Data Channel Design and Performance for LTE Narrowband IoT

Ratasuk, Rapeepat; Mangalvedhe, Nitin; Kaikkonen, Jorma; Robert, Michel

doi:10.1109/vtcfall.2016.7880951

Cited by 34 publications

(24 citation statements)

References 3 publications

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“…We consider that the RU configuration with 12 subcarriers × 2 time slots is always selected for every IoT device [38]. Therefore, only one IoT device can be scheduled for transmission at the same time.…”

Section: Assumption 1 (Resource Unit (Ru) Configuration In Nb-iot)mentioning

confidence: 99%

“…Assumption 2 (Link adaption in NB-IoT): In LTE system, link adaptation is performed dynamically per 1ms subframe to adapt the Modulation and Coding Scheme (MCS) level according to the instantaneous channel quality. As a narrowband transmission technology with a relatively low data rate, the transmission of a transport block (TB) in NB-IoT can occupy multiple consecutive subframes, i.e., a TB may be mapped to {1, 2, 3, 4, 5, 6, 8, 10} RUs in time [38]. This means that the transmission duration can be larger than the coherence time of the wireless channel.…”

Section: Assumption 1 (Resource Unit (Ru) Configuration In Nb-iot)mentioning

confidence: 99%

“…s k ← f (s k , a k ) by (5) for each IoT device n = 1, 2, · · · , N dõ s n,k ← f n (s n,k , a k ) by (15) end for for each IoT device n = 1, 2, · · · , N do Update the per-node value function V n,k (s n,k−1 ) by (29), (30), (31) Update the f-weight vector w fs n,k−1 ,k by (32),(33) Update the c-weight vector w cs n,k−1 ,k by (34),(35) Update the average reward θ n,k by (36),(37), (38) end for Generate the next event e k+1 for each IoT device n = 1, 2, · · · , N do s n,k+1 ← h n (s n,k , e k+1 ) according to (16) end for end for…”

Section: Algorithm 2 Deep Reinforcement Learning Algorithmmentioning

confidence: 99%

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Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Lei

Xiong

et al. 2019

IEEE Internet Things J.

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By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

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Section: Assumption 1 (Resource Unit (Ru) Configuration In Nb-iot)mentioning

confidence: 99%

Section: Assumption 1 (Resource Unit (Ru) Configuration In Nb-iot)mentioning

confidence: 99%

Section: Algorithm 2 Deep Reinforcement Learning Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Lei

Xiong

et al. 2019

IEEE Internet Things J.

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“…For C > C TN−1 , M CS N is used. Note that, the idea of grouping the UEs based on coupling-gain thresholds is similar to the one used in NB-IoT [20].…”

Section: B Mcs Selection Schemementioning

confidence: 99%

Joint Resource Configuration and MCS Selection Scheme for Uplink Grant-Free URLLC

Jacobsen

Abreu

Berardinelli

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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Ultra-reliable and low-latency communications (URLLC) addresses the most challenging set of services for 5G New Radio. Uplink grant-free transmissions is recognized as a promising solution to meet the ambitious URLLC target (1 ms latency at a 99.999% reliability). Achieving such a high reliability comes at the expense of poor spectral efficiency, which ultimately affects the load supported by the system. This paper proposes a joint resource allocation solution including multiple modulation and coding schemes (MCSs) and power control settings for grant-free uplink transmissions on shared resources. The scheme assigns smaller bandwidths parts and higher MCS to the UEs in good average channel conditions, reducing the probability of fully overlapping transmissions. The performance analysis shows that the scheme is capable of increasing the system outage capacity by ∼90%, compared to prior art solutions using a conservative single-MCS configuration with fully overlapping transmissions.

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“…In the current literature, [2] and [3] show the performance of the NB-IoT radio channels. In [4], the authors discuss the main sources of latency and present an evaluation of the resource occupation in different IoT cases.…”

Section: Introductionmentioning

confidence: 99%

Analytical Modeling and Experimental Validation of NB-IoT Device Energy Consumption

Andrés-Maldonado

Lauridsen

Ameigeiras

et al. 2019

IEEE Internet Things J.

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The recent standardization of 3GPP Narrowband Internet of Things (NB-IoT) paves the way to support Low-Power Wide-Area (LPWA) use cases in cellular networks. Narrowband IoT (NB-IoT) design goals are extended coverage, low power and low cost devices, and massive connections. As a new radio access technology, it is necessary to analyze the possibilities NB-IoT provides to support different traffic and coverage needs. In this paper, we propose and validate an NB-IoT energy consumption model. The analytical model is based on a Markov chain. For the validation, an experimental setup is used to measure the energy consumption of two commercial NB-IoT User Equipments (UEs) connected to a base station emulator. The evaluation is done considering three test cases. The comparison of the model and measurements is done in terms of the estimated battery lifetime and the latency needed to finish the Control Plane procedure. The conducted evaluation shows the analytical model performs well, obtaining a maximum relative error of the battery lifetime estimation between the model and the measurements of 21% for an assumed Inter-Arrival Time (IAT) of 6 minutes.

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Data Channel Design and Performance for LTE Narrowband IoT

Cited by 34 publications

References 3 publications

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Joint Resource Configuration and MCS Selection Scheme for Uplink Grant-Free URLLC

Analytical Modeling and Experimental Validation of NB-IoT Device Energy Consumption

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