Optimization of Random Access Channel in NB-IoT

Harwahyu, Ruki; Cheng, Ray-Guang; Wei, Chia-Hung; Sari, Riri Fitri

doi:10.1109/jiot.2017.2786680

Cited by 82 publications

(52 citation statements)

References 14 publications

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“…2) Related works on optimization in NB-IoT: In NB-IoT networks, most existing studies either focused on the resource allocation during RACH procedure [13,14], or that during the data transmission [15,16]. For RACH procedure, the access success probability was statistically optimized in [13] using exhaustive search, and the authors in [14] studied the fixed-size data resource scheduling for various resource requirements.…”

Section: Introductionmentioning

confidence: 99%

“…For the data transmission, [15] presented an uplink data transmission time slot and power allocation scheme to optimize the overall channel gain, and [16] proposed a link adaptation scheme, which dynamically selects modulation and coding level, and the repetition value according to the acknowledgment/negativeacknowledgment feedback to reduce the uplink data transmission block error ratio. More importantly, these works ignore the time-varied heterogeneous traffic of massive IoT devices, and considered a snap shot [13,15,16] or steady-state behavior [14] of NB-IoT networks. Our most relevant work is [17], where the authors studied the steady-state behavior of NB-IoT networks from the perspective of a single device.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

Jiang

Deng

Nallanathan

et al. 2019

IEEE J. Select. Areas Commun.

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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 resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency. Partnership Project (3GPP) partners have standardized a new radio access technology, namely NarrowBand-IoT (NB-IoT) [1]. NB-IoT is expected to provide reliable wireless access for IoT devices with various

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

Jiang

Deng

Nallanathan

et al. 2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

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“…Moreover, 3GPP recognizes the importance of this technology describing the open issues to be addressed during the future 5G standardization process [7]. To address these points, continuously growing efforts were made, mainly over the last two years: radio resource optimization of physical channels such as NPRACH [8,9], energy efficient uplink scheduling schemes [4,10], among others.…”

Section: Introductionmentioning

confidence: 99%

Extreme Coverage in 5G Narrowband IoT: A LUT-Based Strategy to Optimize Shared Channels

Luján

Mellino

Otero

et al. 2020

IEEE Internet Things J.

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One of the main challenges in IoT is providing communication support to an increasing number of connected devices. In recent years, narrowband radio technology has emerged to address this situation: Narrowband Internet of Things (NB-IoT), which is now part of 5G. Supporting massive connectivity becomes particularly demanding in extreme coverage scenarios such as underground or deep inside buildings sites. We propose a novel strategy for these situations focused on optimizing NB-IoT shared channels through the selection of link parameters: modulation and coding scheme, as well as the number of repetitions. These parameters are established by the base station (BS) for each block transmitted until reaching a target block error rate (BLERt). A wrong selection of these magnitudes leads to radio resource waste and a decrease in the number of possible concurrent connections. Specifically, our strategy is based on a look-up table (LUT) scheme which is used for rapidly delivering the optimal link parameters given a target QoS. To validate our proposal, we compare with alternative strategies using an open source NB-IoT uplink simulator. The experiments are based on transmitting blocks of 256 bits using an AWGN channel over the NPUSCH. Results show that, especially under extreme conditions, only a few options for link parameters are available, favoring robustness against measurement uncertainties. Our strategy minimizes resource usage in all scenarios of acknowledged mode and remarkably reduces losses in the unacknowledged mode, presenting also substantial gains in performance. We expect to influence future BS software design and implementation, favoring connection support under extreme environments.

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“…Other LPWA technologies such as Weightless-N, Weightless-P from Weightless SIG and RPMA (Random Phase Multiple Access technology) from Ingenu are also commercially deployed and used to support specific vertical use cases. There are also several new 3GPP standards such as EC-GSM, LTE-M and NB-IoT [1] that are currently being specified to enable future 3GPP networks to support the specific requirements and use cases of the fast growing IoT markets in upcoming years.…”

Section: Introductionmentioning

confidence: 99%