Nicolò Michelusi([email protected]),Muddassar Hussain,DavidLove(Purdue)Anoosheh Heidarzadeh,AlexSprintson (TAMU)
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NSF funded project: NSF EARS CNS-1642982: "Real-time Control of Dense, Mobile,Millimeter Wave Networks Using a Programmable Architecture" (2016-2019),Nicolò Michelusi (PI), David Love (co-PI),James Krogmeier (co-PI), PurdueUniversity; Alex Sprintson (co-PI), TAMU; Chris Anderson (co-PI), USNA
CNS-1642982
0 0.2 0.4 0.6 0.8 1False alarm probability,
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1-P M
Dw
orst
( , ) = 0.02( , ) = 0.08( , ) = 0.14
[1]
Mm-waveinmobile,denseenvironments
Architecture
Energyefficientbeamalignment(BA)v Problem:aligntx/rx beamswithmobileusers;QoS constraints;minpower
AdaptiveBAprotocol
Fractionalsearch Performance
Summary
v Mobilitymaydisruptgainsofmm-wave
Ø Beam-alignment(BA)bottleneck
1. SDNarchitectureØ Real-timecontrolinfast-varyingnetworksØ X-layertoreducecomm.overhead
2. AdaptiveenergyefficientBAprotocol3. Neyman-Pearsonbeamdesign
v Futuremobile&densenetworksdemanda ..Highflexibilitytoaddressoverwhelming .....communicationoverhead
1) Weproposeaflexible architecturefordynamicnetworkcontrolviaSDN
2) Wedesignflexibleadaptivebeam-alignmentprotocoltominimizeenergycost&supportQoS
3) Weaddressbeam-design todirectlyincorporatedetectionperformance
v “Fractionalsearch”optimal:
v iteratively,inclosedform
Rateconstraint
Detectabilityconstraint
Coverageconstraint2)DataCommunication
1)Beamalignment
ITA’17,Asilomar’17
!
�s
µ⇤= argmin
µE"L�1X
k=0
Ek + (N � L)PLTslot
����S0
#
s.t.
N � L
NW
tot
log
2
✓1 + �
PL
!L
◆� R,
Ek = �s!k, 8k < L,Z ↵L+!L/2
↵L�!L/2SL(✓)d✓ = 1,
⇢k
⇢k =
(4⇢2
k+1+4⇢k+1�1
8⇢k+1, ⇢k+1 > 1
2 ,
⇢k+1, 0 < ⇢k+1 12
⇢L =�d
�s
Energy/radianttoattainrateconstraint
Energy/radiantfordetectability
0 5 10 15 20 25 30 35 40 450
0.5
1R = 1 MbpsR = 10 MbpsR = 100 MbpsR = 1000 Mbps
Slotindex,k
10−1 100 101 102 103 10434
36
38
40
42
Bisection searchFractional search exact
Rate(Mbps)
Power(d
Bm)
MDPformulationv Adaptivebeamalignmentprotocolasfinite. horizonMarkovdecisionprocess
Ø Timeinterval k=0,…,L-1Ø State Uk:widthofuncertaintyareaØ Action :beamwidthØ Cost :sensingenergyØ Finalcost :datacomm.energyØ Transitions:dependonACK/NACK
v Analysisofcost-to-gofunction&structural. properties
�s!k
�dUL
!k
!⇤k(Uk) =
1
2fkUk, fk =
✓1� 1
2⇢k+1
◆+
f kFractio
nalfactor
NPBeamDesignv Hypothesistestineachsensingslot:
v Neyman-Pearsondetector:
v Beamdesignproblem:
|sHy|2
ksk22
H0
7H1
�
minc2CMt
PMD(c)
s.t. PFA(c) �, kck22 = 1
H1 : y = ↵pPdH(✓)cs+w, ✓ ⇠ U(Rmain
c )
H0 : y = ↵pPdH(✓)cs+w, ✓ ⇠ U(Rsec
c )
NPBeamDesignProblemv Modifiedproblemviarelaxation:
v Optimalbeam:
D�,⌫ , D1 diag(�)DH1 �D0 diag(⌫)D
H0
max
z2R,kck22=1
z
s.t. z Gain(c, ✓), 8✓ 2 Dmainc
Gain(c, ✓) �(�, �), 8✓ 2 Dsecc
Performance
[1].J.Song,J.ChoiandD.J.Love,"Codebookdesignforhybridbeamforminginmillimeterwavesystems," 2015IEEEInternationalConferenceonCommunications(ICC),London,2015,pp.1298-1303.
33%
v Main-lovegainimproves .. bydecreasing
v Lessenergydispersion .... outsidemain-lobe
v Betterdetectionperformance.byNP-baseddesign
v Upto30%improvementover. state-of-art[1]
�(�, �)
-1.5 -1 -0.5 0 0.5 1 1.5AoD,
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Gai
n(c,
)
( , ) = 0.02( , ) = 0.08( , ) = 0.14
[1]Rguard Rguard
c?(�,⌫) =vmax
(D�,⌫)
kvmax
(D�,⌫)k2
Weightedarrayresponsematrix
main sidedualvars
optimizedviaSDP
- :beam-width- U:intervalofuncertainty- :energy/radiant
ACMmmNets’17
SDNframeworkgoalsv Enable ..........programmability..atMAC&PHY ....layers
v Enabledifferent...per-packet ......behaviors
v Defineand .......incorporate ......wireless primitives
ApplyClearWrite
Metadata
Meter
Instruction Set
Goto
Output(1)
Group(2)eth(src=)icmpv6(type=)
Action List
Action Set
table: 0 buffer: 0meter: 0queue: 0
Action Set
eth(src=)eth(dst=)queue(5)output(2)
Ethernet VLAN VLAN IPv4 TCP Payload
14B 4B 4B 20B 20B 1000B1063B
src: 00:02:03:04:05:06dst: 00:20:30:40:50:60type: VLAN (0x8100)
pcp: 0vid: 1000type: VLAN (0x8100)
pcp: 0vid: 100type: IPv4 (0x0800)
dscp: 0ecn: 0protocol: TCP (6)src: 10.0.0.1dst: 11.1.1.1
src: 10000dst: 5060
m / a / c / w / md / gInstructions
ChoiceArrival Extraction Selection Execution Egress
Group
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