NarrowBand IoT Data Transmission Procedures for Massive Machine Type Communications

Large-scale deployments of massive Machine Type Communications (mMTC) involve several challenges on cellular networks. To address the challenges of mMTC, or more generally, Internet of Things (IoT), the 3rd Generation Partnership Project has developed NarrowBand IoT (NB-IoT) as part of Release 13. NB-IoT is designed to provide better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower-energy consumption. NB-IoT reuses Long Term Evolution functionality with simplifications and optimizations. Particularly for small data transmissions, NB-IoT specifies two procedures to reduce the required signaling: one of them based on the Control Plane (CP), and the other on the User Plane (UP). In this work, we provide an overview of these procedures as well as an evaluation of their performance. The results of the energy consumption show both optimizations achieve a battery lifetime extension of more than 2 years for a large range in the considered cases, and up to 8 years for CP with good coverage. In terms of cell capacity relative to SR, CP achieves gains from 26% to 224%, and UP ranges from 36% to 165%. The comparison of CP and UP optimizations yields similar results, except for some specific configurations.This work is partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (Projects TIN2013-46223-P, and TEC2016-76795- C6-4-R), and the Spanish Ministry of Education, Culture and Sport (FPU Grant 13/04833)

Analytic Analysis of Narrowband IoT Coverage Enhancement Approaches

The introduction of Narrowband Internet of Things (NB-IoT) as a cellular IoT technology aims to support massive Machine-Type Communications applications. These applications are characterized by massive connections from a large number of low-complexity and low-power devices. One of the goals of NB-IoT is to improve coverage extension beyond existing cellular technologies. In order to do that, NB-IoT introduces transmission repetitions and different bandwidth allocation configurations in uplink. These new transmission approaches yield many transmission options in uplink. In this paper, we propose analytical expressions that describe the influence of these new approaches in the transmission. Our analysis is based on the Shannon theorem. The transmission is studied in terms of the required Signal to Noise Ratio, bandwidth utilization, and energy per transmitted bit. Additionally, we propose an uplink link adaptation algorithm that contemplates these new transmission approaches. The conducted evaluation summarizes the influence of these approaches. Furthermore, we present the resulting uplink link adaptation from our proposed algorithm sweeping the device's coverage.Comment: Accepted in the 2018 Global IoT Summit (GIoTS) conferenc

SNR Gain Evaluation in Narrowband IoT Uplink Data Transmission with Repetition Increment: A Simulation Approach

Deploying Internet of Things (IoT) on a large scale necessitates widespread network infrastructures supporting Machine Type Communication. Integrating IoT into cellular networks like LTE, known as Narrowband-IoT (NB-IoT), can fulfill this infrastructure need. Standard 3GPP Release 13 introduces NB-IoT's Repetition features, expanding radio transmission coverage while maintaining LTE performance. Focusing on uplink data traffic, this study examines NB-IoT's repetition mechanism, grid resource distribution, and NPUSCH performance through simulations. Results show that at SNR greater than -5 dB, maximum repetitions of 128 yield the highest BLER, while minimum repetitions of 2 result in the lowest. Quadrupling repetitions increases SNR by 5 dB, emphasizing repetition's role in error mitigation and uplink reliability, especially in challenging SNR conditions. For optimal throughput in SNR above -5 dB, maximum repetitions of 128 for NPUSCH format 1 are recommended. These findings underscore the importance of repetition in enhancing Narrowband IoT performance, offering insights for system optimization, where increasing the number of repetitions generally leads to higher SNR gain. The attained BLER and throughput values from Narrowband IoT simulations highlight the robustness of data transmission across varying channel conditions, affirming NB-IoT applicability to a wide range of IoT applications

Rate-latency optimization for NB-IoT with adaptive resource unit configuration in uplink transmission

Narrowband Internet of Things (NB-IoT) is a cellular IoT communication technology standardized by 3rd Generation Partnership Project (3GPP) for supporting massive machine type communication and its deployment can be realized by a simple firmware upgrade on existing long term evolution (LTE) networks. The NB-IoT requirements in terms of energy efficiency, achievable rates, latency, extended coverage, make the resource allocation, in a limited bandwidth, even a more challenging problem w.r.t. to legacy LTE. The allocation, done with subcarrier (SC) granularity in NB-IoT, should maintain adequate performance for the devices while keeping the power consumption as low as possible. Nevertheless, the optimal solution of the resource allocation problem is typically unfeasible since nonconvex, NP-hard and combinatorial because of the use of binary variables. In this article, after the formulation of the optimization problem, we study the resource allocation approach for NB-IoT networks aiming to analyze the tradeoff between rate and latency. The proposed suboptimal algorithm allocates radio resource (i.e., SCs) and transmission power to the NB-IoT devices for the uplink transmission and the performance is compared in terms of latency, rate, and power. By comparing the proposed allocation to a conventional round robin (RR) and to a brute-force approach, we can observe the advantages of the formulated allocation problem and the limited loss of the suboptimal solution. The proposed algorithm outperforms the RR by a factor 2 in terms of spectral efficiency and, moreover, the study includes techniques that reduce the dropped packets from 29% to 1.6%