Departure talk. Gustavo B. Figueiredo 01/15/16 1/21/16

Transcription

1 1 Departure talk Gustavo B. Figueiredo 01/15/16

2 2 Planning and Dimensioning in H- CRAN

3 3 References Heterogeneous Cloud Radio Access Networks: A New Perspec=ve For Enhancing Spectral And Energy Efficiencies. Mugen Peng, Yuan LI, Jiamo JIANG, Jian LI and Chonggang Wang. IEEE Wireless CommunicaGons, December Balancing Backhaul Load In Heterogeneous Cloud Radio Access Networks. Chen Ran, Shaowei Wang, and Chonggang Wang. IEEE Wireless CommunicaGons, June 2015.

4 4 Motivations (ADSL)-like user experience will be provided in the fivh generagon (5G) wireless systems average area capacity of 25 Gb/s/km2 100 Gmes higher than current fourth generagon (4G) systems a 1000 improvement in energy efficiency (EE) is angcipated by 2020 Cell densificagon, is a necessary step to reach such improvements HetNet (Small, pico and femto cells ): use of low-power node (LPN).

5 5 Motivations (problems ) PENG_LAYOUT_Layout 12/18/14 4:14 PM Page 127 Too dense LPNs incur severe interference, which restricts performance gains and commercial development of HetNets. CoMP transmission and recepgon is the most promising techniques in 4G but it has disadvantages in real networks performance gain depends heavily on the backhaul constraints and even degrades with increasing density of LPNs 1G/2G/3G CoMP CoMP 4G TradiGonal C-RAN deployments impose extremely high demands on backhauls and at the current development stage of most operators, it will require high investments to be deployed. Figure 1. Noncooperative cellular RAN Intercell cooperative cellular RAN

6 6 T_Layout 12/18/14 4:14 PM Page 127 H-CRAN Combine LPNs with a highpower node (HPN, e.g., macro or micro base stagon) to form a HetNet. Integrate with a C-RAN architecture by connecgng the HPN to the BBU pool Cloud computing 5G Cloud-computing-based cooperative communication MBS Massive MIMO MBS Distribute operagons by the C- RAN and the HPN HPN supports voice and lowrate data C-RAN supports high-rate data MBS RRH RRH Optical/micrometer RRH RRH RRH

7 7 H-CRAN architecture Internet Gateway 5G MBS Massive MIMO Network MAC PHY PHY_RF RRH Gateway PHY_RF RRH BSC 2G/3G/LTE MBS Network MAC PHY Gateway PHY_RF HPN 1 Fronthaul X2/S1 Backhaul Fronthaul BBU pool X2/S1 Backhaul Fronthaul HPN i PHY_RF Gateway IEEE base station Network MAC PHY_baseband IEEE access point Gateway Network MAC PHY X2/S1 Backhaul Fronthaul X2/S1 Backhaul PHY_RF PHY_RF RRH HPN j PHY_RF PHY_RF RRH HPN N Network MAC PHY Figure 2. System architecture for proposed H-CRANs.

8 8 Conventional H-CRAN CO s1/x2

9 9 Proposed approach CO CO s1/x2 Local processing units Trade-off between energy consumpgon and fronthaul/backhaul load

10 10 Proposed approach (detailed view) InterconnecGon architecture and technologies MEC/BBU Processing/ CO Switch/Spliber Line cards

11 11 Research opportunities: static scenario Planning Dimensioning Given: Wireless network load Given: network load and topology Determine: Determine: Placement of processing funcgons Level of splicng Control operagon (BBU processing, CoMP, etc) The number of wavelengths to local processing funcgons The number of wavelengths to fronthauling

12 12 Research opportunities: dynamic scenario Planning Dimensioning Load Balancing strategies for Dynamic Access topologies Enable/Disable local processing units according to wireless traffic fluctuagons Re-size the amount of resources (wavelengths) available to each funcgon Processing Fronthauling Consider EE to switch on/ off RRH or HPC. Re-opGmize parameters for the new topology

