devices without sending a request to the base station (BS). Secondly, NOMA pairing is employed in a distributed manner to reduce signaling overhead. Due to the limited capability of information gathering at the BS in massive scenarios, learning techniques are best fit for such problems. Therefore, multi-arm bandit learning is adopted to schedule the fast grant MTDs. Then, constrained random NOMA pairing is proposed that assists in decoupling the two main challenges of fast uplink grant schemes namely, active set prediction and optimal scheduling. Using NOMA, we were able to significantly reduce the resource wastage due to prediction errors. Additionally, the results show that the proposed scheme can easily attain the impractical optimal OMA performance, in terms of the achievable rewards, at an affordable complexity.
