Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

Malik, Hassan; Alam, Muhammad Mahtab; Pervaiz, Haris; Moullec, Yannick Le; Al‐Dulaimi, Anwer; Pärand, Sven; Reggiani, Luca

doi:10.1109/mnet.001.1900087

Cited by 26 publications

(11 citation statements)

References 11 publications

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“…The simulated result of the algorithm shows an 8% growth rate in the cooperative scheme and a 17% decrease in energy expended as correlated to the non-cooperative scheme. The authors in [114] have highlighted some major RRM issues affecting maximization of spectral efficiency and device connectivity between UL and DL which result in underutilization of resources, particularly for asymmetric traffic distribution. Concern has also been raised about NB-IoT performance due to interference from multi-tier 5G HetNets.…”

Section: F Radio Resource Management (Rrm)mentioning

confidence: 99%

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Hancke

Abu‐Mahfouz

2020

IEEE Access

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Narrowband Internet of Things (NB-IoT) is a type of low-power wide-area (LPWA) technology standardized by the 3 rd-Generation Partnership Project (3GPP) and based on long-term evolution (LTE) functionalities. NB-IoT has attracted significant interest from the research community due to its support for massive machine-type communication (mMTC) and various IoT use cases that have stringent specifications in terms of connectivity, energy efficiency, reachability, reliability, and latency. However, as the capacity requirements for different IoT use cases continue to grow, the various functionalities of the LTE evolved packet core (EPC) system may become overladen and inevitably suboptimal. Several research efforts are ongoing to meet these challenges; consequently, we present an overview of these efforts, mainly focusing on the Open System Interconnection (OSI) layer of the NB-IoT framework. We present an optimized architecture of the LTE EPC functionalities, as well as further discussion about the 3GPP NB-IoT standardization and its releases. Furthermore, the possible 5G architectural design for NB-IoT integration, the enabling technologies required for 5G NB-IoT, the 5G NR coexistence with NB-IoT, and the potential architectural deployment schemes of NB-IoT with cellular networks are introduced. In this article, a description of cloud-assisted relay with backscatter communication, a comprehensive review of the technical performance properties and channel communication characteristics from the perspective of the physical (PHY) and medium-access control (MAC) layer of NB-IoT, with a focus on 5G, are presented. The different limitations associated with simulating these systems are also discussed. The enabling market for NB-IoT, the benefits for a few use cases, and possible critical challenges related to their deployment are also included. Finally, present challenges and open research directions on the PHY and MAC properties, as well as the strengths, weaknesses, opportunities, and threats (SWOT) analysis of NB-IoT, are presented to foster the prospective research activities.

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Section: F Radio Resource Management (Rrm)mentioning

confidence: 99%

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Hancke

Abu‐Mahfouz

2020

IEEE Access

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“…However this component is also coherent with the rate and, consequently, the SINR maximization principle, since the probability of retransmission or the number of prescribed repetitions are minimized when the SINR is maximized. Therefore, the problem formulation in (1) and 2is also addressing the minimization of the third component of the latency; in Sect. IV-D and V-C we will justify how this third component can be considered the dominant one in this study and we will present a sub-optimal strategy specifically devoted to its minimization.…”

Section: B Latency Minimizationmentioning

confidence: 99%

“…Internet of Things (IoT) is becoming a fundamental part of our society as it brings the digitalization in every sector of life; smart homes, smart cities, smart health care, smart agriculture are just some of the examples in which IoT is playing a crucial role [1], [2]. However, in such digitalized ecosystems, wireless IoT devices are expected to grow exponentially and this continuous process requires more efficient ways for using the available spectrum.…”

Section: Introductionmentioning

confidence: 99%

Rate-Latency Optimization for NB-IoT With Adaptive Resource Unit Configuration in Uplink Transmission

Elgarhy

Reggiani

Malik

et al. 2021

IEEE Systems Journal

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Narrowband Internet of Things (NB-IoT) is a cellular IoT communication technology standardized by 3rd Generation Partnership Project for supporting massive machine type communication and its deployment can be realized by a simple firmware upgrade on existing 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 sub-carrier 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 non-convex, NP-hard and combinatorial because of the use of binary variables. In this paper, after the formulation of the optimization problem, we study the resource allocation approach for NB-IoT networks aiming to analyze the trade-off between rate and latency. The proposed sub-optimal algorithm allocates radio resource (i.e. subcarriers) 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 sub-optimal 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%.

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“…The aim of flexible duplexing is to perform adaptive resource allocation considering the asymmetric uplink and downlink traffic and jointly optimize in the time-frequency domain, such that the distinction between TDD and FDD is blurred or even completely removed. In [177], the author presents radio resource allocation based on flexible duplexing to avoid inter-cell interference. Similarly, in [178], the author presents the potential benefits and the corresponding challenges for flexible duplexing in wireless networks.…”

Section: Radio Resource Schedulingmentioning

confidence: 99%

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

et al. 2020

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The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using

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Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

Cited by 26 publications

References 11 publications

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Rate-Latency Optimization for NB-IoT With Adaptive Resource Unit Configuration in Uplink Transmission

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

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