在密集部署微型基地台(small cell)的回程網路(backhaul network)場景中，毫米波(millimeter wave, mmWave)因易於部署以及部署成本較低的緣故成為一項備受矚目的無線回程網路方案。但是微型基地台之間的毫米波連線容易受到障礙物的存在而發生阻擋(blockage)，導致吞吐量大幅降低甚至造成連線中斷(outage)。為了確保回程網路服務範圍內使用者的體驗，微型基地台之間流量(flow)必須保持每秒十億位元(gigabits per second, Gbps)的傳輸速率以上。因此，對於具備中繼節點(relay node)的回程網路，我們提出同步決策與QoS感知共時排程(Synchronized Decision and QoS-aware Concurrent Scheduling, \method)演算法。其中，它包含中繼選擇以及共時傳輸決策兩個程序。在中繼節點選擇程序中，演算法會幫尚未獲得中繼節點協助的受阻擋流量(blocked flow)選擇中繼節點。在共時傳輸決策程序中，演算法會在同個空間下以共時的方式安排節點到節點的流量傳輸。不同於現有的演算法設計，我們的方法採用同步進行的架構。因此在排程的過程中動態更新每個受阻擋流量的中繼選擇，這能夠減少於同時隙傳輸的流量在訊號上的干擾，並且提高受阻擋流量的傳輸機會。根據實驗結果，我們提出的方法與其他現有的方法相較之下，完成更多流量的服務品質需求(number of complete flows)以及達到更高的系統吞吐量(system throughput)。

In the backhaul network scenario where small cells are densely deployed, millimeter wave is a great solution due to its characteristics of the ease of deployment and lower costs. However, millimeter wave is easily blocked by obstacles during transmission, which significantly reduce throughput that would cause connection outage. To ensure user experience in the backhaul network, each small cell must sustain more than gigabits per second data rate for data transmission. Hence, we propose Synchronized Decision and QoS-aware Concurrent Scheduling (\method), which includes two procedures: relay selection and concurrent transmission decision. In the relay selection procedure, the algorithm selects a relay node for blocked flow. In the concurrent transmission decision procedure, the algorithm arranges spectrum resources for node-to-node data transmission within a spatial dimension in a concurrent manner. Unlike other existing algorithms, our approach uses synchronous architecture where the relay selection for each blocked flow is dynamically updated during scheduling which will reduce the signal interference of flows transmitted at the same time slot and improves the transmission chance of the blocked flows. The simulation results show our proposed approach achieves higher number of complete flows and higher system throughput compared to others.

[1] M. Olsson et al., “5GrEEn: Towards Green 5G Mobile Networks,” in Proceedings
of the 2013 IEEE 9th International Conference on Wireless and Mobile Computing,
Networking and Communications (WiMob), Oct. 7-9, 2013, pp. 212-216.
[2] R. Taori and A. Sridharan, “Point-to-Multipoint In-Band mmWave Backhaul for 5G
Networks,” IEEE Communications Magazine, vol. 53, no. 1, pp. 195-201, Jan.
2015.
[3] Y. Niu et al., “Relay-Assisted and QoS Aware Scheduling to Overcome Blockage in
mmWave Backhaul Networks,” IEEE Transactions on Vehicular Technology, vol.
68, no. 2, pp. 1733-1744, Feb. 2019.
[4] Y. Zhu et al., “QoS-aware Scheduling for Small Cell Millimeter Wave Mesh Back-
haul,” in Proceedings of the 2016 IEEE International Conference on Communica-
tions (ICC), May 22-27, 2016, pp. 1-6.
[5] Y. Niu et al., “Energy-Efficient Scheduling for mmWave Backhauling of Small Cells
in Heterogeneous Cellular Networks,” IEEE Transactions on Vehicular Technol-
ogy, vol. 66, no. 3, pp. 2674-2687, Mar. 2017.
[6] C. Sum et al., “A Virtual Time-Slot Allocation Throughput Enhancement Scheme
with Multiple Modulations for a Multi-Gbps Millimeter-Wave WPAN System,” in
Proceedings of the 2009 IEEE Wireless Communications and Networking Confer-
ence, Apr. 5-8, 2009, pp. 1-6.
[7] J. Qiao et al., “STDMA-based Scheduling Algorithm for Concurrent Transmissions
in Directional Millimeter Wave Networks,” in Proceedings of the 2012 IEEE Inter-
national Conference on Communications (ICC), Jun. 10-15, 2012, pp. 5221-5225.
