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Case Study 1: - Reducing the Energy Consumption of Macro Base Station
1
Cooperative Rotation
Active Cell Rotation
2
Active Cell Rotation Scheme with Cooperation
Proposal 1: Cyclic Activation Based Dynamic Cell Configurations For Homogeneous Deployments
[2] Z. Pan, S. Shimamoto, “Cyclic Activated Power-efficient Scheme based upon Dynamic
C e l l
Configuration”, Proc. of IEEE Wireless Communications and Networking Conference
(WCNC) 2012
Markov Birth-Death Process• Modeling of Traffic Variance
𝑇1 =
𝑝𝑖 ∆𝑡 =𝑁
𝑖
𝜆∆𝑡 𝑖
𝑖!𝑒−𝜆∆𝑡
𝑖
1 −𝜆∆𝑡 𝑖
𝑖!𝑒−𝜆∆𝑡
𝑁−𝑖
𝑤ℎ𝑒𝑛 𝑛 → 𝑛 + 1
𝑞𝑗 ∆𝑡 =𝑁
𝑗𝑒−𝜇∆𝑡
𝑗1 − 𝑒−𝜇∆𝑡
𝑁−𝑗
𝑤ℎ𝑒𝑛 𝑛 → 𝑛 − 1
(Binomial distribution)
n: number of calls in progress N: total users per cell
𝜆: birth rate (times/hour) 𝜇: average duration (s)
• Transition Probability of 7 States
𝑇2 =
𝑃𝐼,𝐽 ∆𝑡 = 𝑖−𝑗>𝑡ℎ
𝑝𝑖 ∙ 𝑞𝑗
𝑄𝐼,𝐽 ∆𝑡 = 𝑖−𝑗<𝑡ℎ
𝑝𝑖 ∙ 𝑞𝑗
𝑅𝐼,𝐽 ∆𝑡 = 1 − 𝑃𝑖,𝑗 ∆𝑡 − 𝑄𝑖,𝑗 ∆𝑡 , 𝑖 − 𝑗 < 𝑡ℎ
𝑃𝐼,𝐽: probability of increase 𝑄𝐼,𝐽: probability of reducing
𝑅𝐼,𝐽: probability of maintain 3
Probability of birth
Probability of death
Traffic variance: 𝑣𝑖𝑗 = 𝑃𝑟 𝑖 − 𝑗
Transition Matrix of 7 States
Simulation Results
Power Consumption of BS Power Consumption of MT
Active Rotation: 39% savings
Cooperative Rotation: 49% savings
4
5
Call Drop Rate Delay Distributions in all postponed calls
𝐸𝐶𝑅𝐵𝑆 =𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑆𝑦𝑠𝑡𝑒𝑚 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦=
𝑊𝑎𝑡𝑡
𝑏𝑝𝑠𝐺𝑓 =
𝑃𝑀𝑆 1 − 𝐷𝑟
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡=
𝐸𝐶𝑅𝑀𝑆
1 − 𝐷𝑟
Summary 1• Markov Modeling and Instantaneous Traffic Simulation
• Performance Evaluation
6
Poisson Arrival
Exponential Terminated
Pure Traffic Variance
(Per Cell Group)
Threshold Control
Transition Probability of 7 States
PerformanceActive
RotationCooperative
Rotation
BS PowerConsumption
39% savings 49% savings
MS Power Consumption
14dB (shadowing) 12dB
19dB (shadowing)17dB
Call Drop Rate2.5% (shadowing)
0.2%0.6% (shadowing)
0.1%
Delay 2.69 (slots) 2.01 (slots)
(W/Gbps)0.016 (shadowing)
0.0120.013 (shadowing)
0.01
(W/Mbps)1.23 (shadowing)
0.641.61 (shadowing)
1.01
Dynamic Cell Configurations
Energy-efficient Cooperation
QoS SatisfiableBS Solution
MUE
MUE
FUE
FAPMUE
FAPFUE
MBS
FAP
FAP
Cross-tier interference (MBS to FUE)Cross-tier interference (FAP to MUE)
intra-tier interference (FAP to FUE)
Q. Crit ical interferences vs.Additional energy consumption
Q. Maintain QoS vs. Sudden traffic
Power adjustment of FAP Extend the coverage of FAP Exploit idle period of FAP
with adaptive sleep patterns
Case Study 2: - Homogeneous & Heterogeneous Deployments
Homogeneous Deployment
Heterogeneous Deployment
Cross-tier interference cancellation
More services by open access
7
Proposal 2: Cell Sizing based Energy Optimization via Sleep Mode
8
Hybrid Access for Near-indoor FUEs
Utility based power controlreduces the intra-interferencewhile sleep activation reducesthe cross-tier interference
Dynamic coverage extensionwith adaptive transmit powerof FAP in variable traffic load
Variable sleep patterns forfemto-cluster make the smallcell deployment becomesenergy-efficient.
