TT4: Green Heterogeneous Wireless Networks

TT4: Green Heterogeneous Wireless Networks Organized with IEEE Global Communications Conference IEEE Globecom 2015 Sunday December 6, 2015, 8:15 – 12:00

Tutorial Organizers

Muhammad Ismail Texas A&M University at Qatar

Muhammad Z. Shakir Carleton University, Canada

Erchin Serpedin Texas A&M University at Qatar

Khalid Qaraqe Texas A&M University at Qatar

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Tutorial Objectives • This tutorial focus on: Green Heterogeneous Wireless Networks • Promoting energy efficiency through: multi-homing resource allocation, network cooperation, integrating different technologies (RF & VLC), and integrating device centric communications (D2D) • What are green network models and solution classifications? • What are green radio resource management approaches? • What are green network management techniques?

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Tutorial Organization • Part I: Introduction to Green Networks (~ 60 minutes) - Need for green communications - Traffic modeling - Energy efficiency and consumption models - Performance Tradeoffs - Green solutions at low/bursty call traffic loads - Green solutions at high/continuous call traffic loads • Part II: Green Multi-homing Resource Allocation (~ 50 minutes) - Heterogeneous wireless medium - Energy efficiency in heterogeneous networks - Incentives for green downlink multi-homing - Challenges in green uplink multi-homing - VLC-RF multi-homing solution • Part III: Network Management Solutions (~ 70 minutes) - Dynamic planning with balanced energy efficiency - Energy efficient cell-on-edge deployment - D2D communications in hierarchal heterogeneous networks - Emerging device centric communications • Summary

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Part I: Introduction to Green Networks

M. Ismail, M. Z. Shakir, K Qaraqe, E. Serpedin., “Green Heterogeneous Wireless Networks,” Wiley and IEEE Press, 2016. M. Ismail, W. Zhuang, E. Serpedin, K. Qaraqe, “A survey on green mobile networking: from the perspectives of network operators and mobile users,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, Aug. 2015.

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Section Agenda • Need for green communications • Traffic models • Energy efficiency and consumption models • Performance tradeoffs • Green solutions at low/bursty call traffic loads • Green solutions at high/continuous call traffic loads 6

Energy Consumption of Wireless Networks • BSs: responsible for radio resource control and mobility management From operator perspective: BS is main source of energy consumption 3 million BSs  4.5 GW power • MTs: voice, data, and video calls + multiple radio interfaces 3 billion MTs  0.2 – 0.4 GW

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Environmental Concerns • Telecommunications industry  2% of CO2 emissions worldwide (expected to double by 2020)

• Disposal of rechargeable batteries (25,000 tons/year) • High heat dissipation and electronic pollution

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Financial Concerns • Significant portion of operating expenses  energy costs • 18% of OPEX in mature markets in Europe • 32% of OPEX in India • 50% of OPEX for off grid cellular networks 9

QoE Concerns • 60% of mobile users complain about their limited battery capacity • Exponentially increasing gap between: offered battery capacity and demand for energy • MT operational time in between battery charging has become a crucial factor

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Green Network Objectives • Reducing energy consumption of communication devices (BSs and MTs) • Taking into account the environmental impact of the proposed solution A cost effective electricity demand schedule for network operator is not a green solution if it does not ensure that the proposed solution is also eco-friendly in terms of the associated carbon footprint 11


Traffic Models Why? • Some energy efficiency and consumption models are defined based on the call traffic load • Different green approaches are adopted at different traffic load conditions • Some green approaches relies on traffic fluctuations to save energy • Performance evaluation using appropriate traffic models 13

Categories of Traffic Models Dynamic

Static Spatial Regional traffic load density

Temporal

Stochastic geometry

FSMC

Long scale Flow level

Poisson-Exponential

FSMC

Short scale Packet level Infinite buffer

Finite 14

Categories of Traffic Models (Continue) Static Traffic Models

Spatial Traffic Models

Fixed set of MTs communicate with fixed set of BSs [1]

Regional traffic load density ρ 𝑥 = λ(𝑥)/𝜇(𝑥) for location 𝑥 [2]

No consideration of mobility of MTs and no call/packet level dynamics

Stochastic geometry location of BSs and MTs follow PPPs 𝛩𝑛 , 𝛩𝑚 [3]

Dynamic Traffic Models Capture spatial and temporal fluctuations of the traffic load

FSMC 𝑀 MTs with 𝐺 𝑀 spatial location 𝐿 = {𝐿1 , … , 𝐿𝐺 𝑀 } states with transition probabilities: captures number of MTs in regions [4] 15

Categories of Traffic Models (Continue) Long scale temporal fluctuations

Call-level temporal fluctuations Call arrival = Poisson, call duration = exponential [6] FSMC M = {0, … , 𝑀} states with transition probabilities [4]

Packet-level temporal fluctuations 𝐴 𝑡 =

𝑝 ψ(𝑡) 𝑁

𝑘 𝑀𝑘 𝑟𝑘 [5]

𝑜𝑚 𝑡 + 1 = max{ 𝑜𝑚 𝑡 + 𝑎𝑚 𝑡 + 1 − 𝑧𝑚 𝑡 , 𝑎𝑚 𝑡 + 1 or = min{ 𝑜𝑚 𝑡 + 𝑎𝑚 𝑡 + 1 − 𝑧𝑚 𝑡 , 𝐹} 16

Categories of Traffic Models Dynamic

Static Spatial Regional traffic load density

Temporal

Stochastic geometry

FSMC

Long scale Flow level

Poisson-Exponential

FSMC

Short scale Packet level Infinite buffer

Finite 17


Power Consumption Models MT

BS Operation only Large-cell Ideal Realistic

Operation and embodied Femto-cell Load independent

Transmission Including Including power only circuit power reception power With PA

Constant

Without PA

BW scale

Load dependent Including temporal fluctuations Backhaul power consumption

Data rate scale 19

Power Consumption Models (Continue)

Large cell BS  Transmission Power

Hardware

Consumption (W)

Microprocessor

1.7

Memory

0.5

Backhaul ct.

0.5

FPGA

2

Memory

0.5

Others

1.5

RF TX

1

RF RX

0.5

RF PA

2

Percentage % 26.4

39.3

34.3

Femto cell BS  Signal processing 20

Power Consumption Models (Continue) Large-cell BS Power Consumption (a) Ideal: BS consists only of energy proportional devices: 𝑃𝑠 = 𝜌𝑃t [2] (b) Realistic: captures the BS traffic load independent power consumption: 𝑃𝑠 = ∆𝑠 𝑃t + 𝑃f [2,5]

Femto-cell BS Power Consumption

(a) Load independent: does not depend on offered traffic load: 𝑃𝑠 = 𝑃𝜇 + 𝑃FPGA + 𝑃t + 𝑃PA [8] (b) Load dependent: depends on traffic load, packet size: 𝑃𝑠 = 𝑃d (𝑞, 𝑙) + 𝑃f [9] 21

Power Consumption Models (Continue) BS Power Consumption Including Temporal Fluctuations 𝑃𝑠 = 0.35𝑃max + 0.4𝑃50 + 0.25𝑃sp Full, half, and sleep mode [10] BS Power Consumption Including Backhaul For micro-wave and optical fiber backhaul links [12]

BS Consumption Including Embodied Energy 𝐸𝑠 = 𝐸e + 𝐸o = 𝐸ei + 𝐸em + 𝐸o Besides operational energy, involves energy consumed by all processes associated with manufacture & maintenance of BS [13] 22

Power Consumption Models (Continue) MT Transmission Only Power Consumption

Including Circuit Power Consumption

(a) Without PA: dos not account for PA efficiency: 𝑃𝑚 = 𝑃𝑡 [14]

(a) Constant: independent of data rate and BW  𝑄𝑚 [1,17]