[8] M. R. Akdeniz et al., “Millimeter Wave Channel Modeling and Cellular Capacity
Evaluation,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6,
pp. 1164-1179, Jun. 2014.
[9] S. Sun et al., “Propagation Path Loss Models for 5G Urban Micro- and Macro-
Cellular Scenarios,” in Proceedings of the 2016 IEEE 83rd Vehicular Technology
Conference (VTC Spring), May 15-18, 2016, pp. 1-6.
[10] R. J. Teather, A. Pavlovych, W. Stuerzlinger, and I. S. MacKenzie, “ Effects of
Tracking Technology, Latency, and Spatial Jitter on Object Movement,” in Pro-
ceedings of the 2009 IEEE Symposium on 3D User Interfaces, Mar. 2009, pp.
43–50.
[11] Y. Li et al., “A Joint Scheduling and Resource Allocation Scheme for Millimeter
Wave Heterogeneous Networks,” in Proceedings of the 2017 IEEE Wireless Com-
munications and Networking Conference (WCNC), Mar. 19-22, 2017, pp. 1-6.
[12] J. Qiao et al., “Enabling Multi-Hop Concurrent Transmissions in 60 GHz Wireless
Personal Area Networks,” IEEE Transactions on Wireless Communications, vol.
10, pp. 3824-3833, Nov. 2011.
[13] Y. Liu, Q. Hu and D. M. Blough, “Blockage Avoidance in Relay Paths for Road-
side mmWave Backhaul Networks,” in Proceedings of the 2018 IEEE 29th Annual
International Symposium on Personal, Indoor and Mobile Radio Communications
(PIMRC), Sep. 9-12, 2018, pp. 1-7.
[14] S. Singh et al., “Millimeter Wave WPAN: Cross-Layer Modeling and Multi-Hop Ar-
chitecture,” in Proceedings of the IEEE INFOCOM 2007 - 26th IEEE International
Conference on Computer Communications, May 6-12, 2007, pp. 2336-2340.
[15] J. Qiao et al., “Enabling Device-to-Device Communications in Millimeter Wave 5G
Cellular Networks,” IEEE Communications Magazine, vol. 53, no. 1, pp. 209-215,
Jan. 2015.
[16] H. Luo, S. Lu, and V. Bharghavan, “A New Model for Packet Scheduling in Multi-
Hop Wireless Networks,” in Proceedings of the 6th Annual International Confer-
ence on Mobile Computing and Networking, Aug. 1, 2000, pp. 76-86.
[17] Z. Genc et al., “Robust 60 GHz Indoor Connectivity: Is It Possible with Reflec-
tions?,” in Proceedings of the 2010 IEEE 71st Vehicular Technology Conference,
May 16-19, 2010, pp. 1-5.
[18] C. Yiu and S. Singh, “Empirical Capacity of mmWave WLANS,” IEEE Journal
on Selected Areas in Communications, vol. 27, no. 8, pp. 1479-1487, Oct. 2009.
[19] X. An et al., “Beam Switching Support to Resolve Link-Blockage Problem in 60
GHz WPANs,” in Proceedings of the 2009 IEEE 20th International Symposium on
Personal, Indoor and Mobile Radio Communications, Sep. 13-16, 2009, pp. 390-
394.
[20] F. Firyaguna et al., “Performance Evaluation of Scheduling in 5G-mmWave Net-
works under Human Blockage,” ar13iv:2007.13112 [cs.IT], Jul. 2020.
[21] M. Alrabeiah and A. Alkhateeb, “Deep Learning for mmWave Beam and Blockage
Prediction Using Sub-6 GHz Channels,” IEEE Transactions on Communications,
vol. 68, no. 9, pp. 5504-5518, Sep. 2020.
[22] T. Nishio et al., “Proactive Received Power Prediction Using Machine Learning
and Depth Images for mmWave Networks,” IEEE Journal on Selected Areas in
Communications, vol. 37, no. 11, pp. 2413-2427, Nov. 2019.
[23] “IEEE Standard for Information Technology– Local and Metropolitan Area
Networks– Specific Requirements– Part 15.3: Amendment 2: Millimeter-wave-
based Alternative Physical Layer Extension,” IEEE Std 802.15.3c-2009, Oct. 2009.
[24] I. Toyoda et al., “Reference Antenna Model with Side Lobe for TG3c Evaluation,”
in Proceedings of the IEEE 802.15.3c document, Nov. 2006.