為了滿足5G日漸增長流量需求，而需要提升傳輸速率，所應映的技術為毫米波，為了降低毫米波的高路徑衰減的解決方法為波束成形的技術，波束成形是個信號處理方式可以透過基地台的天線陣列產生出指向性的波束來提高傳輸效能。除了提升傳輸速率外，未來的5G架構就是一個異質網路，所以不同階層的基地台的干擾問題，會變得越來越重要。如何在異質網路跟毫米波環境下的協調不同大小的基地台的波束之間的干擾也會是非常重要的問題。在之前LTE有提出了一個增強型的基地台間干擾的協調機制，但是在使用波束成形的技術下運用一般的增強型的基地台間干擾的協調機制會降低整體網路的效能。在本篇論文，我們提出了一個改良型的增強型的基地台間干擾的協調機制用在不同階層的基地台的波束上。我們可以讓不同的波束分配不同的空白子片段來提高整體網路的效能還有使用效率，最後我們以Gurobi這套軟體來評估整體網路的效能。

In order to satisfy increasing traffic demands in 5G network. 5G provides mmWave technology. Beamforming is considered to be a promising technique to reduce the pathloss of mmWave. Beamforming is a signal processing by antenna arrays for directional signal transmission or reception. The directional signal can reduce the pathloss of mmWave very well. Besides, increasing traffic demands. 5G will also support Heterogeneous Network (HetNet). One of the key challenges is reducing the interference and maximizing the throughput. In LTE, it provides enhanced Inter-Cell Interference Coordination (eICIC) to reduce the interference of the cells. Clearly, an uniform eICIC in mmWave scenario can't fully utilize the beams capability. In this paper, we propose an non-uniform eICIC configuration algorithm to improve the throughput of the cells. Final, we use Gurobi to evaluate our performance

[1] ShareTechNote, “5g/nr - beam management.” http://www.sharetechnote.
com/html/5G/5G_Phy_BeamManagement.html/.
[2] 3rd Generation Partnership Project, “Tr 36.300 evolved universal terrestrial radio
access (e-utra) and evolved universal terrestrial radio access network (e-utran),”
[3] S. Borst, A. Ö. Kaya, D. Calin, and H. Viswanathan, “Dynamic path selection in 5g
multi-rat wireless networks,” in IEEE International Conference on Computer Communications
(INFOCOM), pp. 1–9, 2017.
[4] S. Singh, S.-p. Yeh, N. Himayat, and S. Talwar, “Optimal traffic aggregation in multirat
heterogeneous wireless networks,” in IEEE International Communications Conference
(ICC), pp. 626–631, 2016.
[5] H. J. Kushner and P. A. Whiting, “Convergence of proportional-fair sharing algorithms
under general conditions,” IEEE Transactions on Wireless Communications,
vol. 3, no. 4, pp. 1250–1259, 2004.
[6] G. Panwar, R. Tourani, T. Mick, A. Mtibaa, and S. Misra, “Dice: Dynamic multirat
selection in the icn-enabled wireless edge,” in Proceedings of the Workshop on
Mobility in the Evolving Internet Architecture(MobiArch), pp. 31–36, 2017.
[7] S. Deb, P. Monogioudis, J. Miernik, and J. P. Seymour, “Algorithms for enhanced
inter-cell interference coordination (eicic) in lte hetnets,” IEEE/ACM transactions
on networking, vol. 22, no. 1, pp. 137–150, 2014.
[8] J. Zheng, J. Li, N. Wang, and X. Yang, “Joint load balancing of downlink and uplink
for eicic in heterogeneous network,” IEEE Transactions on Vehicular Technology,
vol. 66, no. 7, pp. 6388–6398, 2017.
[9] A. Chiumento, M. Bennis, C. Desset, L. Van der Perre, and S. Pollin, “Adaptive csi
and feedback estimation in lte and beyond: a gaussian process regression approach,”
EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1,
p. 168, 2015.
[10] NetSimSupportTeam, “How to calculate phy data rate in lte?.” https:
//tetcos.freshdesk.com/support/solutions/articles/
14000069248-how-to-calculate-phy-data-rate-in-lte-.
[11] G. Zonghao, R. Edward, and B. Robert, “Gurobi optimizer.” http://www.
gurobi.com/.
[12] ShareTechNote, “5g - framestructure.” http://www.sharetechnote.com/
html/5G/5G_FrameStructure.html/.
[13] ShareTechNote, “Lte-advanced-256qam.” http://www.sharetechnote.
com/html/LTE_Advanced_256QAM.html.
[14] C. Bettstetter, G. Resta, and P. Santi, “The node distribution of the random waypoint
mobility model for wireless ad hoc networks,” IEEE Transactions on Mobile
Computing, no. 3, pp. 257–269, 2003.