Massive Machine-type Devices Research Articles

Energy efficiency and delay determinacy tradeoff in energy harvesting-powered zero-touch deterministic industrial M2M communications

Data transmission for Industrial Internet of Things (IIoTs) is of the utmost importance, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. On the one hand, as a key characteristic for Industry 5.0, deterministic transmission aims to ensure the data arrive at destination devices accurately, has attracted remarkable attentions. On the other hand, energy efficiency problem is another major concern in Industry 5.0 since the massive Machine-Type Devices (MTDs). Consequently, in this paper, we investigate the tradeoff between energy efficiency and delay determinacy in energy harvesting-powered Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) communications. In particular, we first derive the probability of the transmission delay falling within a certain time window to characterize the delay determinacy and then figure out the relationship between delay determinacy and the energy efficiency by taking the system control performance into account. After that, in order to model the uncertainty of the stochastic environment, we formulate the problem as a stochastic game by jointly considering transmission power control, frequency spectrum allocation and the selection of base stations. Afterwards, a random graph-based sparse Long Short-Term Memory (LSTM) network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates that the proposed sparse LSTM algorithm enables to achieve a specific delay determinacy with a higher energy efficiency as compared to Deep Q-Learning Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. In addition, we also analysis the influence of sparse coefficient and the size of time window on the relationship between energy efficiency and delay determinacy.

Early Collision Detection for Massive Random Access in Satellite-Based Internet of Things

As a complementary solution for seamless and ubiquitous coverage, satellite communications will play crucial roles in future global Internet of Things (IoT). Focusing on enhancing access efficiency and resource utilization of massive machine-type devices (MTDs), we propose an efficient collision detection scheme at the first step of random access (RA) procedure for satellite-based IoT. By leveraging a single root Zadoff-Chu (ZC) sequence with an elaborate set of cyclic shift offsets to generate all the available preamble sequences, the proposed scheme can achieve rapid collision detection and load estimation in one-shot correlation operation, while having the robustness to the non-orthogonal interference. The preamble detection probability, collision detection probability, and load monitoring accuracy, are mathematically analyzed, and an optimal set of preamble selection probabilities is given to maximize the overall load monitoring accuracy. Simulation results exhibit the remarkable performance improvement of our scheme in typical satellite line-of-sight (LOS) scenario, by compared to the state-of-the-art collision detection schemes.

A Comprehensive Distributed Queue-Based Random Access Framework for mMTC in LTE/LTE-A Networks With Mixed-Type Traffic

Thanks to their ubiquitous coverage, Long-Term Evolution (LTE) networks are considered the most potential enabler for massive Machine-Type Communications (mMTC) service in fifth-generation (5G) context. LTE standard, however, was not designed for mMTC and scenarios where the massive Machine-Type Devices (MTDs) population try to access a network over a short period may overload the Random Access CHannel (RACH). Furthermore, there is no mechanism to prioritize urgent MTDs in such overload situation. The baseline Access Class Barring (B-ACB) scheme is thus adopted by the 3GPP to address both issues at a substantial cost of access delay. This paper follows a different approach and proposes a complete solution to the two main issues of cellular mMTC. We promote the use of a mechanism called Distributed Queueing (DQ), aided by a MAC-layer load estimation technique, to effectively resolve contentions between the MTDs to improve delay performance with minimal impacts to LTE access procedure and air interface. Then, by exploiting information related to congestion level from the DQ process, a dynamic access prioritization scheme can be realized without additional signaling overhead. Computer simulation under an mMTC-oriented traffic model shows that our framework outperforms the B-ACB in terms of both access delay and energy consumption when all devices are of equal importance. On the other hand, when devices of different priorities coexist, our framework with proper tuning also offers lower delay for all classes and lower overall energy consumption compared to both the baseline and a dynamic ACB solutions in massive bursty access scenarios.