SWIPT-Enabled Relaying in IoT Networks Operating With Finite Blocklength Codes

Hu, Yulin; Zhu, Yao; Gursoy, M. Cenk; Schmeink, Anke

doi:10.1109/jsac.2018.2872361

Cited by 101 publications

(67 citation statements)

References 30 publications

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“…A risk-aware machine learning approach is proposed for URLLC traffic management in [238], by minimizing the risk of loss. An SWIPT-enabled URLLC network is studied in [239], where the credibility of the system is maximized by choosing the optimal SWIPT parameters based on both the PS and TS protocols.…”

Section: Ultra-reliable Low Latency Communications (Urllc)mentioning

confidence: 99%

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

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The advancements in smart power grid and the advocation of "green communications" have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.Index Terms-5G/B5G communication networks, energy har-). The first two authors contributed equally to this work. vesting, smart grid, energy redistribution and trading, optimization techniques. arXiv:1912.13203v1 [eess.SY] 31 Dec 2019 EH and smart gridpowered wireless networks Types and models for RES (Sec. II) Smart grid-powered wireless networks (Sec. VIII) Wireless power and information transfer (Sec. VII) 5G/B5G applications of EH and smart gridpowered wireless networks (Sec. XI) Energy redistribution Energy planning under dynamic pricing (Sec. IX) Two-way energy trading and cooperation (Sec. X) EH-based wireless networks Point-to-point link (Sec. III) Multipoint-to-point system (Sec. IV) Multipoint-to-multipoint system (Sec. V) Multi-hop link (Sec. VI) Lessons learnt and future directions (Sec. XII) Fig. 1. The main structure of this article. from the aspects of network deployment, energy/spectrum efficiency, and delay/bandwidth versus power. Featuring energyharvesting wireless networks, beamforming techniques are reviewed in [22]. Energy scheduling, optimization, and application are presented in [23]. Circuit design, hardware implementation, EH techniques, and communication protocols are reviewed in [24]-[27] with specific emphases on radio frequen...

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Section: Ultra-reliable Low Latency Communications (Urllc)mentioning

confidence: 99%

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

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“…Joint optimization of computation and communication radio have been investigated to reduce the computation energy consumption in both uplink and downlink systems [18]- [20]. Exploiting the energy harvesting technology or wireless energy and information transmission technology, the MTC devices obtaining energy from other devices to make itself EE transmission in short packet communication was first analyzed in [21], followed by the studies of resource allocation for wireless powered Internet of Things (IoT) with finite blocklength consideration [22], [23]. However, due to the limitation of cost and chip space, many MTC devices may not be able to support wireless powered communication or energy harvesting at this stage.…”

Section: A State Of the Artmentioning

confidence: 99%

Energy-Efficient Short Packet Communications for Uplink NOMA-Based Massive MTC Networks

Han

Liu

et al. 2019

IEEE Trans. Veh. Technol.

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The 5th generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%.

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“…1) Constant energy demand rate and fixed transmission rate: Let us consider the channel and transmission settings described in Section IV-A1. Then, we can express the moment generating functions in (28) and (29) as follows:…”

Section: B Effective Capacitymentioning

confidence: 99%

On the Energy and Data Storage Management in Energy Harvesting Wireless Communications

Akın

Gursoy

2019

IEEE Trans. Commun.

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Energy harvesting (EH) in wireless communications has become the focus of recent transmission technology studies. Herein, energy storage modeling is one of the crucial design benchmarks that must be treated carefully. Understanding the energy storage dynamics and the throughput levels is essential especially for communication systems in which the performance depends solely on harvested energy. While energy outages should be avoided, energy overflows should also be prevented in order to utilize all harvested energy. Hence, a simple, yet comprehensive, analytical model that can represent the characteristics of a general class of EH wireless communication systems needs to be established. In this paper, invoking tools from large deviation theory along with Markov processes, a firm connection between the energy state of the battery and the data transmission process over a wireless channel is established for an EH transmitter. In particular, a simple exponential approximation for the energy overflow probability is formulated, with which the energy decay rate in the battery as a measure of energy usage is characterized. Then, projecting the energy outages and supplies on a Markov process, a discrete state model is established and an expression for the energy outage probability for given energy arrival and demand processes is provided. Finally, under energy overflow and outage constraints, the average data service (transmission) rate over the wireless channel is obtained and the effective capacity of the system, which characterizes the maximum data arrival rate at the transmitter buffer under quality-of-service (QoS) constraints imposed on the data buffer overflow probability, is derived. Index TermsEnergy harvesting, energy overflow, energy outage, effective capacity, queueing theory, large deviation theory, Markov process.S. Akın is with the Institute

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SWIPT-Enabled Relaying in IoT Networks Operating With Finite Blocklength Codes

Cited by 101 publications

References 30 publications

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Energy-Efficient Short Packet Communications for Uplink NOMA-Based Massive MTC Networks

On the Energy and Data Storage Management in Energy Harvesting Wireless Communications

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