MIMO Throughput-aware Random Clustering Throughput-aware Random Clustering MATLAB MIMO MIMO MIMO MIMO MIMO MIMO [16] MIMO [1

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1 CSI MIMO 1,a) Bryan Ng 2 Winston Seah 2,b) 3,c) 1,d) 1 1 MIMO MIMO MIMO MIMO Throughput-aware Random Clustering Throughput-aware Random Clustering MIMO MATLAB % [1] M2M IoT [2] 1 1 ( 1) Osaka University 2 Victoria University of Wellington 3 Shizuoka University a) kobayashi.makoto@ist.osaka-u.ac.jp b) Winston.Seah@ecs.vuw.ac.nz c) saru@inf.shizuoka.ac.jp d) watanabe@ist.osaka-u.ac.jp ( 2) ( 3) [3, 4] ( 4) [5] MIMO [6 15] MIMO MIMO アクセスポイントユーザ端末

2 MIMO Throughput-aware Random Clustering Throughput-aware Random Clustering MATLAB MIMO MIMO MIMO MIMO MIMO MIMO [16] MIMO [17, 18] MU-MIMO (Multi User MIMO) MU-MIMO MIMO [6 15] MIMO MIMO MIMO MIMO MIMO 5 MIMO MIMO Backhaul 5 AP1!"#! $%&'()#! $%&'()*! /012304%! 5!"*! $%&'()+! $%&'(),! $5()65%%'6 MIMO!"+! $%&'()-! $%&'().! AP3 3 MIMO CSI (Channel State Information) CSI MIMO MIMO CSI K N K N [19] CSI MIMO MIMO MIMO K K! MIMO

3 3. Throughput-aware Random Clustering 2 MIMO Throughput-aware Random Clustering Throughput-aware Random Clustering MIMO 3.1 Throughput-aware Random Clustering MIMO Throughput-aware Random Clustering 1 Algorithm 1 Algorithm 5 S C M R k k R M R 1 M u M R > R k Algorithm 1 Throughputaware Random Clustering Throughput-aware Random Clustering C while (Access point addition phase) 1 C 3.2 C M (Data transmission phase) (Throughput measurement phase) M Algorithm 1 Throughput-aware random clustering 1: C 2: while do 3: u 0 4: Acccess point addtion phase 5: for k = 1 to M do 6: Data transmission phase 7: Throughput measurement phase 8: end for 9: Access point remove phase 10: R = 1 M M k=1 R k 11: R R 12: end while 1 S C M R k R R u RSSI(AP i ) AP min Algorithm 1 Algorithm 5 k M 1 M M R > R k AP i RSSI RSSI Algorithm 2 Access point addition phase 1: Select access point {AP i AP i S \ C} randomly 2: C C + {AP i } M (Access point remove phase) C Algorithm 2 AP i AP i S C 3

4 $#%&! $#%'! ()*+,-!!"#$!!"#! """! 6!"#! (./%01! Frame sequence "/ ! "$2$! "$2$! Algorithm 3 Throughput measurement phase 1: R k throughput(c) 2: if R k < R then 3: u u + 1 4: end if 3.3 C MIMO 6 MIMO IEEE ac MIMO 1 NDPA (Null Data Packet Announcement) NDP (Null Data Packet) NDPA NDP 1 NDP C NDP CSI-FB (Channel State Information Feedback) CSI-FB NDP h h CSI-FB h (DATA) [20 22] BA (Block ACK) NDPA NDP CSI-FB DATA, BA IEEE ac NDP IEEE ac 3.4 Algorithm 3 Algorithm 3 1 1$! throughput R k throughput(c) C throughput R capacity [bps] R capacity = W log (1 + P t h 2 ) W N. W [Hz] P t [mw] N [mw/hz] h = [h 1,..., h K ] t h CSI-FB R capacity R k [bps] T data T total R k = T datar capacity T total ( ) K T total T total = T DIFS + T BO + T NDPA + K(T NDP + T SIFS ) + T CSI + (1) T DIFS + T BO + T header + T data + T SIFS + T BA T DIFS DIFS T BO Back off T NDPA NDPA T NDP NDP T SIFS SIFS T CSI CSI-FB T header T BA Block Ack R k Algorithm M R u u 3.5 Algorithm 4 u 0 1 remove access point 1 4

