Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open-source along with a lightweight data visualization platform.Comment: Accepted for publication in IEEE Communications Magazine (Internet of Things and Sensor Networks Series

Channel equalization and interference analysis for uplink Narrowband Internet of Things (NB-IoT)

We derive the uplink system model for In-band and Guard-band narrowband Internet of Things (NB-IoT). The results reveal that the actual channel frequency response (CFR) is not a simple Fourier transform of the channel impulse response, due to sampling rate mismatch between the NB-IoT user and Long Term Evolution (LTE) base station. Consequently, a new channel equalization algorithm is proposed based on the derived effective CFR. In addition, the interference is derived analytically to facilitate the co-existence of NB-IoT and LTE signals. This work provides an example and guidance to support network slicing and service multiplexing in the physical layer

Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.Comment: Submitted for conference publicatio

Architectures and Key Technical Challenges for 5G Systems Incorporating Satellites

Satellite Communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas. This aspect is reflected by recent commercial and standardisation endeavours. In particular, 3GPP recently initiated a Study Item for New Radio-based, i.e., 5G, Non-Terrestrial Networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios. However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realisation of a satellite-based NR network. In this paper, based on the architecture options currently being discussed in the standardisation fora, we discuss and assess the impact of the satellite channel characteristics on the physical and Medium Access Control layers, both in terms of transmitted waveforms and procedures for enhanced Mobile BroadBand (eMBB) and NarrowBand-Internet of Things (NB-IoT) applications. The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular Random Access (RA), Timing Advance (TA), and Hybrid Automatic Repeat reQuest (HARQ) and, depending on the considered service and architecture, different solutions are proposed.Comment: Submitted to Transactions on Vehicular Technologies, April 201

Evaluation, Modeling and Optimization of Coverage Enhancement Methods of NB-IoT

Narrowband Internet of Things (NB-IoT) is a new Low Power Wide Area Network (LPWAN) technology released by 3GPP. The primary goals of NB-IoT are improved coverage, massive capacity, low cost, and long battery life. In order to improve coverage, NB-IoT has promising solutions, such as increasing transmission repetitions, decreasing bandwidth, and adapting the Modulation and Coding Scheme (MCS). In this paper, we present an implementation of coverage enhancement features of NB-IoT in NS-3, an end-to-end network simulator. The resource allocation and link adaptation in NS-3 are modified to comply with the new features of NB-IoT. Using the developed simulation framework, the influence of the new features on network reliability and latency is evaluated. Furthermore, an optimal hybrid link adaptation strategy based on all three features is proposed. To achieve this, we formulate an optimization problem that has an objective function based on latency, and constraint based on the Signal to Noise Ratio (SNR). Then, we propose several algorithms to minimize latency and compare them with respect to accuracy and speed. The best hybrid solution is chosen and implemented in the NS-3 simulator by which the latency formulation is verified. The numerical results show that the proposed optimization algorithm for hybrid link adaptation is eight times faster than the exhaustive search approach and yields similar latency

Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method