Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Abstract

Massive machine-type communication (mMTC) and ultrareliable and low-latency communication (URLLC) are two key service types in the fifth-generation (5G) communication systems, pursuing scalability and reliability with low-latency, respectively. These two extreme services are envisaged to agglomerate together into critical mMTC shortly with emerging use cases (e.g., wide-area disaster monitoring, wireless factory automation), creating new challenges to designing wireless systems beyond 5G. While conventional network slicing is effective in supporting a simple mixture of mMTC and URLLC, it is difficult to simultaneously guarantee the reliability, latency, and scalability requirements of critical mMTC (e.g., < 4ms latency, 106 devices/km 2 for factory automation) with limited radio resources. Furthermore, recently proposed solutions to scalable URLLC (e.g., machine learning aided URLLC for driverless vehicles) are ill-suited to critical mMTC whose machine type users have extremely limited energy budget and computing capability that should be tightly optimized for given tasks. In view of this, this paper aims to characterize promising use cases of critical mMTC and search for their possible solutions. To this end, we first review the state-of-theart (SOTA) technologies for separate mMTC and URLLC services and then identify key challenges from conflicting SOTA requirements, followed by potential approaches to prospective critical mMTC solutions at different layers. Index Terms-Ultra reliable low latency communication (URLLC), massive machine type communications (mMTC), critical mMTC, 5G, beyond 5G.

An Enhanced CoAP Scheme Using Fuzzy Logic With Adaptive Timeout for IoT Congestion Control

“…The RTT trend is defined simply as the difference between RTTs in consecutive intervals, i.e., t and t -1, as expressed in Eq. (12).…”

“…Although CoAP supports connection-oriented transmission, with modern communication standards, there remains a problem when a considerable number of IoTs are connected to a network. For instance, on an Massive Machine-Type Communications (mMTC) 5G network, the numbers can often reach 1 million connections per square meter [11], [12], while with standard ZigBee, as many as 65,536 devices are feasible [13]. This overwhelming number of connections leads to congestion issues when transmissions from the client node to the server are demanded concurrently.…”

5G NR FR2 Femtocell Coverage Map Using an Omnidirectional Twisted SWAA

“…Finally, we have allocated 12 DMRS (demodulation reference signal) symbols in each time slot, considering all 12 sub-carriers. Table I summarizes the main 5G NR variables considering one frame period, aiming the total throughput calculation using (2). A resource block (RB) is defined as the minimum resource allocation to one user and is composed of a time-frequency scheme of 1 frame and 12 sub-carriers.…”

“…OBILE communication systems have been evolving as the number of connected users increases and new applications are launched on the market [1]. Such reality, demands an evolution from the fourth generation (4G) to the fifth-generation (5G) of mobile communications, which relies not only on supplying more throughput to the users, but also enhanced connectivity, reliability, low-latency and management of a massive number of devices [2]. This plurality of features creates the need for a redesigned heterogeneous access network (HetNet) for attending multiple verticals, including industrial, commercial and residential applications [3].…”

Beamforming Techniques for Over-the-Air Computation in MIMO IoT Networks

“…As previously mentioned, the conventional AirComp techniques in the MIMO sensor network mainly exploited multiple transmit antennae for spatial multiplexing to increase the data rate or computation rate. However, in practice, most sensor networks are static, requiring high energy efficiency and reliable AirComp techniques (e.g., smart home, factory automation) [ 19 ]. From this perspective, we propose a framework for single-function computation in MIMO IoT sensor networks to improve the reliability of the computation result by exploiting multi-transmit antennae for spatial diversity and formulate an optimization problem with an intuitive approach from the equation of mean squared error (MSE), which represents the performance of function computation.…”