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Optimizing Resource Allocation in60 GHz Wireless Access Networks
George Athanasiou, Pradeep Chathuranga Weeraddana,Carlo Fischione
Electrical Engineering School and Access Linnaeus Center,KTH Royal Institute of Technology, Stockholm, Sweden
{georgioa, chatw, carlofi}@kth.se
Ericsson Research 06.03.13
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 1 / 51
Group
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 2 / 51
Group Activities
• Research interests• Wireless networking analysis and optimization with applications to 3/4/5G• Wireless sensor networks• Smart grids• Wireless industrial control
• Research projects• European Institute of Technology, Car2X Communications, 2013• European Institute of Technology, LTE in Smart Grids, 2013• Hycon2 EU project, Highly-complex and networked control systems,
2010-2014• Hydrobionets EU project, Autonomous control of large-scale water
treatment plants based on self-organized wireless BI0MEM sensor andactuator networks, 2011-2014
• Swedish Research Council, In-Network Optimization, 2012-1014
• Educational activities• Teaching at PhD, Master and Bachelor level
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 3 / 51
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 4 / 51
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 5 / 51
Growth of Mobile Traffic
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 6 / 51
Growth of Mobile Traffic
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 6 / 51
2012: 70% mobile traffic growth compared to 2011
Growth of Mobile Traffic
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 6 / 51
2012: 51% of the mobile traffic was video
Growth of Mobile Traffic
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 6 / 51
2012: 12 times the entire global internet traffic in 2000
Growth of Mobile Traffic
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 6 / 51
2017: 13 times higher compared to 2012
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
2012: 0.9% of mobile connections
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
End of 2013: # of mobile connected devices > earth population
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
2017: 4G will represent 10% of connections and 45% of mobile traffic
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
2017: 2/3 will be video
LTE Deployment
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 7 / 51
2017: 2,7 GB mobile traffic per smartphone per month
Mobile Traffic Offloading
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 8 / 51
The amount of traffic offloaded from smartphones will be 46%, and the amount oftraffic offloaded from tablets will be 71% in 2017 (Cisco, 2013)
90% of all cellular base stations will be small cells by 2016 (IEEE Spectrum Magazine,Informa Telecoms & Media, 2013)
Priorities
• High bandwidth
• High coverage
• Green
• Cheap
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 9 / 51
Evolution or Revolution?
• Infrastructure moves closer to the users
• Devices communicate directly (D2D)
• Base station is “dying”
• Heterogeneous networks converge
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 10 / 51
Mobile Data Offloading
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 11 / 51
Cost
reduction
Capacity improvement
Mobile Data Offloading
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 12 / 51
Cost
reduction
Capacity improvement
Are there better solutions for mobile data offloading?
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 13 / 51
60 GHz Wireless Access Networks
• Unlicensed short range transmissions inthe 60 GHz millimeter wave (mmW) band
• Achieve Gbps communication
• Reduced interference
• Low-cost mmW transceivers
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 14 / 51
MillimeterWave Band
• History (J.C. Bose, 1897)
• High path loss
• High oxygen absorption
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 15 / 51
60 GHz Wireless Standards
• IEEE 802.11ad
• WiGig
• IEEE 802.15.3c
• WirelessHD
• ECMA-387
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 16 / 51
Applications
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 17 / 51
60 GHz to the Mobile
• 60 GHz small base stations (eg. on lamp posts)
• Downlink offload traffic in 60 GHz band with uplink LTE feedback
• 60 GHz radio on mobile device in receive-only mode
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 18 / 51
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 19 / 51
Optimizing Client Association in60 GHz Wireless Access Networks
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 20 / 51
Optimizing Client Association in60 GHz Wireless Access Networks
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 21 / 51
