Uplink Resource Allocation for Narrowband Internet of Things (NB-IoT) Cellular Networks

Yu, Ya-Ju; Wang, Jhih-Kai

doi:10.23919/apsipa.2018.8659711

Cited by 11 publications

(13 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 the spectral efficiency is simulated for different numbers of devices per cell. It can be noticed that DAL outperforms the RR; the acronym RR-Cq indicates a standard RR operating with a fixed RUC q (q = {1, 2, 3, 4}, see (9)- (13). Furthermore, the DAL provides adaptively the flexibility for selecting the configuration that fits better the different design criteria, i.e.…”

Section: Performance Evaluation and Simulation Resultsmentioning

confidence: 99%

“…For a single cell and non-adaptive RUC allocation. In [13] for single cell, as well, the authors have developed an adaptive allocator that minimizes the consumed resources. Finally, in [14], an adaptive RUC allocation has been used and, for addressing the energy efficiency optimization, the authors have proposed a heuristic algorithm composed of two steps: first, finding the configuration parameters that minimize energy consumption and satisfy QoS requirements and, second, optimizing these parameters in order to respect latency constraints.…”

Section: Related Work and The Novel Contributionsmentioning

confidence: 99%

“…Equ. (9)-(13) characterize the constraints on the 4 RUCs: (9) guarantees that only one value of v u c,q is 1, while the values from (12) and (13) force the total numbers of SCs and time-slots per user to be equal to one of the predefined RUC shapes. The following issues make this problem not solvable: (i) due to (3), i.e.…”

Section: A Rate Maximizationmentioning

confidence: 99%

See 2 more Smart Citations

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

Elgarhy

Reggiani

Malik

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

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%.

show abstract

Section: Performance Evaluation and Simulation Resultsmentioning

confidence: 99%

Section: Related Work and The Novel Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Elgarhy

Reggiani

Malik

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…Therefore, due to the assumed lack of nodes mobility, it not only reduces the amount of radio resources utilized in control channels but also eliminates the problem of handover between the evolved NodeBs (eNBs). It is worth mentioning that the maximal throughput reaches 200 kbps or 160-250 kbps [14,15] for uplink and downlink, respectively, and it is allocated only for the data transfer services.…”

Section: The Nb-iot Interfacementioning

confidence: 99%

Software-Defined NB-IoT Uplink Framework—The Design, Implementation and Use Cases

Olejniczak

Blaszkiewicz

Cwalina

et al. 2021

Sensors

View full text Add to dashboard Cite

In the radiocommunication area, we may observe a rapid growth of new technology, such as 5G. Moreover, all the newly introduced radio interfaces, e.g., narrowband Internet of Things (NB-IoT), are strongly dependent on the software. Hence, the radiocommunication software development and optimization, as well as the 3GPP technical specification, should be introduced at the academic level of education. In this paper, a software-defined NB-IoT uplink framework in the field of design is presented, as well as its realization and potential use cases. The framework may be used as an academic tool for developing, investigating, and optimizing the digital transmitter paths. The proposed realization is focused on the key elements in the physical layer of the NB-IoT interface used in the sensor devices. Furthermore, the paper also highlights the need of the data processing optimization to minimize the power consumption and usage of the resources of the NB-IoT node during transmitting gathered telemetric data.

show abstract

“…Fan et al [12] investigated to migrate virtual machines from green energy deprived cloudlets to green energy overprovisioning cloudlets to fully utilize the green energy in the network. However, all these works emphasize utilizing workload allocation among edge servers to enhance the user experience or energy efficiency of the network instead of focusing on resource allocation [13], [14].…”

Section: Related Workmentioning

confidence: 99%

Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning

Fan¹,

Bai²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Fog nodes in the vicinity of IoT devices are promising to provision low latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones. Owing to the time-varying channel conditions, traffic loads and computing loads, it is challenging to improve the quality of service (QoS) of mobile IoT devices. As task delay consists of both the transmission delay and computing delay, we investigate the resource allocation (i.e., including both radio resource and computation resource) in both the wireless channel and fog node to minimize the delay of all tasks while their QoS constraints are satisfied. We formulate the resource allocation problem into an integer non-linear problem, where both the radio resource and computation resource are taken into account. As IoT tasks are dynamic, the resource allocation for different tasks are coupled with each other and the future information is impractical to be obtained. Therefore, we design an on-line reinforcement learning algorithm to make the sub-optimal decision in real time based on the historical data. The performance of the designed algorithm has been demonstrated by extensive simulation results.

show abstract

Uplink Resource Allocation for Narrowband Internet of Things (NB-IoT) Cellular Networks

Cited by 11 publications

References 4 publications

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

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

Software-Defined NB-IoT Uplink Framework—The Design, Implementation and Use Cases

Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning

Contact Info

Product

Resources

About