(b) With PA: accounts for PA efficiency: 𝑃𝑚 = 𝑃𝑡 /ζ [15] Including Reception Power Consumption: Constant term [16]

(b) BW Scale: 𝑃𝑚c = 𝑃𝑚ref + 𝜎𝐵𝑚 /𝐵ref [16] (c) Data Rate Scale: 𝑃𝑚c = 𝛽1 + 𝛽2 𝑅𝑚 [18] 23

Power Consumption Models MT

BS Operation only Large-cell Ideal Realistic

Operation and embodied Femto-cell Load independent

Transmission Including Including power only circuit power reception power With PA

Constant

Without PA

BW scale

Load dependent Including temporal fluctuations Backhaul power consumption

Data rate scale 24

Throughput Models Network Side

MT Side

(a) High Traffic Load: Shannon 𝐶𝑠 = 𝐵𝑠 log 2 det 𝐼 + 𝑃𝐻 [19]

(a) Instantaneous: Shannon 𝑅𝑚 = 𝐵𝑚 log 2 (1 + 𝛾𝑚 /Г)  Instantaneous CSI = large signaling overhead [21]

(b) Low Traffic Load: Area Spectral Efficiency: 𝑇𝑠 = ℙ𝑠 ϛ log 2 1 + ϛ where prob. ℙ𝑠 (ϛ) is average of Pr{𝛾𝑥→𝑢 > ϛ} [20]

(b) Average: 𝑅𝑚 = 𝔼𝐻 {𝐵𝑚 log 2 (1 + 𝛾𝑚 /Г)}  Average over channel state 𝐻 [22] 25

Energy Efficiency and Consumption BS/MT ECG

BS

Low Traffic

OP/IP

ASE/IP

MT

High Traffic NW Cap/IP

Temporal ECRW, TEEER, ECRVL

NW

Single User Multi User Load Indep Load dep Without Error

Absolute With ECR Error

Without Fairness

With Fairness

APC

Rural Urban

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Energy Efficiency and Consumption (Continue) Energy Consumption Gain (ECG)

Generic and relative measure 𝐸base −𝐸test ECG = [23] 𝐸base

EEI: Ratio of attained utility to consumed energy ECI: Ratio of consumed energy to attained utility 27

Energy Efficiency and Consumption (Continue) EEI-BS-Low Traffic: (a) Power Ratio: ratio of OP to IP power: η𝑠 = 𝑃𝑡 /𝑃𝑠 [24] (b) ASE/IP: ratio of ASE to IP power: η𝑠 = 𝑇𝑠 /𝑃𝑠 [20] EEI-BS-High Traffic: Ratio aggregate capacity to IP : η𝑠 = 𝐶𝑠 /𝑃𝑠 [24]

ECI-BS: (a) ECRW: η𝑠 = (0.35𝑃max + 0.4𝑃50 + 0.25𝑃sp )/𝐶𝑠 [25] (b) TEEER: Logarithmic function of ECRW [26] (c) ECRVL: η𝑠 = (0.35𝑃max + 0.4𝑃50 + 0.25𝑃sp )/(0.35𝐶max + 0.4𝐶50 + 0.25𝐶sp ) [27] Absolute: η𝑠 =

𝑃𝑠 /𝐶𝑠 10 log( )[25] 𝐾𝑇 ln(2) 28

Energy Efficiency and Consumption (Continue) MT-EEI-Single User: (a) Without error: 𝑅𝑚 /𝑃𝑚 [17] (b) With error: 𝑅𝑚 𝑓(𝛾𝑚 )/𝑃𝑚 [28]

NW-ECI-Load Independent: η𝑚 = η𝑚 =

MT-EEI-Multi User: (a) Without Fairness: ηtotal = 𝑅𝑚 / 𝑃𝑚 or η𝑚 [21] (b) With Fairness: ηtotal = log(η𝑚 ) [29]

APC: Ratio of power consumption to area [30] NW-EEI-Load Dependent: (a) Rural: [11]

(b) Urban: [11]

Total coverage area Total Power Consumed 𝑀Busy Hour Total Power Consumed 29

Energy Efficiency and Consumption BS/MT ECG

BS

Low Traffic

OP/IP

ASE/IP

MT

High Traffic NW Cap/IP

Temporal ECRW, TEEER, ECRVL

NW

Single User Multi User Load Indep Load dep Without Error

Absolute With ECR Error

Without Fairness

With Fairness

APC

Rural Urban

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Performance Tradeoffs Network Side: Without Fixed Power

𝑃=

𝑅 −1 𝐵 𝐵𝑁0 2

ηEE

ηSE = η (2 SE − 1)𝑁0

With Fixed Power

Minima

Maxima 32

Performance Tradeoffs (Continue) Mobile User Side: Without Circuit Power

With Circuit Power

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Green Solutions at Low Traffic Load BS On-Off Switching

User association

MT RI On-Off Switching

BS operation

Downlink

Without traffic shaping

Uplink

Bidirectional

With traffic shaping 35

BS On-Off Switching User Association

BS Operation

Concentrates MTs in a few BSs to switch off other BSs

(a) Accommodating future traffic demands: reserve BW via traffic prediction [34] (b) Determine wake up instants: number 𝑀 -based or vacation 𝑉-based [35] (c) Switch off mode entrance (BS wilting) and exit (BS blossoming) [36]

Centralized/decentralized

Greedy Switching Decisions (a) User-BS distance [31] (b) Network impact [32]

(c) Coverage holes [33]

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BS On-Off Switching (Continue)

M-based Scheme

V-based Scheme: Single Vacation

37 V-based Scheme: Multiple Vacation

BS On-Off Switching (Continue)

BS Wilting

BS Blossoming 38

MT Radio Interface Switching Downlink Traffic (a) Without traffic shaping: switching decision is based on data packet unavailability at the serving BS  optimal sleep duration [23] (b) With traffic shaping: enable longer idle duration: data buffering at MT or at BS (TCP) [37]

Uplink Traffic Controls also transmission power and MCS  joint opt. according to traffic & channel conditions [38] Bidirectional Traffic Models both uplink and downlink traffic activity  finite general Markov background process [39]

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Green Solutions at Low Traffic Load BS On-Off Switching

User association

MT RI On-Off Switching

BS operation

Downlink

Without traffic shaping

Uplink

Bidirectional

With traffic shaping 40

Green Solutions at High Traffic Load Single NW Access BS

Margin adaptive HetNet Association

MT

OFDMA

Multi-homing BS

MT

Short Comm. Small cells

BS selection NW cooperation & power allocation TDMA

Relays

Multiple Energy Sources D2D

Multiple retailers On-grid and green sources Mixed renewables Multiple 41 batteries

Single Network Access BS (a) Homogeneous Medium  margin adaptive strategy: assign radio resources to MTs for min BS power consumption [40] (b) Heterogeneous Medium: assign MTs to BS/AP for min energy consumption (Data offloading) [41]

MT (a) OFDMA  subcarrier allocation, power control, and joint allocation and control, and dynamic carrier aggregation (PCC & SCC) [42] (b) TDMA  opportunistic transmission [43] 42

Multi-homing Access BS

MT MT determine how many and which BSs to connect to [1] Two sub-problems: BS selection

Multi-homing threshold 𝑅𝑏 (CH gain) beyond which apply multi-homing [44]

Optimal transmission rate for selected BS 43

Short Range Communications Small cells Power gain increases with # of cells (Embodied Energy!) Cell-on-edge & Uniform Inter-cell interference [45] Relays Relays vs small cells Optimal placement (fixed) [46] Relay assignment [47], mobile relays [48]

D2D In-band underlay  interference [49]

In-band overly  optimal split [50] Out-band  coordination [51] 44

Multiple Energy Sources Multiple Retailers How much electricity to procure from each retailer to power BS with min blocking and cost [52] On-grid and Green Energy Objective: maximize green energy utilization and grid energy saving [26]

