Optimal resource allocation for smart-grid powered MIMO broadcast channels

Hu, Shuyan; Wang, Xin; Giannakis, Georgios B.

doi:10.1109/wcsp.2015.7341073

Cited by 3 publications

(4 citation statements)

References 12 publications

(16 reference statements)

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“…To solve the problem in [93], [94] over an infinite scheduling period by an online algorithm, Wang et al [97] rely on the stochastic subgradient method to obtain resource schedules "on-the-fly" by suppressing (decoupling) the time-coupling between the variables and constraints. The random variables are supposed to be i.i.d..…”

Section: B Distributed Schedulingmentioning

confidence: 99%

“…Lagrange dual-based sub-gradient method [93], [94], [110], [114], [179] Non-differentiable functions Converging to global optimum for (pseudo)convex functions under certain conditions…”

Section: One Sample Per Iteration Converging With Oscillationsmentioning

confidence: 99%

“…The optimal resource allocation is specifically designed in multi-input multi-output (MIMO) systems powered by smart grid in [93], [94] by maximizing the weighted or expected sum-rate. Relying on an uplink-downlink duality derived information-theoretically [95], the downlink MIMO broadcast channel capacity region in the downlink can be equivalently calculated as the union of the uplink multi-access channels capacity regions when the uplink and downlink have to meet the same sum-power constraint.…”

Section: A Centralized Schedulingmentioning

confidence: 99%

“…Relying on an uplink-downlink duality derived information-theoretically [95], the downlink MIMO broadcast channel capacity region in the downlink can be equivalently calculated as the union of the uplink multi-access channels capacity regions when the uplink and downlink have to meet the same sum-power constraint. To derive the optimal power allocation strategy in an offline fashion, the Lagrangian dual based subgradient method is leveraged in [93], [94] by applying a nested optimization process. The downlink covariance matrices can be derived from their uplink counterparts using the uplink-downlink duality.…”

Section: A Centralized Schedulingmentioning

confidence: 99%

See 3 more Smart Citations

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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110

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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: B Distributed Schedulingmentioning

confidence: 99%

“…Lagrange dual-based sub-gradient method [93], [94], [110], [114], [179] Non-differentiable functions Converging to global optimum for (pseudo)convex functions under certain conditions…”

Section: One Sample Per Iteration Converging With Oscillationsmentioning

confidence: 99%