Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs to cellular users. This interference challenge stems from the fact that the acquisition of channel state information (CSI) from machine type devices (MTD) to cellular base stations (BS) is infeasible due to the small packet nature of MTC traffic. In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between MTC and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. The BS and MTA share the same uplink resources, and, therefore, MTD transmissions create interference on the BS. However, if there is a large number of MTDs to chose from for transmission at each given time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such an interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. To further investigate such interference, the distribution of the signal-to-interference-plus-noise ratio (SINR) of the cellular user is derived, and, subsequently, the distribution of the outage probability is presented. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. The receive beamformer is used as the context of the contextual MAB setting and Thompson sampling: a well-known method of solving contextual MAB problems is proposed. Since the number of contexts in this setting can be unlimited, approximating the posterior distributions of Thompson sampling is required. Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS. Linear full posterior sampling achieves almost 90% of the optimal allocation when the CSI from all the MTDs to the BS is known.

中文翻译：

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多天线系统零空间中机器类型通信的上下文 Bandit 学习

由于 MTC 对蜂窝用户的干扰，确保传统宽带蜂窝用户与机器类型通信 (MTC) 的有效共存具有挑战性。这种干扰挑战源于这样一个事实，即由于 MTC 业务的小分组特性，从机器类型设备 (MTD) 到蜂窝基站 (BS) 获取信道状态信息 (CSI) 是不可行的。在本文中，提出了一种基于机会空间正交化 (OSO) 概念的新方法，用于 MTC 和传统蜂窝通信之间的干扰管理。特别地，蜂窝系统被认为具有多天线 BS，其中接收波束成形器被设计为最大化蜂窝用户的速率，以及从大量 MTD 接收数据的机器类型聚合器 (MTA)。BS 和 MTA 共享相同的上行链路资源，因此，MTD 传输会对 BS 产生干扰。然而，如果对于每个波束形成器在每个给定时间有大量的 MTD 可供选择用于传输，则可以选择一个 MTD，使得它几乎不会对 BS 造成干扰。对来自同一波束形成器上的多个 MTD 的此类干扰的特征进行了全面的分析研究。已经证明，对于每个波束形成器，存在一个 MTD，使得对 BS 的干扰可以忽略不计。为了进一步研究这种干扰，我们推导出了蜂窝用户的信干噪比 (SINR) 的分布，然后给出了中断概率的分布。然而，OSO 的最优实现需要 BS 中所有链路的 CSI，这对 MTC 来说是不切实际的。为了解决这个问题，提出了一种基于上下文多臂老虎机（MAB）学习概念的在线学习方法。接收波束成形器用作上下文 MAB 设置和 Thompson 采样的上下文：提出了一种众所周知的解决上下文 MAB 问题的方法。由于此设置中的上下文数量可以是无限的，因此需要近似 Thompson 采样的后验分布。提出了两种函数逼近方法，a) 线性全后验采样，和 b) 神经网络，用于为给定波束形成器传输的 MTD 的最佳选择。仿真结果表明，可以在没有 CSI 的情况下实现从 MTD 到 BS 的 OSO。