Mixed Renewables Complementary sources  wind in winter and solar in summer [53]

MT Multiple Batteries Recovery effect: pulsed discharge profile  schedule for parallel batteries [54] 45

Green Solutions at High Traffic Load Single NW Access BS

Margin adaptive HetNet Association

MT

OFDMA

Multi-homing BS

MT

Short Comm. Small cells

BS selection NW cooperation & power allocation TDMA

Relays

Multiple Energy Sources D2D

Multiple retailers On-grid and green sources Mixed renewables Multiple 46 batteries

Summary • Need for green communications • Traffic models • Energy efficiency and consumption models • Performance tradeoffs • Green solutions at low/bursty call traffic loads • Green solutions at high/continuous call traffic loads 47

Part II: Green Multi-homing Resource Allocation M. Ismail, M. Z. Shakir, K Qaraqe, E. Serpedin., “Green Heterogeneous Wireless Networks,” Wiley and IEEE Press, 2016. M. Ismail, W. Zhuang, “Green radio communications in a heterogeneous wireless medium,” IEEE Wireless Communications, vol. 21, no. 3, pp. 128 – 135, June 2014. M. Ismail, E. Serpedin, and K. Qaraqe, “Cooperation incentives and downlink radio resource allocation for green communications in a heterogeneous wireless environment,” IEEE Trans Vehicular Technology, accepted. M. Marzban, M. Ismail, M. Abdallah, M. Khairy, K. Qaraqe, E. Serpedin, “IDC interference-aware resource allocation for LTE/WLAN heterogeneous networks,” IEEE Wireless Communications Letters, accepted. M. Ismail, A. T. Gamage, W. Zhuang, X. Shen, E. Serpedin, and K. Qaraqe, “Uplink decentralized joint bandwidth and power allocation for energy efficient operation in a heterogeneous wireless medium,” IEEE Trans Commun, vol. 63, no. 4, April 2015. M. Kashef, M. Ismail, M. Abdallah, K. Qaraqe, E. Serpedin, “Energy Efficient Resource Allocation for Mixed RF/VLC 48 Heterogeneous Wireless Networks,” IEEE Journal on Selected Areas of Communications, under review.

Section Agenda • Heterogeneous wireless medium

• Energy efficiency in heterogeneous networks • Incentives for green downlink multi-homing • Challenges in green uplink multi-homing • VLC-RF multi-homing solution 49

Heterogeneous Wireless Medium Overlapped Coverage:

Different networks  cellular, WLANs, WMANs Different cells  macro, micro, pico, and femto

Different technologies  RF, VLC, D2D 50

Energy Efficiency in HetNets Existing research: Optimal transmission power allocation, adapt to CH conditions, given some allocated BW Joint BW and power allocation: (a) Uplink: MT with low battery energy and/or bad ch. conditions (MT1) can be allocated larger BW than better (MT2)  saving of MT energy (b) Downlink: same as above (MT3 and 4)  saving of BS energy

51

Research Issues Green Downlink

RF-Only Data Aggregation

Multiple Applications

Win-win cooperation

IDC Interference

Green Uplink

RF-VLC Multi-user system Energyefficiency & reliability 52

Multi-homing for Green Downlink RF-Only: Data Aggregation

Non-cooperative

Cooperative

53

System Model Set ℕ of cellular networks, each with BS set 𝕊𝑛 with overlapped coverage Fixed power consumption 𝑃𝑛𝑠,f and PA efficiency ζ, max. power 𝑃𝑛𝑠,max , and max. BW 𝐵𝑛𝑠,max Set 𝕄𝑛𝑠 of subscribers of network 𝑛 in coverage of BS 𝑠, min. data rate 𝑅𝑚,min Allocated power 𝑃𝑛𝑠𝑚 & allocated BW 𝐵𝑛𝑠𝑚 , channel gain ℎ𝑛𝑠𝑚 54

Non-cooperative Solution min NCS NCS

{𝑃𝑛𝑠𝑚 ,𝐵𝑛𝑠𝑚 }≥0

s. t.

𝑃𝑛NCS

NCS = 𝑃 𝑃𝑛𝑠 𝑛𝑠,f + ζ

NCS 𝑅𝑛𝑠𝑚

≥ 𝑅𝑚,min , ∀ 𝑚 ∈ 𝕄𝑛𝑠

NCS 𝐵𝑛𝑠

≤ 𝐵𝑛𝑠,max , ∀ 𝑠 ∈ 𝕊𝑛

NCS ≤ 𝑃 𝑃𝑛𝑠 𝑛𝑠,max , ∀ 𝑠 ∈ 𝕊𝑛

NCS 𝑃𝑛𝑠𝑚 𝑚 ∈𝕄𝑛𝑠

NCS NCS 𝑅𝑛𝑠𝑚 = 𝐵𝑛𝑠𝑚 log 2 (1 NCS = 𝐵𝑛𝑠

NCS ℎ 𝑃𝑛𝑠𝑚 𝑛𝑠𝑚 + ) NCS 𝑁0 𝐵𝑛𝑠𝑚

NCS 𝐵𝑛𝑠𝑚 𝑚 ∈𝕄𝑛𝑠

Convex Optimization 55

Win-win Cooperative Solution Cooperation  Mutual benefit: Nash Bargain Solution Υ=

N𝐵𝑆 , 𝐵 N𝐵𝑆 ∀ 𝑛, 𝑠, 𝑚 𝑃𝑛𝑠𝑚 𝑛𝑠𝑚 NBS 𝐵𝑛𝑠 ≤ 𝐵𝑛𝑠,max , ∀

NBS 𝑃𝑛𝑠 = 𝑃𝑛𝑠,f + ζ

NBS ≥ 𝑅 𝑅𝑛𝑠𝑚 𝑚,min ∀ 𝑚

𝑛 𝑠 NBS 𝑠, 𝑃𝑛𝑠 ≤ 𝑃𝑛𝑠,max , ∀

𝑠}

NBS 𝑃𝑛𝑠𝑚 𝑚

𝜬 = {(𝑃𝑛 , 𝑃𝑛NCS )∀ 𝑛 ∈ ℕ|Ω𝑁 ∈ Υ}

Determine Υ ∗ ⊂ Υ 56

Win-win Cooperative Solution (Continue) Asymmetric NBS: (𝑃𝑛NCS − 𝑃𝑛NBS )ƍ𝑛

max NBS NBS


ƍ𝑛 log(𝑃𝑛NCS − 𝑃𝑛NBS )

max NBS NBS


ℕ

𝑛

NBS , 𝐵 NBS ∈ Υ ∀ ℕ s. t. 𝑃𝑛𝑠𝑚 𝑛𝑠𝑚

𝑃𝑛NCS

>

𝑃𝑛NBS

∀𝑛 ∈ℕ

Convex Optimization

𝑠 𝐵𝑛𝑠,max

ƍ𝑛 = ℕ

𝑠 𝐵𝑛𝑠,max 57

Win-win Cooperative Solution (Continue) Power allocation

Max. Lagrangian and Min. Dual function

BW allocation No

Data rate

Yes

Optimal joint allocation

Min. Dual function

58

Benchmark: Sum Minimization Solution min SMS SMS


𝑛

𝑃𝑛SMS SMS 𝑅𝑛𝑠𝑚

s. t. 𝑛

≥ 𝑅𝑚,min ∀ 𝑚

𝑠

Convex Optimization

SMS ≤ 𝐵 𝐵𝑛𝑠 𝑛𝑠,max , ∀ 𝑠 ∈ 𝕊𝑛 SMS 𝑃𝑛𝑠 ≤ 𝑃𝑛𝑠,max , ∀ 𝑠 ∈ 𝕊𝑛

59

Simulation Results

60

Simulation Results (Continue)