13 13 QoS-aware service degradation in EON

14 14 Motivations In the occurrence of resource shortage, services should be degraded by either: Change the modulagon format Decrease transmission rate Increase delay Preempt exisgng connecgons to accommodate new ones w i w 2 w 1

15 15 References On QoS-Assured Degraded Provisioning in Service DifferenGated OpGcal Telecom Networks. Zhizhen Zhong, Nan Hua, Jipu Li, Gustavo B. Figueiredo, Yanhe Li, Xiaoping Zheng, and Biswanath Mukherjee. Submibed to ICC 16 On Hybrid IR and AR Service Provisioning in ElasGc OpGcal Networks. Wei Lu, Zuqing Zhu, and Biswanath Mukherjee. Journal Of Lightwave Technology, November 2015.

16 16 QoS model vs. state of the network w i w 2 w 1 Weights assigned to lightpaths might be Gghtened to a QoS model, which in its turn determines the precedence relagon among lightpaths

17 17 QoS model vs. state of the network For pracgcal purposes not only the QoS model should be considered but also the state of the network However, considering the state of the network to make decisions would lead to violagon of the QoS model.. Proposed solugon: to provide a dynamic weighgng funcgon to priorigze requests depending on the QoS model and on the state of the network.

18 18 Proportional QoS model In a proporgonal service differengagon model it is expected that: (1) q i s i = q j s j (i, j =1,..., N) where q i is is the evaluated QoS metric and s i the differengagon factor for class i and N the total number of classes let τ be a short interval, the measured QoS metrics obtained during τ is given by: (2) q i (t,t +τ ) = q j (t,t +τ ) ± Δ ij s i s j

19 19 Dynamic weighting function From equagon 1 we have a 1 q 1 a 1 s 1 =... = a nq n a n s n = a 1q 1 + a 2 q a n q n a 1 s 1 + a 2 s a n s n Or in another words: q i * = s i N k=1 N k=1 a k q k a k s k = s i N a k s k k=1 a T N k=1 a T a k q k = s i s wt q T where q T and s wt represent the total blocking probability and the sum of weighted differengagon parameters and are given by: q T = N k=1 a k q k a T, s wt = a k s k a T

20 20 Dynamic weighting function The following equagon can be used to measure the deviagon of q i regarding q * i Δ i = q i q i * The following dynamic weighgng funcgon can be applied to priorigze requests according to the previous deviagon w i = α(δ i p i N i )+ βs(t) Where P i represents the stagc priority of each class, N i is the number of class I requests and s(t) is a compound funcgon represengng the state of the network (fragmentagon, lightpath complegon Gme, signaling cost..etc)

21 21 Individual Activities Problem: Scheduling VPONs to support CoMP Design and implementagon of simulator Analysis of 2 K factorial experimental design ICC paper: Load Balancing and Latency ReducGon in MulG-User CoMP over TWDM-VPONs Extension to journal: New metrics, load-aware scheduling algorithm

22 22 Collaborations Task 1 Bayesian Network and Dynamic Bayesian Network models for capturing cascading failures 2 AnalyGcal Model based on Generalized Processor Sharing to esgmate maximum achievable delay in degraded service networks Fellow Carlos Zhizhen 3 Scheduling algorithm for VPON formagon to support SU-CoMP Zhizhen 4 BBU placement in OpGcal Data centers to support mulg-tenant systems Carlos 5 A fuzzy opgmizagon model to the traveling repairman, Chen Ma 6 OpGmality proofs for scheduling algorithms in NG-EPON Lin 7 CoMP-set SelecGon and VPON scheduling for MU-COMP Xinbo 8 Resource allocagon strategies in applicagon-aware networks Divya

23 23 Learnings Be a leader Be problem-oriented rather than solugon oriented Everything can be researchable but we're only interested in problems that can be fundable We're not interested in problems that were studied by others Bis High-quality results and presentagon is the minimum expected Massimo

24 24 Goals Improve English Make friends Collect cultural experiences

25 25 Personal Goals Improve English Make friends Collect cultural experiences

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