[3] Z. Pan, S. Shimamoto, “Cell Sizing Based Energy Optimization in Joint Macro-Femto
Deployments via Sleep Activation”, Proc. of IEEE Wireless Communications and Networking
Conference (WCNC) 2013, Published
CBS
MBS-2
FAP a
FAP b
MUE a
MUE b
MBS-3
MBS-1
Dynamic cell sizing
FUE a
Cross-tier interference(MBS to FUE)
Cross-tier interference (FAP to MUE)
intra-tier interference (FAP to FUE)
Sleep patterns for femto-cluster
9
FAP Coverage Extension in Sleep Edge-MBSExtension without power adjustment Extension with power adjustment
10
(birth rate = 1times/h, open access rate = 4, CBS is active)
Sleep Edge MBS
Active Center MBS
CBS
Sleep Edge MBS
Active Center MBS
CBS
Power Consumption of FAP Cluster Energy Consumption Rating
Proposal 2 Extension:Cell Sizing based Power allocation for Sleep Two-tier Networks
11
Neuron control based poweradjustment makes thetransmit power of FAP becomemore accurate in a faster pace.
The dynamic cell sizing canadapt to the variable trafficload in a self-optimized sense.
Extended FAPs help the MBSsleep longer and make thetwo-tier system becomeenergy-efficient.
Hybrid Access for Near-indoor MUEs
[4] Z. Pan, M. Saito, J. Liu, S. Shimamoto, “Neuron Control-Based Power Adjustment
Scheme for Sleep Two-tier Cellular Networks”, IEEE Wireless Communications and
Networking Conference (WCNC), Apr. 2014
Neuron Control-Based Power Adjustment with Self-Optimizing
• Artificial Neuron Model– Multi-input indicator control with high speed and accuracy.
– Changing transfer function during a learning phase.
12
learning
Basic neural control process
𝑜𝑗 = φ
𝑖=0
𝑛
(𝜔𝑖𝑗𝑥𝑖 + 𝜃𝑗)
: input
: weight
𝜑 : activation function
𝑜𝑗 : output
𝑥1⋯𝑥𝑛
𝜔1𝑗 ⋯𝜔𝑛𝑗
Simulation Results FAP Extension in the Sleep Macrocell
Adjustment by UBPC Adjustment by NC13
Simulation ResultsActive Ratio of a Sleep MBS
Active Ratio vs. Power Consumption of two-tier system
Normalized Target Adjustment Ratio
𝜆: birth rate (times/h)
14
Summary 2
15
• Utility and Traffic Based Power AdjustmentSleep
Macrocell
FAP Coverage Extension
FAPFAP
Cluster
Efficient MBS Efficient FBS
Cross-tier Interference
Power ControlIntra-interference
HybridAccess
SleepPatterns
• Neuron Control Based Power Adjustment
𝑥1, 𝑥2⋯𝑥𝑛 : i n p u t s𝜔1, 𝜔2⋯𝜔𝑛: weights𝜃𝑖 : correction factor𝜑: sigmoid function
Proposal for FAPs Proposal for MBS
Coverage extension with UBPC, FAP Power consumption in different sleep patterns, Energy consumption rating
Coverage extension with NC and UBPC,MBS active ratio vs. two-tier system power consumption in different open access rateAdjustment target ratio vs. convergence time
• Performance Evaluations
Case Study 3: - Handoffs in Heterogeneous Deployments
• Desirable Handoff Features:
Maximize: Reliability, PerformanceMaintain: Seamless mobility,
Load balancingMinimize: Interference,
Number of handoffs
Handoffs in two-tier Heterogeneous Scenario
Horizontal handoff: within the same network technologyVertical handoff: within the different network technology 16
RF Fingerprint Based Localization
• Radio frequency fingerprint (RFF):Identifies the device from which a radio transmission originated by looking at the properties of its transmission, including specific radio frequencies.