61




Win-win cooperation

IDC Interference

Green Uplink


Multi-homing for Green Downlink RF-Only: Multiple Applications

Two radio technologies having adjacent bands in same device  OOB Radiations  IDC Interference Ex: LTE B40 (2.3-2.4 MHz) and WiFi (2.41-2.5 MHz) LTE interface = uplink transmission, WiFi interface = downlink transmission  WiFi downlink suffers IDC 63

Modeling IDC Interference

64

Modeling IDC Interference (Continue) LTE BS: set of channels ℂ LTE Rx filter transfer fn: 𝑆(𝑓 − 𝑓𝑐 ) WLAN PSD: 𝑆 ′ (𝑓 − 𝑓 ′ )

𝑉𝑐,𝑚 =

′ 𝑓 − 𝑓 ′ d𝑓 𝑆𝑚 𝑓 − 𝑓𝑐 𝑆𝑚 ′ 𝑆𝑚 𝑓 − 𝑓 ′ d𝑓

𝐼𝑐,𝑚

𝑃𝑚UL 𝑉𝑐,𝑚 = 𝐴𝑚

′ = 𝑉𝑐,𝑚

′ 𝑓 − 𝑓 ′ d𝑓 𝑆𝑚 𝑓 − 𝑓𝑐 𝑆𝑚

′ = 𝐼𝑚

𝑆𝑚 𝑓 − 𝑓𝑐 d𝑓 UL′ 𝑉 ′ 𝑃𝑐,𝑚 𝑐,𝑚 𝑥𝑐,𝑚 𝐴𝑚 ℂ

65

IDC Interference Results

66

IDC Interference Aware Resource Allocation 𝑀

𝐶 DL 𝑃𝑐,𝑚 +

min ′ DL

DL′ } {𝑥𝑐,𝑚 ,𝑡𝑚 ,𝑃𝑐,𝑚 ,𝑃𝑚

s. t.

𝑀

𝑚=1 𝑐=1

𝑃𝑚DL′ 𝑚=1

𝑅𝑚 ≥ 𝑅𝑚,min , ∀𝑚 ∈ 𝕄 ′ ≥ 𝑅′ 𝑅𝑚 𝑚,min , ∀𝑚 ∈ 𝕄 𝑀

𝑥𝑐,𝑚 ≤ 1, ∀𝑐 ∈ ℂ, 𝑥𝑐,𝑚

𝐶

𝑅𝑚 = 𝑐=1

DL ℎ 𝐵 𝑃𝑐,𝑚 𝑐,𝑚 𝑥𝑐,𝑚 log 2 (1 + ) 𝐶 𝑁0 + 𝐼𝑐,𝑚

𝐷𝐿′ ℎ′ 𝑃 𝑚 𝑚 ′ ′ ′ 𝑅𝑚 = 𝑡𝑚 𝐵 log 2 (1 + ′ ′ ) 𝑁0 + 𝐼𝑚 ∈ {0,1}

𝑚=1 𝑀 ′ ≤ 1, 0 < 𝑡 ′ < 1, ∀𝑚 𝑡𝑚 𝑚 𝑚=1

67

IDC Interference Aware Resource Allocation (Continue)

LTE Resource Allocation 𝑀

s. t.

𝑀

𝐶 DL 𝑃𝑐,𝑚

minDL

{𝑥𝑐,𝑚 ,𝑃𝑐,𝑚 }

WiFi Resource Allocation

𝑚=1 𝑐=1

𝑅𝑚 ≥ 𝑅𝑚,min , ∀𝑚 ∈ 𝕄

𝑀

{𝑡𝑚 ,𝑃𝑚 }

𝑚=1

′ ′ s. t. 𝑅𝑚 ≥ 𝑅𝑚,min , ∀𝑚 ∈ 𝕄 𝑀 ′ ≤ 1, 0 < 𝑡 ′ < 1, ∀𝑚 𝑡𝑚 𝑚

𝑥𝑐,𝑚 ≤ 1, ∀𝑐 ∈ ℂ, 𝑥𝑐,𝑚 ∈ {0,1} 𝑚=1

𝑃𝑚DL′

min ′ DL′

𝑚=1 68

Simulation Results

69


70




Win-win cooperation

IDC Interference

Green Uplink


Multi-homing for Green Uplink Set ℕ of cellular networks, each with BS set 𝕊𝑛 with overlapped coverage, 𝐵𝑛𝑠 Set 𝕄𝑛𝑠 in coverage of BS 𝑠 of network 𝑛, all MTs 𝕄 with uplink traffic Allocated power 𝑃𝑛𝑠𝑚 & allocated BW 𝐵𝑛𝑠𝑚 , channel gain ℎ𝑛𝑠𝑚 PA efficiency ζ , fixed circuit power F consumption 𝑃𝑛𝑠𝑚 , BW scaling dynamic component 72

Radio Resource Allocation max

{min η𝑚 } , η𝑚

{𝐵𝑛𝑠𝑚 ,𝑃𝑛𝑠𝑚 }≥0 𝑚∈𝕄

s. t.

𝑅𝑚 = 𝑃𝑚

min , ∀𝑚 ∈ 𝕄 𝑅𝑚 ≥ 𝑅𝑚

𝐹 𝜆 =

s. t.

max

{min {𝑅𝑚 − 𝜆𝑃𝑚 }}


Same constraints

𝐵𝑛𝑠𝑚 ≤ 𝐵𝑛𝑠 , ∀𝑛, 𝑠 𝑚∈𝕄𝑛𝑠 T 𝑃𝑛𝑠𝑚 ≤ 𝑃𝑛𝑠 , ∀𝑛, 𝑠, 𝑚

𝑃𝑚 ≤ 𝑃𝑚T , ∀𝑚 ∈ 𝕄 Concave-Convex Fractional Program

Dinkelbach-type Procedure Solve above optimization and update 𝜆 = min η𝑚 , until 𝐹 𝜆 = 0 𝑚∈𝕄

73

Joint BW & Power Allocation

max

{𝐵𝑛𝑠𝑚 ,𝑃𝑛𝑠𝑚 }≥0

s. t.

𝜃 , 𝜃 = min {𝑅𝑚 − 𝜆𝑃𝑚 } 𝑚∈𝕄

𝜃 ≤ 𝑅𝑚 − 𝜆𝑃𝑚 , ∀𝑚

Convex Optimization

Same constraints

74

Joint BW & Power Allocation (Continue) Initialization

Power allocation No BW allocation No

Data Rate

Yes

Update λ and check convergence

Yes

Optimal joint allocation 75

Sub-optimal Framework Initialization Phase: max

{min 𝔼 η𝑚 }


𝑛

𝔼 η𝑚 = 𝑛

𝑛

𝑠

𝐵𝑛𝑠𝑚 2Ω𝑛𝑠𝑚 𝑃𝑛𝑠𝑚 ) 𝑠 2 log 2 (1 + 𝑁 𝐵 0 𝑛𝑠𝑚 𝑃𝑛𝑠𝑚 F { + 𝑃 𝑠 ζ 𝑛𝑠𝑚 + ψ𝑛𝑠𝑚 𝐵𝑛𝑠𝑚 } 𝑛𝑠𝑚

𝐵𝑛𝑠𝑚 2Ω𝑛𝑠𝑚 𝑃𝑛𝑠𝑚 min log 2 (1 + ) ≥ 𝑅𝑚 2 𝑁0 𝐵𝑛𝑠𝑚

𝐵𝑛𝑠𝑚 ≤ 𝐵𝑛𝑠 , ∀𝑛, 𝑠

T 𝑃𝑛𝑠𝑚 ≤ 𝑃𝑛𝑠 , ∀𝑛, 𝑠, 𝑚

𝑃𝑚 ≤ 𝑃𝑚T , ∀𝑚 ∈ 𝕄

𝑚∈𝕄𝑛𝑠 76

Simulation Results

77


78




Win-win cooperation

IDC Interference

Green Uplink


RF-VLC Internetworking VLC = visible light communications Attractive Features:

Challenges:

1. Large spectrum compared with RF

1. Reliability (LoS)

2. Secure way of communications

2. Realizing an uplink

3. Energy efficient communications RF-VLC Internetworking 80

VLC Transceiver Transmitter:

Receiver:

2 Total driving power: 𝑃dr = 𝑖dr

𝐼rx,𝑚 = ℎ𝑚 𝐼tr,𝑚

𝑃𝑚 is a fraction of 𝑃dr

𝑖rx,𝑚 = 𝜍𝐼rx,𝑚

opt

𝐼tr = 𝒳𝑖dr and 𝑃tr

= 𝐼tr

2 𝑃rx,𝑚 = 𝑖rx,𝑚

81

VLC Channel LoS Model:

LoS = 𝐻𝑚

𝐺 𝜃𝑚

Lambertian Concentrator emission order gain

Incidence Half angle FoV angle

𝜔𝑚 + 1 𝐴𝑚 𝜔 𝜑 𝐹 𝜃 𝐺 𝜃 cos 𝑚 𝑚 𝑚 𝑚 cos 𝜃𝑚 , 𝜃𝑚 ≤ 𝛩𝑚 2 2𝜋𝑑𝑚 0, 𝜃𝑚 > 𝛩𝑚 Optical filter Irradiance gain angle

𝜖2 , 0 ≤ 𝜃𝑚 ≤ 𝛩𝑚 2 = sin 𝛩𝑚 0, 𝜃𝑚 > 𝛩𝑚

82

VLC Interference Single VLC AP = collection of LEDs in ceiling of a room AP coverage = area free of illumination and communications dead zones

83

VLC-RF Internetworking Scenarios Load Balancing

Throughput Maximization

Joint user association and resource allocation

By default, MT connects to VLC AP and when throughput less than threshold  connects to RF AP (single-network or multi-homing) [56]

Ensures no congestion MINLP [55]

network

Uplink Support Downlink is supported by VLC AP and uplink is supported by RF AP [57]

84

Green RF-VLC Internetworking One VLC AP and one RF Femto-cell 𝐵VLC and 𝐵RF = total available BW 𝐵VLC,𝑚 , 𝐵RF,𝑚  allocated BW, 𝑃VLC,𝑚 , 𝑃RF,𝑚  allocated power c c 𝑃VLC and 𝑃RF  fixed power consumption

𝑃VLC and 𝑃RF  max allowed transmission 85

Energy Efficiency Maximization 𝛾RF,𝑚

𝑃RF,𝑚 ℎRF,𝑚 = 𝐵RF,𝑚 𝑁0,RF

𝛾VLC,𝑚

𝑃VLC,𝑚 (𝒳𝜍ℎVLC,𝑚 )2 = 𝐵VLC,𝑚 𝑁0,VLC

LoS NLoS 𝑅RF,𝑚 = 𝐵RF,𝑚 (κRF log 2 1 + 𝛾RF,𝑚 + (1 − κRF ) log 2 1 + 𝛾RF,𝑚 )

𝑅VLC,𝑚 = 𝐵VLC,𝑚 κVLC log 2 1 + 𝛾VLC,𝑚 86

Energy Efficiency Maximization (Continue) max

{𝑃VLC,𝑚 ,𝑃RF,𝑚 ,𝐵VLC,𝑚 ,𝐵RF,𝑚 }≥0

s. t.

𝑅T η= 𝑃T

𝑅VLC,𝑚 + 𝑅RF,𝑚 ≥ 𝑅𝑚 , ∀𝑚 𝑃VLC,𝑚 ≤ 𝑃VLC

𝑚

𝑃RF,𝑚 ≤ 𝑃RF

𝑅𝑇 =

𝑅VLC,𝑚 + 𝑚

𝑚

𝐵VLC,𝑚 ≤ 𝐵VLC 𝑚

η

𝐵RF,𝑚 ≤ 𝐵RF

𝑅RF,𝑚 𝑚

C C 𝑃𝑇 = 𝑃VLC + 𝑃RF +

𝑃RF,𝑚 𝑚

𝑚

Fractional Programming

Dinkelbach & Joint Optimal

87

Simulation Results

88


89

Summary • Heterogeneous wireless medium

• Energy efficiency in heterogeneous networks • Incentives for green downlink multi-homing • Challenges in green uplink multi-homing • VLC-RF multi-homing solution 90

Part III: Network Management Solutions

M. Ismail, M. Z. Shakir, K Qaraqe, E. Serpedin., “Green Heterogeneous Wireless Networks,” Wiley and IEEE Press, 2016. M. Ismail, M. Kashef, E. Serpedin, and K. Qaraqe, “On balancing energy efficiency for network operators and mobile users in dynamic planning,” IEEE Communications Magazine, accepted. M. Ismail, M. Kashef, E. Serpedin, and K. Qaraqe, “Dynamic planning with balanced energy efficiency for network operators and 91 mobile users,” IEEE Online Greencomm’14.

Section Agenda • Dynamic planning with balanced energy efficiency • Energy efficient cell-on-edge deployment

• D2D communications in hierarchal heterogeneous networks • Emerging device centric communications

92

Traditional Dynamic Planning MTs with uplink traffic associated with faraway BS High energy consumption in uplink  MT battery depletion

High dropping rate in the uplink  service quality degradation

93

Challenges Coupling between BS switching and MT association

Two time scale decision process: BS switching = slow scale and MT association = fast scale

New Switch-off and Wake-up Decision Criteria

Switch-off and wake-up criteria  capture impact of MT battery depletion on UL degradation 94

System Model Cluster 𝕊 of BSs in separate bands Cooperative networking scenario Tilting angle 𝑎𝑠  BS coverage 𝐴𝑠,𝑎𝑠

Uplink and downlink video calls Uplink and downlink rates: 𝜆UL , 𝜆DL

Average durations: 𝜇UL , 𝜇DL Capacity, rates, BWs: 𝑀𝑠UL , 𝑀𝑠DL , 𝑅UL , 𝑅DL , 𝐵𝑠UL , 𝐵𝑠DL

Number of served users: 𝑚DL =

Spatial distributions: 𝜌DL (𝐴𝑠,1 ) and 𝜌UL (𝐴𝑠,1 ) DL UL 𝑚 , 𝑚 = 𝑠 𝑠

UL 𝑚 𝑠 𝑠

95

System Model (Continue) Average channel power gain: characterized by path loss Ω = 10−PL[dB]/10

𝑓𝑐 [GHz] PL dB = 𝐴 log10 𝑑 m + 𝐵 + 𝐶 log10 ( ) 5 DL , 𝑎 > 0 𝑃 + ∆ 𝑃 𝑎 , 𝑚 𝑠 𝑠,tx 𝑠 𝑠 𝑠 Average BS power consumption: 𝑃𝑠,DL 𝑎𝑠 , 𝑚𝑠DL = F,𝑠 𝑃0,𝑠 , 𝑎𝑠 = 0 𝑃𝑠,tx 𝑎𝑠 , 𝑚𝑠DL = Average MT power consumption:

𝑃𝑠,UL 𝑎𝑠 , 𝑚𝑠UL

𝑚DL 𝑠 𝑅DL 𝐵DL 𝑠

𝑁0 𝐵𝑠DL (2 ΩDL

= 𝑃𝐶 +

− 1) 𝑚UL 𝑠 𝑅UL 𝐵UL 𝑠

𝑁0 𝐵𝑠UL (2 ΩUL

− 1) 96

Two Time Scale Approach

97

Slow Time Scale System State: Υ 𝑡 = 𝛾UL 𝑡 , 𝛾DL 𝑡 , 𝛾UL 𝑡 =

𝜆UL , 𝛾DL 𝜇UL

𝑡 =

𝜆DL 𝜇DL

UL DL Actions: 𝑊 𝑡 = {𝑎𝑠 (𝑡)∀𝑠 ∈ 𝕊|𝑃BL (𝑡) ≤ 𝜖UL , 𝑃BL (𝑡) ≤ 𝜖DL }

Transition Probabilities: 𝑄 Υ 𝑡 + 1 Υ 𝑡 , 𝑊 𝑡

=𝑄 Υ 𝑡+1 Υ 𝑡

98

Fast Time Scale Approach System State: 𝑋 𝑖, 𝑡 = (𝑚UL 𝑖, 𝑡 , 𝑚DL (𝑖, 𝑡)) Arrivals = Bernoulli process, Service = Geometric  Uplink queue: Geo/Geo/𝑀UL /𝑀UL and Downlink queue: Geo/Geo/𝑀DL /𝑀DL Actions: 𝑈 𝑖, 𝑡 = 𝑃𝑠,DL 𝑎𝑠 𝑡 , 𝑚𝑠DL 𝑖, 𝑡

, 𝑃𝑠,UL 𝑎𝑠 𝑡 , 𝑚𝑠UL 𝑖, 𝑡

𝑃𝑠,mx , 𝑃𝑠,UL 𝑎𝑠 𝑡 , 𝑚𝑠UL 𝑖, 𝑡

𝑃𝑠,DL 𝑎𝑠 𝑡 , 𝑚𝑠DL 𝑖, 𝑡

≤

≤ 𝑃𝑠,mx

Transition Probabilities: 𝑄 𝑋 𝑖 + 1, 𝑡 𝑋 𝑖, 𝑡 , 𝑈 𝑖, 𝑡

= 𝑄 𝑚UL (𝑖 + 99

Decision Cost Function BS expected energy consumption: 𝐸DL (𝑚DL (𝑖, 𝑡)) = 𝑞(𝑚DL (𝑖, 𝑡))

𝑠

𝑃𝑠,DL 𝑎𝑠 𝑡 , 𝑚𝑠DL 𝑖, 𝑡

MT expected energy consumption: 𝐸UL (𝑚UL (𝑖, 𝑡)) = 𝑞(𝑚UL (𝑖, 𝑡))

𝑠

𝑚𝑠UL 𝑖, 𝑡 𝑃𝑠,UL 𝑎𝑠 𝑡 , 𝑚𝑠UL 𝑖, 𝑡

Cost Function: 𝐶f (𝑋 𝑖, 𝑡 , 𝑈(𝑖, 𝑡)) = 𝐸DL (𝑚DL (𝑖, 𝑡)) + 𝛽𝐸UL (𝑚UL (𝑖, 𝑡)) 100

Optimal Decisions Fast scale total value function: 1 𝑉𝜋𝑡 (𝑀0 (𝑡)) = 𝔼{ lim 𝐼→∞ 𝐼

𝐼

𝐶f (𝑋 𝑖, 𝑡 , 𝑈(𝑖, 𝑡))|𝑊(𝑡)} 𝑖=1

Slow time scale immediate cost: 𝐶s Υ 𝑡 , 𝑊 𝑡 , 𝜋𝑡 = 𝔼{𝑉𝜋𝑡 𝑀0 𝑡 } Decision policies: 𝑇

min min 𝔼{ 𝜋∈ℿ 𝜋1 ,..,𝜋𝑡 ∈ℿ

𝐶s Υ 𝑡 , 𝑊 𝑡 , 𝜋𝑡 } 𝑡=1

101

Simulation Results

102


103


104

Section Agenda • Dynamic planning with balanced energy efficiency • Energy efficient cell-on-edge deployment

• D2D communications in hierarchal heterogeneous networks • Emerging device centric communications

105

Serving the Edges… • LTE Advanced should target the cell-edge user throughput to be as high as possible, given a reasonable system complexity • An intelligent interference management control is required to handle cell-edge interferers • A more homogeneous distribution of the user experience over the coverage area is highly desirable and therefore a special focus should be put on improving the cell-edge spectral and energy efficiency performance

Indoor/

Source: HetNets and small-cell Solutions, Qualcomm Atheros, Inc., 2012. 106

Living on the Edge (LOTE)! • Higher uplink power • Non-homogeneous experience • Poor spectral efficiency • Higher co-channel interference • Coverage holes

107

3GPP 36.913 LTE Advanced R11

Higher transmit power

Higher Interference

Source: How to Dimension User Traffic in 4G Networks, Celplan International Inc., May 2014. 108

UNDERSTANDING TWO TIER HETNETS WITH FEMTO-ON-EDGE

109

Two Tier HetNet Macrocell Network

lm

Small-Cell Network

fn

110

Macro-Only Network RT

Rm

Bn

The distance after which mobile users are required to transmit with maximum power to compensate path loss is called threshold distance.

RT

R0

r

Macrocell base station

Macrocell mobile user

Maximum power transmission region High power transmission link between mobile user and base stations

H. Tabassum, M. Z. Shakir, and M.-S. Alouini, “On the Area green Efficiency (AGE) of Heterogeneous networks,” in Proc. Intl. Conf. Global Communs., GLOBECOM’2012, Anaheim, California, USA, Dec., 2012. 111

Small-cell Population Could be probabilistically modelled

All Small-Cells switched off

A. Ekti, M. Z. Shakir, K. Qaraqe, and E. Serpedin, “Downlink power consumption of HetNets based on the probabilistic traffic model of mobile users,” in Proc. IEEE Symp. Personal, Indoor and Mobile Radio Communications, PIMRC’2013, Sep. 2013. 112

Uniformly Distributed Small-cell (UDC) RT Bn

Rm

R0

Bn

r

Rm

UDC

COE

500

121

40

1000

441

80

1500

961

120

2000

1681

160

r

113

Energy Consumption: UDC

114

Interference: UDC

115

Cell-on-Edge (COE) R1

Rm R0

Small-cells are uniformly distributed around the edge of the reference macrocell such that the resultant small-cell deployment is referred to as cellon-edge (COE) configuration.

R2 Rn

Bm

r

Bn

r

Macrocell base station

Macrocell mobile user

Small-cell base station

Small-cell mobile user

Low power transmission link between mobile user and base stations

116

Mobile User Distribution

117

Mobile User Splitting Between Macro and Small-cell Networks Proportional macrocell users

Modified reuse distance - avoid overlapping

118

HetNet Bandwidth Partition

119

Macrocell Channel Allocation Number of users in Macrocell network

120

Small-cell Channel Allocation F – Number of users in each femtocell N F – Number of users in femtocell network

121

ENERGY AWARE TRANSMISSION DESIGN

122

Path-loss Shadowing and Fading

123

Path-loss Model for HetNets

124

Dual Slope Path-loss Model

125

Fading Modeling

126

Downlink Power Consumption

127

Uplink Power Control Mechanisms

128

Slow Power Control Mechanism

Composite shadowing and fading

M. Z. Shakir, H. Tabassum, and M.-S. Alouini, “Analytical bound on the area spectral efficiency of the two tier Heterogeneous networks over generalized fading channels,” in IEEE Trans. Vehicular Technology, vol. 63, no. 5, pp. 2306-2318, Jun. 2014. 129

Uplink Power Adaptation Based on PC Mobile users are considered to be able to estimate/ compensate their path loss while adjusting their transmit power accordingly Uplink receiver at the BS estimates the SINR of the received signal and compares it with the target SINR value. If the received SINR is below the target SINR, a TPC command is transmitted to the mobile user to request an increase in transmit power. Otherwise, the TPC command will request a decrease in transmit power.

