Energy sharing within EH-enabled wireless communication networks

Huang, Xueqing; Ansari, Nirwan

doi:10.1109/mwc.2015.7143338

Cited by 58 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Wireless Power Transfer: Power can be shared through wireless power transfer. Nonetheless, this is limited to very short distances due to the high power losses associated with long wireless power transmission [5].…”

Section: A Different Power Sharing Methodsmentioning

confidence: 99%

Renewable Energy Sharing Among Base Stations as a Min-Cost-Max-Flow Optimization Problem

Benda

Chu

Sun

et al. 2019

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

Limited work has been done to optimize the power sharing among base stations (BSs) while considering the topology of the cellular network and the distance-dependent power loss (DDPL) in the transmission lines. In this paper, we propose two power sharing optimization algorithms for energy-harvesting BSs: the max-flow (MF) algorithm and the min-cost-max-flow (MCMF) algorithm. The two proposed algorithms minimize the power drawn from the main grid by letting BSs with power surpluses transmit harvested power to BSs with deficits. The MCMF algorithm has an additional DDPL cost associated with each transmission line. Hence, the MCMF algorithm shares the harvested power over shorter distances and loses less power during the transmission than the MF algorithm. Our numerical results show that for a fully connected cellular network, i.e., every pair of BSs can share power, with a moderate power loss coefficient per l (∈ R + ) meters of transmission line, the MCMF algorithm saves up to 10%, 22%, and 30% more main grid power than the MF algorithm for 5, 10, and 15 BSs uniformly distributed in a square area of l 2 square meters, respectively.

show abstract

Section: A Different Power Sharing Methodsmentioning

confidence: 99%

Renewable Energy Sharing Among Base Stations as a Min-Cost-Max-Flow Optimization Problem

Benda

Chu

Sun

et al. 2019

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…More recently, a relay selection scheme, incorporating channel conditions and battery status, was proposed in [18] to choose one among multiple AF energy harvesting relays for IT. Furthermore, energy cooperation and sharing strategies have been proposed in [11], [12] to overcome dynamics of ambient energy harvesting and enable perpetual operation. In another set of works [13]- [15], R e l a y i n g P h a s e 1 ( b ) : E n e r g y R e l a y i n g relay-powered communications was considered, where energysufficient relay transfers energy to RF harvesting nodes.…”

Section: A Related Artmentioning

confidence: 99%

“…In recent studies, harvested energy at relay is either used for energy relaying (ER) [5] or IR [6]- [10], [16]- [20]. Though [13]- [15] studied tradeoff in ET and IR efficiency assuming energy-rich relay, these works along with [11], [12] did not investigate RF harvesting relay assisted ER possibilities. This work fills this existing research gap.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Dilemma at RF Energy Harvesting Relay: Downlink Energy Relaying or Uplink Information Transfer?

Mishra

Krishnaswamy

2017

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Performance of RF powered communication network is bottlenecked by short downlink energy transfer range and doubly-near-far problem faced in uplink information transfer to Hybrid Access Point (HAP). These problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma at R by providing insights on its optimal positions suited for either energy relaying (ER) or information relaying (IR). It also investigates the possibilities of integrated ER and IR along with the regions where neither ER nor IR will be useful. In this regard, while considering Rician fading channels and practical nonlinear RF energy harvesting model, the expression for mean harvested dc power at S via energy transfer from HAP and ER from R is first derived. The closedform outage probability expression is also derived for decodeand-forward relaying with maximal-ratio-combining at HAP over Rician channels. Using these expressions insights on optimal relaying mode is obtained along with global-optimal utilization of harvested energy at R for ER and IR to maximize the delaylimited RF-powered throughput. Numerical results validate the analysis and provide insights on the optimal relaying mode. Index Terms-Integrated information and energy relaying, practical RF energy harvesting model, Rician fading, outage analysis, throughput maximization, generalized convexity

show abstract

“…The energy deficit may be complemented by purchasing electricity from smart grid (SG) while any surplus RE may be sold back, thanks to the intelligent two-way power flow enabled by the SG [10], [11]. However, it will be more cost effective if the distributed RE generated at BSs sites is synergized by a common energy infrastructure to collectively serve the energy requirements of all BSs [12]- [14]. In other words, the excess RE at one BS will compensate for the deficit at another BS by enabling energy sharing between the two BSs.…”

Section: Introductionmentioning

confidence: 99%