Optimizing Client Association in60 GHz Wireless Access Networks
• Goal: Minimize the maximum access point (AP) utilization in thenetwork and ensure fair load distribution
• Solution: Distributed algorithm for client association based onLagrangian duality theory and subgradient methods
• Results: Theoretical and numerical analysis
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 22 / 51
G. Athanasiou, P. C. Weeraddana, C. Fischione and L. Tassiulas, “Optimizing Client Associationin 60 GHz Wireless Access Networks”, arXiv:1301.2723, Cornell University Library, 2013 [Online].Available: http://arxiv.org/abs/1301.2723
System Model
• N = {1, . . . , N} APs and M = {1, . . . ,M} clients
• Achievable rate from AP i to client j ∈Mi is
Rij = W log2
(1 +
PijGij(N0 + Ij)W
)• Qj is the demanded data rate of client j
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 23 / 51
W System bandwidth
Pij Transmission power of AP i to client j
Gij Power gain from AP i to client j
N0 Power spectral density of the noise
Ij Interference spectral density at client j
Mi Set of clients that can be associated to AP i
Nj Set of APs that client j could be associated with
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
System Model
• Channel utilization between AP i and client j is
βij =QjRij
• Utilization of AP i is ∑j∈Mi
βijxij
• (xij)j∈Mi are binary decision variables, which indicate the clientassociation
xij =
{1 if client j is associated to AP i0 otherwise
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 24 / 51
Client Association Problem Formulation
minimize maxi∈N
∑j∈Mi
βij xij
subject to Qjxij ≤ Rij , i ∈ N , j ∈Mi∑i∈Nj xij = 1, j ∈M
xij ∈ {0, 1}, j ∈M, i ∈ Nj
• Variable: (xij)i∈N , j∈Mi
• Main problem parameters: (βij)i∈N ,j∈Mi , (Qj)j∈M, (Rij)i∈N ,j∈Mi
• Constraints: a) The demand of client j is less or equal to theachievable rate from AP i to client j, b) Client j can only be assignedto one AP, c) The decision variables are binary
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 25 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Equivalent Epigraph Form
minimize t
subject to∑
j∈Miβij xij ≤ t, i ∈ N∑
i∈Nj xij = 1, j ∈Mxij ∈ {0, 1}, j ∈M, i ∈ Nj
• Variable: (xij)i∈N , j∈Mi and t
• Main problem parameters: (βij)i∈N ,j∈Mi
• Mixed integer linear program (MILP)
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 26 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Solution Method Challenges
• Existing MILP solvers are centralized
• Typically based on global branch and bound algorithms ⇒ theworst-case complexity grows exponentially with the problem size
• Even small problems, with a few tens of variables, can take a verylong time
• Our approach: Lagrangian duality + Subgradient methods ⇒decentralized
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 27 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Solution Method Challenges
• Existing MILP solvers are centralized
• Typically based on global branch and bound algorithms ⇒ theworst-case complexity grows exponentially with the problem size
• Even small problems, with a few tens of variables, can take a verylong time
• Our approach: Lagrangian duality + Subgradient methods ⇒decentralized
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 27 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Lagrangian Duality
Partial Lagrangian
L(t,x,λ
)= t+
∑i∈N
λi
( ∑j∈Mi
βijxij − t)
= t
(1−
∑i∈N
λi
)+∑j∈M
∑i∈Nj
βijλixij
λ = (λi)i∈N : multipliers for the first set of inequality constraints
Dual functiong(λ)
= inft∈IRx∈X
L(t,x,λ
)X =
{x=(xij)j∈M,i∈Nj
∣∣∣∣∣ ∑i∈Nj
xij=1, xij∈{0, 1}, j ∈M i∈Nj
}.
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 28 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Lagrangian Duality
Partial Lagrangian
L(t,x,λ
)= t+
∑i∈N
λi
( ∑j∈Mi
βijxij − t)
= t
(1−
∑i∈N
λi
)+∑j∈M
∑i∈Nj
βijλixij
λ = (λi)i∈N : multipliers for the first set of inequality constraints
Dual functiong(λ)
= inft∈IRx∈X
L(t,x,λ
)X =
{x=(xij)j∈M,i∈Nj
∣∣∣∣∣ ∑i∈Nj
xij=1, xij∈{0, 1}, j ∈M i∈Nj
}.
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 28 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Lagrangian Duality
Dual problem
maximize g(λ) =∑
j∈M gj(λ)
subject to∑
i∈N λi = 1
λi ≥ 0, i ∈ N
• Variables: λ = (λi)i∈N
• gj(λ) is the optimal value of the subproblem
minimize∑
i∈Nj βijλixijsubject to xj ∈ Xj ,
with the variable xj .
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 29 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Lagrangian Duality
Dual problem
maximize g(λ) =∑
j∈M gj(λ)
subject to∑
i∈N λi = 1
λi ≥ 0, i ∈ N
• Variables: λ = (λi)i∈N
• gj(λ) is the optimal value of the subproblem
minimize∑
i∈Nj βijλixijsubject to xj ∈ Xj ,
with the variable xj .