M. Z. Shakir et. al, “Green Heterogeneous small-cell networks: toward reducing the CO2 emissions of mobile communication industry via uplink power adaptation,” in IEEE Mag. Communs., vol. 51, no 6, pp. 52-61, Jun. 2013. 130

Scope of Performance Analysis

131

AREA SPECTRAL EFFICIENCY (ASE): ANALYTICAL FRAMEWORK

132

Carrier to Interference + Noise Ratio – Macrocell (1/2) lm

rlm

rlm Bm

lm 1B m

1

rlm

1B m 1

lm 1B m 1

R1

D

R0

Bm

Desired Macrocell

ith Interfering B lm Macrocell

1B m 1

Assumptions: • There are M -1 = 6 interfering co-channel macrocells near to the reference macrocell. • Ignore the Interference from second tier of macrocell networks due to weak signals.

The SINR of the desired mobile user in macrocell is defined as the ratio of the average received power level received from the desired mobile user and the sum of the individual interfering power levels received at the reference macrocell base station from the interfering mobile users which are located in each of macrocell in the first tier of the macrocell network. .

M. Z. Shakir et. al, “Analytical bound on the area spectral efficiency of the two tier Heterogeneous networks over generalized fading channels,” in IEEE Trans. Vehicular Technology, in press, Jan. 2014. 133

Carrier to Interference + Noise Ratio – Macrocell (2/2) Received power at the reference BS

Thermal noise power

Sum of the individual interfering power levels

134

Carrier to Interference + Noise Ratio – Small-cell (1/2) f rfn

fn

1

1B n 1

Bn

1

rfn

2R n

1B n

fn r f

n 1B n

rfn Bn

Bn

n+1

rfn

2R n B n

1B n 1

fn 1B n 1

1

Interference from N-1 small cells - Pros & Cons: complicated analysis; less analytical tractability and Geometrical methods are required to model distances Interference from only two adjacent Small-cell - Pros & Cons: less complicated and analytical tractability.

The SINR of the desired mobile user in Small-cell is defined as the ratio of the average received power level received from the desired mobile user in Small-cell and the sum of the individual interfering power levels received at the reference Small-cell base station from the interfering mobile users which are located in each of Small-cell in the network.


Carrier to Interference + Noise Ratio – Small-cell (2/2) Received power at the reference small-cell

Thermal noise power

Sum of the individual interfering power levels

136

Capacity – Macrocell

M. Z. Shakir et. al, “On the area spectral efficiency improvement of Heterogeneous networks by exploiting integration of macro-femto cellular networks,” in Proc. IEEE Intl. Conf. Communs., ICC’2011, Ottawa, Canada, Jun. 2012. 137

Capacity – Small-cell Network Capacity of femtocell network

Capacity of each femtocell

M. Z. Shakir et. al, “On the area spectral efficiency improvement of Heterogeneous networks by exploiting integration of macro-femto cellular networks,” in Proc. IEEE Intl. Conf. Communs., ICC’2011, Ottawa, Canada, Jun. 2012. 138

ASE of HetNets

M. Z. Shakir et. al, “Analytical bound on the area spectral efficiency of the two tier Heterogeneous networks over generalized fading channels,” in IEEE Trans. Vehicular Technology, in press, Jan. 2014. M. Z. Shakir, et. al, “Spectral and energy efficiency analysis of uplink heterogeneous network with cells on edge,” in Elsevier Jour. PHYCOM, Jun. 2014.

139

Simulations Parameters

300

600

140

ASE vs Macrocell Radius It is clear that the ASE of the COE configuration has been significantly improved when Smallcells are active in the macrocell compared to the MoNet and UDC configurations. This is due to the fact that COE deployment restricts only the cell-edge mobile users to communicate with the Small-cells which enhances the overall network ASE compared to UDC and MoNet configurations

141

Relative ASE Gain vs N Due to reduced link distance between the edge mobile users and their respective smallcell BSs, higher relative ASE gain is observed even with only few active Small-cells in COE configuration. This gain, however, tends to reduce with the further increase in the number of smallcells as the level of interference increases with the population of Small-cells.

142

Capacity Bounds : Assumptions Macrocell best user

Macrocell worst user

Small-cell worst user

Small-cell best user


Bounds on Macrocell Capacity

144

Bounds on Small-cell Capacity

145

Closed-Form Capacity Results


Bounds on ASE Macrocell:

Small-cell:

Finally, bounds on ASE can be calculated as:

Fading severity (shape) and scale parameters

Bounds on HetNet Capacity: sum of bounds on macrocell capacity and Small-cell capacity

147

Analytical Bounds on ASE vs Rm

Bounds are useful for other types of small cell deployments . . .

The bounds provide insights on the gain and loss in the ASE of the desired mobile user in best and worst case interference conditions, respectively. The analytical upper bound on the ASE of the COE configuration is quite tight and the lower bound is comparatively loose, however, it demonstrates the worst case ASE when macrocell and smallcell interferer location is near to the desired cell center.

148

Blurring the Cell-Edges Handovers vs Rn

7

Average number of handoovers (%)

6.8

Rm = 500 m Rm = 300 m

6.6 6.4 6.2 6 5.8 5.6 5.4 5.2 5

Graphical illustration of signal to interference (SINR) ratio based handover process in Macroonly network which facilitate the deployment of femtocells around the edge.

10

20

30

40 50 60 70 80 Radius of femtocell [m]

90

100

 Up to 7 % handovers per cell could be avoided  Handover signaling could be reduced  Longer sleep mode could improve energy efficiency 149

Interferences in HetNets

150

Interference Analysis of HetNets Inter site distance increases

Increased interference from N-1 small-cells

N increases

It can be seen that the received interference power at macrocell BS has been significantly reduced due to the presence of Small-cells arranged on the edge of the macrocell. Moreover, it is also noted that the interference received at a Small-cell BS from N-1 small-cells is almost same compared to the interference experienced from two adjacent Smallcells.

Average interference power received per channel in (i) MoNet; (ii) COE configuration with interference from N-1 small-cells and (iii) COE configuration with interference from two adjacent small-cells. 151

AREA GREEN EFFICIENCY (AGE)

152

Power Savings – Macrocell

153

Power Savings – Small-cell

154

Area Green Efficiency (AGE) of HetNets

H. Tabassum, M. Z. Shakir, and M.-S. Alouini, “On the Area green Efficiency (AGE) of Heterogeneous networks,” in Proc. Intl. Conf. Global Communs., GLOBECOM’2012, Anaheim, California, USA, Dec., 2012. 155

AGE vs Macrocell Radius It is illustrated clearly that the COE configuration outperforms the UDC configuration since the deployment of small-cells at the macrocell edge mandates a reduction in the number of edge mobile users transmitting with the maximum power. The AGE improvement is due to the fact that the number of energy efficient users increase in both UDC and COE deployments with the increase in macrocell radius.

156

AGE vs Target SINR The amount of power saving decreases with the increase in desired received signal power which is self explanatory as the mobile users in the small-cell network require relatively more transmission power to maintain higher desired signal power level.

157

Energy Economics of HetNets

158

% of Active Users

Daily Energy Consumption Profile 15 10 5 0

0

5

10

15

20

Energy consumption [kWH]

2.5 No PC MoNet UDC COE

2

1.5

1

0.5

0

0

3

6

9

12

15

18

21

24

Time in Hours 159

ECOLOGY AND ECONOMICS OF MOBILE NETWORKS

160

Reduction in CO2 Emission HetSNets; UDC

M oNets w/ o PC

M oNets with PC

HetSNets; COE

30

Carbon footprint can be further reduced to 8 Mtonnes (67 percent reduction) by introducing small-cells in HetNets with UDC deployment. Finally, the significant reduction in CO2e emissions of the system can be achieved by introducing small-cells around the edges of macrocells. The proposed HetNets with COE deployment guarantee CO2e emission reduction to 3.5 Mtonnes (82 percent reduction).

