Energy Efficient Data Rate Enhancement Channel Coding Technique for Narrowband Internet of Things (NB-IoT)

Migabo, Emmanuel; Djouani, Karim; Kurien, Anish Mathew

doi:10.1109/africon51333.2021.9570868

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In [16], a survey of techniques that target energy consumption models in two specific examples (agriculture and health) was presented. A different approach was presented in [17], where channel coding was used to improve energy consumption and rate. For a D2D system, the authors of [18] used reinforcement learning aided by federated learning to minimize the power consumption and maximize the sum rate through resource allocation.…”

Section: Related Work and Novel Contributionsmentioning

confidence: 99%

Energy Efficiency and Latency Optimization for IoT URLLC and mMTC Use Cases

Elgarhy,

Reggiani,

Alam

et al. 2024

IEEE Access

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The Internet of Things (IoT) is an essential part of 5G, Beyond-5G (B5G), and 6G systems; it has several applications in two of the principal 5G use cases, namely ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), and in their successors within B5G and 6G: extreme ultra-reliable low-latency communication (eURLLC) and ultra-massive machine-type communication (umMTC). IoT systems, which are characterized by narrow bandwidths, have stringent requirements owing to the specific nature of their applications and use cases. The purpose of this study is to investigate and jointly optimize the energy efficiency (EE) and latency through resource allocation for IoT cellular systems. With regard to the contributions, in this study we investigated the optimization of EE in narrowband IoT systems, compared resource unit configurations (RUCs), jointly formulated the optimization of EE and latency, and introduced a suboptimal but efficient algorithm. More precisely, as the EE performance of various resource unit configurations has not been exhaustively investigated in the current state of the art, we analyzed and compared the EE of RUCs. The results show vast differences in performance between RUCs. For example, in terms of EE, the best RUC has an EE more than 80 times higher than the worst, which illustrates the importance of this investigation. We then proposed a scheduler based on the shortest job first (SJF) for minimum latency allocation, and another scheduler based on a joint evaluation of EE and latency. With respect to conventional techniques, these schedulers achieve a better trade-off between latency reduction and gain in terms of EE for a wider range of parameter configurations in multi-cellular layouts. The study demonstrates that in the presence of repetitions, algorithms that achieve high EE will mostly achieve low latency. INDEX TERMS Energy efficiency, latency, resource allocation, uplink scheduling I. INTRODUCTIONCurrently, the Internet of Things (IoT) is one of the main components of modern wireless communication paradigms. There are three main 5G services: massive machine-type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced mobile broadband (eMBB). Two of these services are related to IoT: mMTC, which is for IoT devices and high-density connections, and URLLC, which serves industrial and mission-critical IoT. These services will not only remain in Beyond-5G (B5G) and 6G, but will also evolve into ultra-massive machine-type communication (umMTC) and extreme ultra-reliable low-latency communication (eURLLC). In addition to the requirements of these services, IoT devices often impose additional power consumption requirements. Therefore, the resource allocation should be designed to target multiple performance indicators. This is in line with the trend for B5G and 6G, because these systems are expected to have a mix of the three services rather than only one service at a time [1]; for example, merging the requirements of reliable l...

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Section: Related Work and Novel Contributionsmentioning

confidence: 99%

Energy Efficiency and Latency Optimization for IoT URLLC and mMTC Use Cases

Elgarhy,

Reggiani,

Alam

et al. 2024

IEEE Access

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Improving NB-IoT performance in weak coverage areas with CBSTO polar coding and LMMSE channel estimation

Bavani,

Srinivasan,

Balasubadra

et al. 2024

Peer-to-Peer Netw. Appl.

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Energy-Saving Solutions for Cellular Internet of Things–A Survey

et al. 2022

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The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energysaving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport-or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

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Energy Efficient Data Rate Enhancement Channel Coding Technique for Narrowband Internet of Things (NB-IoT)

Cited by 4 publications

References 9 publications

Energy Efficiency and Latency Optimization for IoT URLLC and mMTC Use Cases

Energy Efficiency and Latency Optimization for IoT URLLC and mMTC Use Cases

Improving NB-IoT performance in weak coverage areas with CBSTO polar coding and LMMSE channel estimation

Energy-Saving Solutions for Cellular Internet of Things–A Survey

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