May 20, 2016 1 / 17 Joint In-Band Backhauling and Interference Mitigation in 5G Heterogeneous Networks Trung Kien Vu, Mehdi Bennis, Sumudu Samarakoon, Me’rouane Debbah†, and Matti Latva-aho Centre for Wireless Communications, University of Oulu, Oulu, Finland, and †Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France.. Email: [email protected].fi. May 20, 2016
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Joint In-Band Scheduling and Interference Mitigation in 5G HetNets
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May 20, 2016 1 / 17
Joint In-Band Backhauling and InterferenceMitigation in 5G Heterogeneous Networks
Trung Kien Vu, Mehdi Bennis, Sumudu Samarakoon,Me’rouane Debbah†, and Matti Latva-aho
Centre for Wireless Communications, University of Oulu, Oulu,Finland, and †Mathematical and Algorithmic Sciences Lab, Huawei
IntroductionI To meet the massive mobile data demand1:
I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands
Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020
12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.
May 20, 2016 3 / 17
IntroductionI To meet the massive mobile data demand1:
I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands
Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020
12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.
May 20, 2016 3 / 17
IntroductionI To meet the massive mobile data demand1:
I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands
Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020
12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.
May 20, 2016 3 / 17
IntroductionI To meet the massive mobile data demand1:
I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands
Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020
12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.
May 20, 2016 4 / 17
Solutions
I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)
I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling
I A network utility maximization problem subject todynamically varying wireless backhaul and network stability
May 20, 2016 4 / 17
Solutions
I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)
I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling
I A network utility maximization problem subject todynamically varying wireless backhaul and network stability
May 20, 2016 4 / 17
Solutions
I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)
I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling
I A network utility maximization problem subject todynamically varying wireless backhaul and network stability
May 20, 2016 5 / 17
ToolsI Random Matrix Theory2
I Large number of antennas N , number of UEs K
I Stochastics optimization3
I Large number of variables and constraints, and dynamicload.
I Success approximation convex method4
I Convert the non-convex program by its solvable convexupper bound.
2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.
3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.
4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.
May 20, 2016 5 / 17
ToolsI Random Matrix Theory2
I Large number of antennas N , number of UEs K
I Stochastics optimization3
I Large number of variables and constraints, and dynamicload.
I Success approximation convex method4
I Convert the non-convex program by its solvable convexupper bound.
2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.
3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.
4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.
May 20, 2016 5 / 17
ToolsI Random Matrix Theory2
I Large number of antennas N , number of UEs K
I Stochastics optimization3
I Large number of variables and constraints, and dynamicload.
I Success approximation convex method4
I Convert the non-convex program by its solvable convexupper bound.
2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.
3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.
4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.
May 20, 2016 5 / 17
ToolsI Random Matrix Theory2
I Large number of antennas N , number of UEs K
I Stochastics optimization3
I Large number of variables and constraints, and dynamicload.
I Success approximation convex method4
I Convert the non-convex program by its solvable convexupper bound.
2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.
3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.
4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.
May 20, 2016 6 / 17
System Model
MBSFD-SC
MUE
Massive MIMO Antennas
beamforming
D: Queue buffer
Dataflow
ISD: 250, 125, 100, ..., 35 m
Q: Network Queue
Data
FD-SC
SUEwir
eless b
ackhau
l
data access
MUE served by MBS
and interfered by SCs
SUE served by SC only
and interfered by MBS
Figure: Network Scenario.
May 20, 2016 7 / 17
Network Assumptions
I N number of antennas, M number of macro users (MUEs),S number of SCs
I N ≥ (K = M + S) ≥ 1
I Dense deployment of SCsI Full-duplex capacityI Two antennas, small cell user (SUE) per each
I Co-channel time-division duplexing (TDD) protocol
I Imperfect channel state information (CSI)
May 20, 2016 8 / 17
Queue Buffer at SC
I rs(t) and rm(t) be the data rates from MBS to SC andMUE, respectively.