17Handoffs in two-tier Heterogeneous Scenario
RFF List is created by CBS
FAP update the RFF to the CBS
CBS sends the RFF list to MUEs
MUE senses and matches the received RFF with the
target FAPHandoff preparation
P-Persistent Robust Handoff Decision
18
P-Persistent handoff1-Persistent handoff
𝑃𝑖𝑛 = 𝑃𝑖𝑛_1 ∙ 𝑃𝑖𝑛_2 ∙ 𝑃𝑖𝑛_3𝑃𝑖𝑛 : Handoff_in FAP probability of MUE𝑃𝑖𝑛_1 : probability of UE movement estimation𝑃𝑖𝑛_2 : probability of channel utility estimation 𝑃𝑖𝑛_3 : probability of UE velocity coordination𝑃𝑜𝑢𝑡 : Handoff_out FAP probability of MUE
𝑃𝑜𝑢𝑡 → UE movement estimation
[5] Z. Pan, M. Saito, J. Liu, S. Shimamoto, “Energy-sensitive Handoff Scheme Employing RF Fingerprint
For Cell Sizing Based Heterogeneous Cellular Networks”, Technical Report, IEICE RCS, Mar. 2014
FAP Handoff-in Probability Estimation
• UE movement estimation
𝑃𝑖𝑛_1 = 𝑃 𝑥 < ∠𝐴𝑈𝐵
= 𝑐𝑑𝑓 𝑃𝑁𝑜𝑟 𝑥 = ∠𝐴𝑈𝐵 = 𝑒𝑟𝑓 ∠𝑉𝑈𝐴 − 𝑒𝑟𝑓 ∠𝑉𝑈𝐵
∠𝐴𝑈𝐵 : max angle that UE can roam into the FAP; 𝑃 𝑥 : probability of angle between the new movement direction and the last movement direction, ∠𝑥~𝑁(0,1)
• FAP channel utility estimation
(M/M/n) queuing model: 𝑃𝑖𝑛_2 = 1 −
𝜌𝑛
𝑛!∙
𝑛
𝑛−𝜌
𝑘=0𝑛−1𝜌
𝑘
𝑘!+𝜌𝑛
𝑛!∙
𝑛
𝑛−𝜌
,
𝜌 traffic intensity, 𝜌 = 𝜆 𝜇, n :maximum number of MUE severed ;
• UE velocity based coordination
𝑃𝑖𝑛_3 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑣𝑀𝐴𝑋 − 𝑡 =1
1+𝑒− 𝑣𝑀𝐴𝑋−𝑡,
𝑣𝑀𝐴𝑋 is the max velocity of UE.
