SCIENCE CHINA Information Sciences, Volume 59, Issue 10: 102310(2016) https://doi.org/10.1007/s11432-015-5481-9

## Cross-layer transmission and energy scheduling under full-duplex energy harvesting wireless OFDM joint transmission

• AcceptedOct 3, 2015
• PublishedApr 26, 2016
Share
Rating

### Abstract

This paper studies the design of the optimal and online cross-layer transmission and energy schedulings for a full-duplex energy harvesting wireless orthogonal frequency division multiplexing (OFDM) joint transmissions. Supported by today's power management integrated circuit, the full-duplex energy harvesting system becomes a reality, which can overcome the transmission time loss problem caused by the half-duplex constraint of the energy storage unit (ESU) in the serial Harvest-Store-Use system. However, its corresponding modeling is still unexplored. Therefore, the full-duplex energy harvesting system is first modeled and proved to be equivalent to a composition of energy behavior models of Harvest-Store-Use in fine-time granularity. Then, the convex optimization problem of cross-layer transmission and energy scheduling is formulated with the objective to maximize the sum of transmission throughput during successively multiple time units, which takes into account the temporal variance of energy harvesting rates and channel states, and the limited capacity of ESUs. The optimal power allocation with three dimensions of time, channel and antenna is solved by utilizing the dual decomposition method with the pre-known temporal variance, and the corresponding result of the system throughput provides the theoretical upper bound. Finally, to reduce the throughput degradation caused by channel state prediction errors, a non-convex online scheduling problem is formulated as the classical energy efficiency format. It is transformed into a convex optimization problem by exploiting the properties of fractional programming, and then, an efficiently iterative solution is designed. Numerical results show that the average throughput of the online algorithm is $24\%$ greater than that of existing time-energy adaptive water-filling algorithm. The degradation of the average throughput is less than $19\%$ with probability $90\%$, even as the channel prediction error reaches $20\%$. These results provide guidelines for the design and optimization for full-duplex energy harvesting joint transmission systems.

### Funded by

National Natural Science Foundation of China(61302108)

Strategic Pilot Project of Chinese Academy of Sciences(XDA06010300)

### Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61302108) and Strategic Pilot Project of Chinese Academy of Sciences (Grant No. XDA06010300).

### References

[1] Han T, Ansari N. On greening cellular networks via multicell cooperation. IEEE Wirel Commun, 2013, 20: 82-89 Google Scholar

[2] Tutuncuoglu K, Yener A. Optimum transmission policies for battery limited energy harvesting nodes. IEEE Trans Wirel Commun, 2012, 11: 1180-1189 CrossRef Google Scholar

[3] Ozel O, Yang J, Ulukus S. Optimal broadcast scheduling for an energy harvesting rechargeable transmitter with a finite capacity battery. IEEE Trans Wirel Commun, 2012, 11: 2193-2203 CrossRef Google Scholar

[4] Badawy G H, Sayegh A A, Todd T D. Fair flow control in solar powered WLAN mesh networks. In: Proceedings of IEEE Wireless Communications and Networking Conference, Budapest, 2009. 1--6. Google Scholar

[5] Ng K W D, Lo E S, Schober R. Energy-efficient resource allocation in OFDMA systems with hybrid energy harvesting base station. IEEE Trans Wirel Commun, 2013, 12: 3412-3427 CrossRef Google Scholar

[6] Huang C, Zhang R, Cui S. Throughput maximization for the gaussian relay channel with energy harvesting constraints. IEEE J Sel Area Commun, 2013, 31: 1469-1479 CrossRef Google Scholar

[7] Jiang X, Polastre J, Culler D. Perpetual environmentally powered sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Los Angeles, 2005. 463--468. Google Scholar

[8] Kuo Y C, Tung W H, Liu L J. Smart integrated circuit and system design for renewable energy harvesters. IEEE J Photovoltaics, 2013, 3: 401-406 CrossRef Google Scholar

[9] Lee D, Seo H, Clerckx B, et al. Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges. IEEE Commun Mag, 2012, 50: 148-155 CrossRef Google Scholar

[10] Ma Z, Zhang Z Q, Ding Z G, et al. Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives. Sci China Inf Sci, 2015, 58: 041301-155 Google Scholar

[11] Xu J, Zhang R. CoMP meets smart grid: a new communication and energy cooperation paradigm. IEEE Tran Veh Tech, 2015, 64: 2476-2488 CrossRef Google Scholar

[12] Chiang Y H, Liao W. Renewable energy aware cluster formation for CoMP transmission in green cellular networks. In: Proceedings of IEEE Global Communications Conference, Austin, 2014. 4611--4616. Google Scholar

[13] Lyman J R. Optimal mean-square prediction of the mobile-radio fading envelope. IEEE Tran Signal Process, 2003, 51: 819-824 CrossRef Google Scholar

[14] Xing C, Wang N, Ni J, et al. MIMO beamforming designs with partial CSI under energy harvesting constraints. IEEE Signal Process Lett, 2013, 20: 363-366 CrossRef Google Scholar

[15] Marsch P, Fettweis G P. Coordinated Multi-Point in Mobile Communications: From Theory to Practice. London: Cambridge University Press, 2011. 5--6. Google Scholar

[16] West M, Harrison J. Bayesian Forecasting and Dynamic Models. Berlin: Springer, 1997. 270--304. Google Scholar

[17] Bertsekas D. Convex Optimization Theory. Belmont: Athena Scientific, 2009. 347--364. Google Scholar

[18] Dinkelbach W. On nonlinear fractional programming. Manage Sci, 1967, 13: 492-498 CrossRef Google Scholar

[19] Ozel O, Tutuncuoglu K, Yang J, et al. Transmission with energy harvesting nodes in fading wireless channels: optimal policies. IEEE J Sel Area Commun, 2011, 29: 1732-1743 CrossRef Google Scholar

Citations

• #### 0

Altmetric

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有