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 29 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
solved @ client j
Subgradient Method
Subgradient method to solve dual problem
λ(k+1) = P(λ(k) − αku(k)
)• P : Euclidean projection onto the unit simplex Π = {λ
∣∣ ∑i∈N λi = 1, λi ≥ 0}
• αk > 0 is the kth step size
• u(k) =
(u(k)i
)i∈N
: a subgradient of −g at λ(k), where
u(k)i = −
∑j∈Mi
βijx?ij ,
and (x?ij)j∈Miis the solution of the ith subproblems with λ = λ(k)
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 30 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
Subgradient Method
Subgradient method to solve dual problem
λ(k+1) = P(λ(k) − αku(k)
)• P : Euclidean projection onto the unit simplex Π = {λ
∣∣ ∑i∈N λi = 1, λi ≥ 0}
• αk > 0 is the kth step size
• u(k) =
(u(k)i
)i∈N
: a subgradient of −g at λ(k), where
u(k)i = −
∑j∈Mi
βijx?ij ,
and (x?ij)j∈Miis the solution of the ith subproblems with λ = λ(k)
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 30 / 51
i = 1 i = 3
i = 2
j = 1
23 (Q3)
45
6
7 8
9
10
R33R13
price update
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 31 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 32 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcastprice broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute pricesconstruct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcastprice broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes uiAP i computes ui
construct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. initinit
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute pricesconstruct u / compute prices
stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcast
determine local association
clients signals its best AP
AP i computes ui
construct u / compute prices
stopping criterion ?stopping criterion ?
.
.
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Illustrationa
. init
price broadcastprice broadcast
determine local associationdetermine local association
clients signals its best APclients signals its best AP
AP i computes uiAP i computes ui
construct u / compute pricesconstruct u / compute prices
stopping criterion ?stopping criterion ?
.
..
YES
NOAP 1 AP 2
AP 3
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 33 / 51
DAA Properties
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 34 / 51
Proposition
Let g(k)best denote the best dual objective value found after k subgradient
iterations, i.e., g(k)best = max{g(λ(1)), . . . , g(λ(k))}. Then, ∀ε > 0
∃n ≥ 1 such that ∀k k ≥ n⇒(d? − g(k)
best
)< ε.
Theorem
The optimal duality gap of the mixed integer linear program is boundedas follows:
p? − d? ≤ (N + 1)(%+ maxj∈M
%j) ,
where % = maxi∈N ,j∈Mi βij and %j = mini∈Nj βij . Moreover, therelative duality gap (p? − d?)/p? diminishes to 0 as M →∞.
Implementation Over Existing Standards
• Initially the clients follow the RSSI-based association policy that IEEE802.11ad and IEEE 802.15.3c define
• DAA is periodically executed to correct possible suboptimalclient associations by reallocating the available resources
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 35 / 51
Implementation Over Existing Standards
• APs trigger the initialization of DAA by setting a special bit into thebeacon frame
• Information exchange is performed through the control frames orpiggy-backing the data frames
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 36 / 51
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 37 / 51
Numerical Analysis
• Consider a multi-user multi-cell environment
• Compare DAA to• Random association• RSSI-based association (IEEE 802.11)• Optimal association (IBM CPLEX)
• Measure• Convergence• Scalability• Time efficiency• Fairness
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 38 / 51
Topologies
• SNR operating point at a distance d from any AP
SNR(d) =
{P0λ
2/(16π2N0W ) d ≤ d0
P0λ2/(16π2N0W ) · (d/d0)−η otherwise
• Radius of each cell r is chosen such that SNR(r) = 10 dB
• Clients are uniformly distributed at random, among the circular cells
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 39 / 51
λ Wavelength
d0 Far field reference distance
η Path loss exponenti = 1 i = 3
i = 2
i = 1 i = 3
i = 2 i = 4
i = 5
Convergence of DAA
• Average primal objective value from DAA after k subgradient
iterations: P(k) = (1/T̄ )∑T̄
T=1 p(k)best(T )
• Average dual optimal value by DAA: D? = (1/T̄ )∑T̄
T=1 d?(T )
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 40 / 51
50 100 150 200 250 300
1.2
1.4
1.6
1.8
2
2.2
subgradient iterations, k
avera
ge o
bje
ctive v
alu
e
random policy, Prand
RSSI, PRSSI
DAA, P(k)
optimal primal value, P*
optimal dual value, D*
5 APs, 100 clients
50 100 150 200 250 300
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
subgradient iterations, k
avera
ge o
bje
ctive v
alu
e
random policy, Prand
RSSI, PRSSI
DAA, P(k)
optimal primal value, P*
optimal dual value, D*
5 APs, 200 clients
Convergence of DAA
• Convergence time is affected by the number of APs and clients: Thesmaller the network, the faster DAA converges