5 Algorithm 4 Access point remove phase 1: if u > 0 then 2: remove access point(c) 3: end if 4: if u > M/2 then 5: remove access point(c) 6: end if Algorithm 5 remove access point 1: AP min arg min RSSI(AP i ) AP i C 2: if C {AP min } then 3: C C \ {AP min } 4: end if u M/2 1 Algorithm 5 remove access point AP i C RSSI RSSI 1 RSSI h RSSI AP i h i RSSI h i 1 C {AP i } 4. Throughput-aware Random Clustering K d AP d AP C 1 d AP 10 m d AP C 10 m mw [23, 24] 2 T DIFS 34µs T BO 67.5µs T NDPA 64µs T NDP 64µs T SIFS 16µs T CSI 1000µs T header 44µs T BA 44 µs 2 DIFS 34 µs backoff 67.5 µs NDPA(Null Data Packet Announcement) 64 µs NDP (Null Data Packet) 64 µs SIFS 16 µs CSI-FB 1000 µs BA (Block ACK) 44 µs Header 44 µs Data Frame 500 µs!!"!!!"!!!")#!!!"!!!"! 7!!"*+*,-)./01! """! """!!"! #$%&'(! K 1 Topology Throughput-aware Random Clustering 3 ( 1 ) Giant MIMO (giant) Giant MIMO MIMO Giant MIMO Throughput-aware Random Clustering ( 2 ) Static Clustering (static) Static Clustering MIMO Static Clustering 10 ( 3 ) Throughput-aware Random Clustering (proposed) Throughput-aware Random Clustering 3 1 M Throughput-aware Random Clustering K Throughput-aware Random Clustering 5

6 8 Number of Access Points vs. Throughput 9 Time Evolution of Throughput [bps/hz] 8 (proposed) Giant MIMO 3 Giant MIMO [ ] [bps/hz] ms 150 ms MIMO IEEE ac 2 (1) T NDP + T SIFS 10 [ ] [bps/hz] 10 (80 ) MIMO MIMO [6 8] [25,26] MIMO [20 22] MIMO MIMO [21,22] MIMO MIMO CoMP (Coordinated Multi- Point) MIMO CoMP LTE-Advanced 6

7 Throughput-aware Random Clustering Throughput-aware Random Clustering 10 Overhead vs. Throughput [8 11] Wireless LAN MIMO [12 15] [12,13] [14, 15] [14] [15] NEMOx MU-MIMO [27, 28] Throughput-aware Random Clustering 6. MIMO Throughput-aware Random Clustering Throughput-aware Random Clustering MIMO [1] Ericsson: Ericsson Mobility Report (2014). [2] IT 2014 (2013). [3] Gollakota, S. and Katabi, D.: ZigZag decoding: Combating hidden terminals in wireless networks, Proceedings of the ACM SIGCOMM 2008 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (ACM SIGCOMM 08), pp (2008). [4] Li, T., Han, M. K., Bhartia, A., Qiu, L., Rozner, E., Zhang, Y. and Zarikoff, B.: CRMA: Collision-resistant multiple access, Proceedings of the 18th ACM Annual International Conference on Mobile Computing and Networking (ACM MobiCom 11), pp (2011). [5],, MBL (2012). [6] Yu, W., Kwon, T. and Shin, C.: Multicell coordination via joint scheduling, beamforming, and power spectrum adaptation, IEEE Transactions on Wireless Communications, Vol. 12, No. 7, pp (2013). [7] Manolakos, A., Noam, Y. and Goldsmith, A. J.: Null space learning in cooperative MIMO cellular networks using interference feedback, Proceedings of IEEE Global Telecommunications Conference (IEEE GLOBECOM 10), pp (2013). [8] Gesbert, D., Hanly, S., Huang, H., Shamai Shitz, S., Simeone, O. and Yu, W.: Multi-cell MIMO cooperative networks: A new look at interference, IEEE Journal on Selected Areas in Communications, Vol. 28, No. 9, pp (2010). [9] Irmer, R., Droste, H., Marsch, P., Grieger, M., Fettweis, G., Brueck, S., Mayer, H. P., Thiele, L. and Jungnickel, V.: Coordinated multipoint: Concepts, performance, and field trial results, IEEE Communications Magazine, Vol. 49, No. 2, pp (2011). [10] Sawahashi, M., Kishiyama, Y., Morimoto, A., Nishikawa, D. and Tanno, M.: Coordinated multipoint transmission/reception techniques for LTE-advanced, IEEE Wireless Communications, Vol. 17, No. 3, pp (2010). [11] Lossow, M., Jaeckel, S., Jungnickel, V. and Braun, V.: Efficient MAC protocol for JT CoMP in small cells, Proceedings of 2013 IEEE International Conference on Communications Workshops (IEEE ICC 13), pp (2013). [12] Rahul, H. S., Kumar, S. and Katabi, D.: JMB: Scaling wireless capacity with user demands, Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (ACM SIGCOMM 12), pp (2012). [13] Balan, H. V., Rogalin, R., Michaloliakos, A., Psounis, 7

