Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Wireless networks QoS optimization using coded caching and machine learning algorithms

Proactive caching shows great potential to minimize peak traffic rates by storing popular data, in advance, at different nodes in the network. We study three new angles of proactive caching that were not covered before in the literature. We develop more practical algorithms that bring proactive caching closer to practical wireless networks. The first angle is where the popularities of the cached files are changing over time and the file delivery is asynchronous. We provide an algorithm that minimizes files’ delivery rate under this setting. We show that we can use the file delivery messages to proactively and constantly update the receiver finite caches. We show that this mechanism reduces the downloaded traffic of the network. The proposed scheme uses index coding [1], and app. A to jointly encodes the delivery of different demanded files with the cache updates to other receivers to follow the changes in the file popularities. An offline and online (dynamic) versions of the scheme are proposed, where the offline version requires knowledge of the file popularities across the whole transmission period in advance and the online one requires the file popularities for one succeeding time slot only. The optimal caching for both the offline and online schemes is obtained numerically. The second angle is the study of segmented caching for delay minimization in networks with congested backhaul. Studies have mainly focused on proactively storing popular whole files. For certain categories of files like videos, this is not the best strategy. As videos can be segmented, sending later segments of videos can be less time-critical. Video is expected to constitute 82% of internet traffic by 2020 [2]. We study the effect of segmenting video caching decisions under the assumption that the backhaul is congested. We provide an algorithm for proactive segmented caching that optimizes the choice of segments to be cached to minimize delay and compare the performance to the whole file proactive caching. The third angle focuses on using reinforcement learning for coded caching in networks with changing file popularities. For such a dynamic environment, reinforcement learning has the flexibility to learn the environment and adapt accordingly. We develop a reinforcement learning-based coded caching algorithm and compare its performance to rule-based coded caching