Connectivity maximization for narrowband IoT systems with NOMA

Mostafa, Ahmed Elhamy; Zhou, Yong; Wong, Vincent W. S.

doi:10.1109/icc.2017.7996362

Cited by 74 publications

(44 citation statements)

References 6 publications

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“…Uplink NOMA 1) Network and Channel Model: We consider an uplink NOMA system where BSs are distributed according to a homogeneous PPP 2 Φ B of intensity λ b . Each user is connected to its nearest BS and there are at least N users in each Voronoi cell 3 . We consider random user selection, i.e., N users are randomly selected for NOMA transmission from users located in the Voronoi cell.…”

Section: System Model and Assumptionsmentioning

confidence: 99%

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Meta Distribution of SIR in Large-Scale Uplink and Downlink NOMA Networks

Salehi

Tabassum

Hossain

2019

IEEE Trans. Commun.

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We develop an analytical framework to derive the meta distribution and moments of the conditional success probability (CSP), which is defined as success probability for a given realization of the transmitters, in large-scale co-channel uplink and downlink non-orthogonal multiple access (NOMA) networks with one NOMA cluster per cell. The moments of CSP translate to various network performance metrics such as the standard success or signal-to-interference ratio (SIR) coverage probability (which is the 1-st moment), the mean local delay (which is the −1-st moment in a static network setting), and the meta distribution (which is the complementary cumulative distribution function of the conditional success probability and can be approximated by using the 1-st and 2-nd moments). For uplink NOMA, to make the framework tractable, we propose two point process models for the spatial locations of the interferers by utilizing the base station (BS)/user pair correlation function. We validate the proposed models by comparing the second moment measure of each model with that of the actual point process for the intercluster (or inter-cell) interferers obtained via simulations. For downlink NOMA, we derive closed-form solutions for the moments of the CSP, success (or coverage) probability, average local delay, and meta distribution for the users. As an application of the developed analytical framework, we use the closedform expressions to optimize the power allocations for downlink NOMA users in order to maximize the success probability of a given NOMA user with and without latency constraints. Closed-form optimal solutions for the transmit powers are obtained for two-user NOMA scenario. We note that maximizing the success probability with latency constraints can significantly impact the optimal power solutions for low SIR thresholds and favour orthogonal multiple access (OMA). Index TermsUltra-reliable and low-latency communication (URLLC), uplink NOMA, downlink NOMA, success (or SIR coverage) probability, stochastic geometry, meta distribution, local delay, moments.The authors are with the

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Section: System Model and Assumptionsmentioning

confidence: 99%

“…2 The motivation of modeling BS locations for real-world cellular networks with PPP was justified in [25]. 3 This can be viewed as the general case of the user point process of type I introduced in [26]. In [26], the user point process for N = 1 is studied which is the case in OMA.…”

Section: System Model and Assumptionsmentioning

confidence: 99%

Meta Distribution of SIR in Large-Scale Uplink and Downlink NOMA Networks

Salehi

Tabassum

Hossain

2019

IEEE Trans. Commun.

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“…Although it can accelerate the transmission procedure, it may decrease the scheduling flexibility and resource efficiency. In [19], it leverages Nonorthogonal Multiple Access (NOMA) to allocate common subcarriers to multiple devices and thus to enhance the spectrum efficiency. However, it does not discuss how to ensure the energy efficiency and transmission reliability, which are the key issues in NB-IoT.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Uplink Resource Units Scheduling for Ultra-Reliable Communications in NB-IoT Networks

Liang

Chen

et al. 2018

Wireless Communications and Mobile Computing

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For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices' energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

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“…Varshney 27 proposed the concept of simultaneous wireless information and power transfer (SWIPT) in wireless communication systems. 31,32 For example, Zhang et al 33 proposed an energy-efficient transmission method for an IoT system with the NOMA technology. 9 This is because the WPT operation can impair the signals.…”

Section: Related Workmentioning

confidence: 99%

“…30 Researchers have employed the NOMA technology in IoT networks. 31,32 For example, Zhang et al 33 proposed an energy-efficient transmission method for an IoT system with the NOMA technology. Shirvanimoghaddam et al 34,35 designed a random NOMA strategy for massive IoT networks where multiple devices are allowed to transmit data over the same sub-band.…”

Section: Related Workmentioning

confidence: 99%

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

Luo

et al. 2019

Int J Communication

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Energy harvesting (EH) has been considered as one of the promising technologies to power Internet of Things (IoT) devices in self-powered IoT networks. By adopting a typical harvest-then-transmit mode, IoT devices with the EH technology first harvest energy by using wireless power transfer (WPT) and then carry out wireless information transmission (WIT), which leads to the coordination between WPT and WIT. In this paper, we consider optimizing energy consumption of periodical data collection in a self-powered IoT network with non-orthogonal multiple access (NOMA). Particularly, we take into account time allocation for the WPT and WIT stages, node deployment, and constraints for data transmission. Moreover, to thoroughly explore the impact of different multiple access methods, we theoretically analyse and compare the performance achieved by employing NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) in the considered IoT network. To validate the performance of the proposed method, we conduct extensive simulations and show that the NOMA outperforms the FDMA and TDMA in terms of energy consumption and transmission power. KEYWORDSInternet of Things (IoT), non-orthogonal multiple access, wireless information transmission, wireless power transfer Int J Commun Syst. 2020;33:e4174.wileyonlinelibrary.com/journal/dac

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Connectivity maximization for narrowband IoT systems with NOMA

Cited by 74 publications

References 6 publications

Meta Distribution of SIR in Large-Scale Uplink and Downlink NOMA Networks

Meta Distribution of SIR in Large-Scale Uplink and Downlink NOMA Networks

Energy-Efficient Uplink Resource Units Scheduling for Ultra-Reliable Communications in NB-IoT Networks

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

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