8 K. and Caire, G.: Achieving high data rates in a distributed MIMO system, Proceedings of the 18th ACM Annual International Conference on Mobile Computing and Networking (ACM MobiCom 12), pp (2012). [14] Yu, H., Bejarano, O. and Zhong, L.: Combating intercell interference in ac-based multi-user MIMO networks, Proceedings of the 20th ACM Annual International Conference on Mobile Computing and Networking (ACM MobiCom 14), pp (2014). [15] Zhang, X., Sundaresan, K., Khojastepour, M. A. A., Rangarajan, S. and Shin, K. G.: NEMOx: Scalable network MIMO for wireless networks, Proceedings of the 19th ACM Annual International Conference on Mobile Computing and Networking (ACM MobiCom 13), pp (2013). [16] Yin, H. and Liu, H.: Performance of space-division multiple-access (SDMA) with scheduling, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp (2002). [17] Foschini, G. J.: Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs Technical Journal, Vol. 1, No. 2, pp (1996). [18] van Zelst, A. and Schenk, T. C.: Implementation of a MIMO OFDM-based wireless LAN system, IEEE Transactions on Signal Processing, Vol. 52, No. 2, pp (2004). [19] Ashikhmin, A. and Marzetta, T.: Pilot contamination precoding in multi-cell large scale antenna systems, Proceedings of 2012 IEEE International Symposium on Information Theory (IEEE ISIT 12), pp (2012). [20] Huh, H., Tulino, A. M. and Caire, G.: Network MIMO with linear zero-forcing beamforming: Large system analysis, impact of channel estimation, and reducedcomplexity scheduling, IEEE Transactions on Information Theory, Vol. 58, No. 5, pp (2012). [21] Zhang, J., Chen, R., Andrews, J. G., Ghosh, A. and Heath Jr., R. W.: Networked MIMO with clustered linear precoding, IEEE Transactions on Wireless Communications, Vol. 8, No. 4, pp (2009). [22] Kaviani, S. and Krzymien, W. A.: Multicell scheduling in network MIMO, Proceedings of IEEE Global Telecommunications Conference (IEEE GLOBECOM 10), pp. 1 5 (2010). [23] IEEE Standard Association: IEEE Standard ac (2013). [24] Murakami, T., Takatori, Y., Mizoguchi, M. and Maehara, F.: A cross-layer switching of OFDMA and MU- MIMO for future WLAN systems, IEICE Communications Express, Vol. 3, No. 9, pp (2014). [25] Heath Jr., R. W., Wu, T., Kwon, Y. H. and Soong, A. C. K.: Multiuser MIMO in distributed antenna systems with out-of-cell interference, IEEE Transactions on Signal Processing, Vol. 59, No. 10, pp (2011). [26] Zhang, J. and Andrews, J.: Distributed antenna systems with randomness, IEEE Transactions on Wireless Communications, Vol. 7, No. 9, pp (2008). [27] Karimi, O. B., Toutounchian, M. A., Liu, J. and Wang, C.: Lightweight user grouping with flexible degrees of freedom in virtual MIMO, IEEE Journal on Selected Areas in Communications, Vol. 31, No. 10, pp (2013). [28] Xie, X. and Zhang, X.: Scalable user selection for MU- MIMO networks, Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM 14), pp (2014). 8

IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info

IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Information Science and Technology, Osaka University a) kawasumi.ryo@ist.osaka-u.ac.jp 1 1 Bucket R*-tree[5] [4] 2 3 4 5 6 2. 2.1 2.2 2.3