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 41 / 51
50 100 150 200 250 300
0.75
0.8
0.85
0.9
0.95
1
1.05
1.1
subgradient iterations, k
ave
rag
e o
bje
ctive
va
lue
random policy, Prand
RSSI, PRSSI
DAA, P(k)
optimal primal value, P*
optimal dual value, D*
3 APs, 30 clients
50 100 150 200 250 300
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
subgradient iterations, k
avera
ge o
bje
ctive v
alu
e
random policy, Prand
RSSI, PRSSI
DAA, P(k)
optimal primal value, P*
optimal dual value, D*
10 APs, 100 clients
Scalability of DAA
• Performance improvement increases while the network becomes biggerand more loaded
• DAA performs close to optimal
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 42 / 51
20 30 40 50 60 70 80 90 100
1
1.5
2
2.5
3
3.5
4
total number of clients, M
avera
ge o
bje
ctive
random policy, Prand
RSSI, PRSSI
DAA, P(1000)
optimal primal, P*
optimal dual, D*
2 APs
50 100 150 200 250 300 350 400 450 500
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
total number of clients, M
avera
ge o
bje
ctive
random policy, Prand
RSSI, PRSSI
DAA, P(1000)
optimal primal, P*
optimal dual, D*
10 APs
Scalability of DAA
• Considering constant load, the average objective value decreases whilethe number of APs increases
• DAA performs close to optimal
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 43 / 51
2 4 6 8 10 12 14
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
total number of APs, N
ave
rag
e o
bje
ctive
va
lue
random policy, Prand
RSSI, PRSSI
DAA, P(1000)
optimal primal, P*
optimal dual, D*
40 clients
2 4 6 8 10 12 140.5
1
1.5
2
2.5
3
3.5
4
total number of APs, N
ave
rag
e o
bje
ctive
va
lue
random policy, Prand
RSSI, PRSSI
DAA, P(1000)
optimal primal, P*
optimal dual, D*
100 clients
Optimality of DAA
• Average relative duality gap:
Ave-RDG = (1/T̄ )∑T̄
T=1(p?(T )− d?(T ))/p?(T )
• Average relative duality gap taking into account the best primalfeasible objective value from DAA after K iterations at time slot T :
Ave-RDG-best-achieved = (1/T̄ )∑T̄
T=1(p(K)best(T )−d?(T ))/p
(K)best(T )
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 44 / 51
100 200 300 400 500 6000
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
number of clients, M
rela
tive d
ualit
y g
ap %
2−APs
3−APs
4−APs
5−APs
10−APs
100 200 300 400 500 6000
1
2
3
4
5
6
7
8
number of clients, M
rela
tive d
ualit
y g
ap w
.r.t the b
est prim
al valu
e %
2−APs
3−APs
4−APs
5−APs
10−APs
Fairness Achieved by DAA
• Jain’s fairness index:
J (k)(T ) =(∑i∈N
Y(k)i (T )
)2/(N
∑i∈N
Y(k)i (T )2),
Y(k)i (T ) =
∑j∈Mi
βijx(k)ij (T ) and x
(k)ij (T ) is the solution (best
feasible) resulted from DAA at time slot T after k iterations
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 45 / 51
50 100 150 200 250 300 350 400 450 500
0.88
0.9
0.92
0.94
0.96
0.98
1
subgradient iterations, k
fairn
ess in
de
x
Optimal (CPLEX)
DAA
RSSI
random policy
Speed and Resources Used by DAA
• Empirical CDF plots of the number of iterations for M = 100, 200,300 clients, with N = 10 APs
• Trade-off between optimality and complexity
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 46 / 51
0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time, t (sec)
CD
F(t
)
Empirical CDF
DAA, M=100
optimal (CPLEX), M=100
DAA, M=200
optimal (CPLEX), M=200
DAA, M=300
optimal (CPLEX), M=300
100 150 200 250 300 350 400 450 500
500
1000
1500
2000
2500
3000
total number of clients, M
ave
rag
e t
ime
to
fin
d t
he
o
ptim
al/su
bo
ptim
al so
lutio
n (
se
c)
optimal (CPLEX)
DAA
Outline
• Past, Present and Future in wireless communications
• 60 GHz millimeterWave wireless technology
• Optimizing resource allocation
• Distributed client association (DAA)
• Numerical analysis of DAA
• Conclusions and open research topics
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 47 / 51
Conclusions
• 60 GHz wireless technology: characteristics, benefits, challenges,applications
• Distributed association algorithm (DAA) for optimizing resourceallocation in 60 GHz wireless access networks
• Performance evaluation of DAA: Asymptotically optimal,convergence, time efficiency and fairness
• Integration of DAA into current standards
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 48 / 51
Open Research Topics
• 60 GHz channel modeling
• Medium Access Control (MAC)
• Connectivity maintenance, blockage and directivity
• Coexistence and cooperation with existing wireless technologies
• Multi-hop communications
• ...
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 49 / 51
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 50 / 51
Thank you
Optimizing Resource Allocation in60 GHz Wireless Access Networks
George Athanasiou, Pradeep Chathuranga Weeraddana,Carlo Fischione
Electrical Engineering School and Access Linnaeus Center,KTH Royal Institute of Technology, Stockholm, Sweden
{georgioa, chatw, carlofi}@kth.se
Ericsson Research 06.03.13
Athanasiou, Weeraddana, Fischione (KTH) Optimizing RA in 60 GHz WNs Ericsson Research 06.03.13 51 / 51