25

CO2e emissions [M tonnes]

19 M tonnes in 2016

20

15 11 M tonnes in 2010

10

30 %

5

67 %

82 %

0 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Years


% of Active users

Daily CO2 emissions Profile 15 10 5 0

0

5

10

15

20

150

CO2e emissions [Mtonnes]

M onet s wit hout PC M oNet s Het SNet s wit h UDC

100

Het SNet s wit h COE

50

0

0

3

6

9

12

15

18

21

24

T ime in hours 162

CO2 Emissions of HetNets for variable Sources of Energy

 Natural gas is the most clean source to fuel future telecom infrastructure  Small cell deployment and their dedicated deployments could further reduce the carbon footprint of telecom networks by 2020

163

Low Carbon Economy Index (LCEI) Low carbon economy index (LCEI) generally defined as the amount of CO2 emissions released per capita gross domestic product (GDP).

It can be seen clearly that the LCEI of heterogeneous networks can be reduced significantly in comparison to the LCEI of macro-only networks. The improvement in LCEI is due to the fact that mobile users in HetSNets adapt the transmit power and thereby reduce the energy consumption and CO2 emissions of the uplink


What D2D offers? Spectral Efficiency •

Reduced path-loss (possible better channel conditions)

• Throughput per area • Migration from small cell to smallest

Energy efficiency • Reduced transmission power •

Link reliability •

cells • Feasible for pCell/Phantom cell for

highest data rate provision Multi-hop cooperation in case of coverage Holes and emergency situation.

From multi-hop (star-like topology) to single hop (mesh-like topology)

Virtually Zero Latency •

Extended coverage •

No backhaul power consumption

Suitable for delay-sensitive applications

Backhaul Relaxation, Load Balancing and Traffic Offloading •

Network and device proactive caching 165

Categories of D2D In-band D2D

• In-band D2D • Underlay • Cellular spectrum • Interference issues

• Overlay • Dedicated spectrum >> poor spectrum utilization • Interference controlled environment

Out-band D2D

• Out-band D2D

• Network assisted

• Limited or full supervision of cellular network • Mobility • Interference • Content management • Backhaul/fronthaul and latency – issues

• Decentralized

• Devices establish connection quickly • Fronthaul/backhaul requirement relaxed • Mobility and long term availability of content – issues

166

Device Centric Communications (DCC) Paradigms • Enhancements specifically targeting small cell/ local area deployments (content driven network densification) • Enhancements specifically targeting new use cases such as IoT, MTC and Big Data for public safety, high demand data traffic, content delivery and popular applications, Sub 1GHz, etc. • Evolution of LTE R12 and-beyond (LTE – B, LTE – D) for emerging cross layer features such as Proximity Services (device discovery, pairing, content generation/delivery – Internetworking caching)

Data plane

Split Functional DCC Architecture 167

Potential of 5G Mobile Users (Devices) Devices-toDevice Communications • More efficient use of radio resources (multiple radio interfaces/hybrid/Split frameworks Bluetooth, W-Fi, WiMax etc.) • Selection of devices and radio interfaces (network assisted / application layer driven) • Evolution of mobile devices to support the emerging features in DCS comes at a cost placing stringent demands on mobile device battery life and energy consumption mainly due to two major reasons*: 1. Slowly progressing battery technology 2. Dramatically varying global climate *J. D. Power and Associates demonstrates that the iPhone ranked top in all categories except for the battery life. *According to another recent survey report up to 60% of the mobile users in China complained that the battery consumption is the greatest hurdle while using 4G services.

168

Devices/Radio Interfaces Selection • The objective is to select the source devices and their respective radio interfaces that maximize the total energy efficiency, i.e.,

where energy efficiency is defined as • Optimal selection is based on Ascending Proxy Auction based algorithm. • Each source device defines set of candidate radio interfaces and their energy efficiency and rank them in strict order for selection in network-assisted mode. • Each device in the network exchanges the preferences and priorities through signaling across the layers for device discovery, device and radio interface selection, pairing etc. 169

70% improved EE due to aggregated resources at sink device and higher data rate

More radio resources are available

Splits down energy consumption

Aggregates the data rate

Energy Efficiency vs Device Selection

70% reduced energy consumption

50% reduced energy consumption

More radio resources are available

M. Ismail, M. Z. Shakir, E. Serpedin, and K.A. Qaraqe, “Efficient selection of source devices and radio interfaces for green Ds2D communications,” IEEE WCNC 2016.

170

Optimal Data Packet Split • Optimal data packet distribution algorithm distributes the data packets amongst the selected devices. • The file transfer latency t at the sink mobile device is defined as the duration required to transfer the desired data packets from all source mobile devices to the sink mobile device by aggregating the multiple radio resources and is given • and given by: • The design rationale behind algorithm is to ensure that source devices complete the file transfer at the same time such that Optimal Packet Split Ratio (OPSR) can be found by solving

171

How much it takes now to transfer 80 MB file via Ds2D?

›

Load optimization over two interfaces based on channel ›

Two interfaces transfer the file during the same duration

 Optimal packet split offers low FTL at higher data rate >> lower gain >>

213.3 kps kbs

1227.8 kbs

88% 33%  Random data packet distribution suffering from higher FTL over one of the interface  Direct D2D is better at higher data rate than random packet split

3822.7 kbs

7776.6 kbs

213.3 kps kbs

1227.8 kbs

3822.7 kbs

7776.6 kbs

172

Green Analysis Mobile battery will last 4-5 hours more when sharing 80 MB file over multiple radio interfaces in comparison with single interface. . .

Battery life

Carbon footprint

Cost

*Mobile battery parameters are taken from Samsung S5. More details and simulations settings are available in M. Z. Shakir, M. Ismail, E. Serpedin, and K. Qaraqe, “Prolonging the Battery Life of 5G Mobile Devices via Ds2D Communications,” under review in IEEE Communications Mag.,2015.

173

Summary • Part I: Introduction to Green Networks - Need for green communications - Traffic modeling - Energy efficiency and consumption models - Performance Tradeoffs - Green solutions at low/bursty call traffic loads - Green solutions at high/continuous call traffic loads • Part II: Green Multi-homing Resource Allocation - Heterogeneous wireless medium - Energy efficiency in heterogeneous networks - Incentives for green downlink multi-homing - Challenges in green uplink multi-homing - VLC-RF multi-homing solution • Part III: Network Management Solutions - Dynamic planning with balanced energy efficiency - Energy efficient cell-on-edge deployment - D2D communications in hierarchal heterogeneous networks - Emerging device centric communications 174

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Acknowledgment The speakers would like to acknowledge their fund: NPRP grant # NPRP 6415-3-111 from the Qatar National Research Fund (a member of Qatar Foundation).

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CONTACT US: MUHAMMAD ISMAIL [email protected] MUHAMMAD ZEESHAN SHAKIR [email protected] ERCHIN SERPEDIN [email protected]

KHALID A. QARAQE [email protected]

188

189

Additional Material

190

System Model

191

User Allocation and Distribution (1/2)

1According

to Cisco, wireless usage is shifting indoors where the majority of mobile traffic, approximately 80% is indoor and nomadic, rather than truly mobile. 192

User Allocation and Distribution (2/2) • • • • • •

193

Hierarchical HetNet • • • • •

194

Spectrum Partitioning • • •

195

Transmission Design •

•

196

Traditional HetNet Sum-Rate

197

Hierarchical HetNet Sum-Rate (1/2)

198

Hierarchical HetNet Sum-Rate (2/2)

199

D2D User Density

200

Simulation parameters

201

Simulation Results (Sum-rate improvement)

202

Outage probability  Reduced SIR: For 10% outage probability, dense HHNET (10 milli users/m2) requires 32.7 dB less SIR as compared to traditional HetNet with same user density.  We have SIR gain of 26.6 dB in HHNET in case of 1 milli users/m2.  Overall low outage: At 140 dB, around 95% users are successfully covered for dense HHNET (10 milli user/m2) as compared to 50% in case of traditional HetNet.

203