∠𝑋𝑈𝑂 = ∠𝛽 + 𝜋, 𝑖𝑓∠𝛽 < 𝜋∠𝛽 − 𝜋, 𝑖𝑓∠𝛽 > 𝜋
∠𝑉𝑈𝑂 = ∠𝑋𝑈𝑂 − ∠𝑋𝑈𝑉
= ∠𝑋𝑈𝑂 − ∠𝛼
= ∠𝛽 − ∠𝛼 + 𝜋 , 𝑖𝑓∠𝛽 < 𝜋
∠𝛽 − ∠𝛼 − 𝜋 , 𝑖𝑓∠𝛽 > 𝜋
∠𝑉𝑈𝐴 = ∠𝑉𝑈𝑂 + ∠𝜑
∠𝑉𝑈𝐵 = ∠𝑉𝑈𝑂 − ∠𝜑
cos∠𝜑 =𝑈𝐴
2+ 𝑈𝑂
2− 𝑂𝐴
2
2 ∙ 𝑈𝐴 𝑈𝑂19
FAP Handoff-out Probability Estimation
• UE movement estimation𝑃𝑖𝑛_1 = 𝑃 𝑥 < ∠𝐴𝑈𝐵
= 𝑐𝑑𝑓 𝑃𝑁𝑜𝑟 𝑥 = ∠𝐴𝑈𝐵 = 𝑒𝑟𝑓 ∠𝑉𝑈𝐴 − 𝑒𝑟𝑓 ∠𝑉𝑈𝐵
∠𝐴𝑈𝐵 : max angle that UE can roam out of the FAP; 𝑃 𝑥 : probability of angle between the new movement direction and the last movement direction, ∠𝑥~𝑁(0,1)
∠𝑋𝑈𝑂 = ∠𝛽 + 𝜋, 𝑖𝑓∠𝛽 < 𝜋∠𝛽 − 𝜋, 𝑖𝑓∠𝛽 > 𝜋
∠𝑉𝑈𝑂 = ∠𝑋𝑈𝑂 − ∠𝑋𝑈𝑉 = ∠𝑋𝑈𝑂 − ∠𝛼
= ∠𝛽 − ∠𝛼 + 𝜋 , 𝑖𝑓∠𝛽 < 𝜋
∠𝛽 − ∠𝛼 − 𝜋 , 𝑖𝑓∠𝛽 > 𝜋
∠𝑉𝑈𝐴 = ∠𝑉𝑈𝑂 + 𝜋 − ∠𝜑
∠𝑉𝑈𝐵 = ∠𝑉𝑈𝑂 − 𝜋 − ∠𝜑
cos 𝜋 − ∠𝜑 =𝑈𝐴
2+ 𝑈𝑂
2− 𝑂𝐴
2
2 ∙ 𝑈𝐴 𝑈𝑂
20
TSince the increasing traffic
and decreasing SINR
𝜏1 𝜏2 𝜏3
FAP
FAP
MBS
UE
UE
UE
UE
UE
UE
Dynamic Sensing & Small-cell Detection
An example of dynamic sensing intervals
Sensing
𝜏𝑖: Sensing interval of 𝑀𝑈𝐸𝑖𝑘: Coefficient𝛤𝑖: SINR of 𝑀𝑈𝐸𝑖𝛤𝑇𝐻: Threshold of SINR 21
cos∠𝛼 =𝑈𝑂
2+ 𝑈𝑈′
2− 𝑈′𝑂
2
2∙ 𝑈𝑂 𝑈𝑈′, 𝑈𝑈′ = 𝑣𝜏𝑖
𝑈𝑈′ = 𝑈𝑂 ∙ cos∠𝛼 − 𝑈′𝑂2− 𝑈𝑂 ∙ cos∠𝛼
2
𝜏𝑖 = 𝑈𝑂 cos∠𝛼 − 𝑘2 − sin2 ∠𝛼
, If 𝑈′𝑂 =k 𝑈𝑂 , 𝑘 = 𝛤𝑖/𝛤𝑇𝐻
Pin Pout
Pin_th
Pout_th
Pin, Pout
PDF Distribution
Handoff-in Decision:
Pin >= Pin_th
Handoff-out Decision:
Pout >= Pout_th
22
Inside FAP Outside FAP
23
Conclusion
• Scenario: Cell sizing based sleep two-tier deployments
Horizontal Handoff: FAP FAP
Vertical Handoff: MBSFAP
• Decision process: Create the RF fingerprint database (MBS)
Dynamic sensing & RF fingerprint matching (MUE)
Anticipated the available small-cell detection
P-persistent handoff
Dynamic sensing & preparation for the next handoff
• Effectiveness & Targets Less number of sensing Energy-efficient handoff decision
Less number of handoff More reliable sleep two-tier system
24
Future Considerations
• A bright future for cellular is not assured.• Rethinking “cells” in cellular is necessary.• The sustainability and reliability should be given tradeoff.• The ”optimal” way to design cellular networks is wide open for innovation. 25
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