This project was sponsored in part by NSF through contract ECCS-0948132
Rahul Amin, Dr. Jim Martin
Dr. Ahmed Eltawil, Amr Hussien
Clemson University, Clemson SC
University of California, Irvine CA
Contact: jim.martin@cs.clemson.edu
http://www.cs.clemson.edu/~jmarty
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Introduction
Problem Statement
Background
System Description
Simulation Methodology
Results and Discussion
Conclusions
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Next Generation Wireless Networks (HetNets)
 Made up of several Radio Access Technologies (RATs) such as 2G/3G/4G and Wi-Fi
 User devices are reconfigurable (or multi-modal) and support a multitude of RATs
 Joint allocation of network-wide resources in this hetnet environment is shown to be
more efficient than ‘independent’ resource allocation by each RAT
 Frameworks to support network-wide resource allocation process have been defined by
3GPP (CRRM, MRRM, JRRM) and IEEE (802.21, P1900.4) working groups
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Our Previous Work
 Studied the spectral efficiency vs. energy consumption tradeoffs while using
reconfigurable devices in a hetnet system
 For a ‘balanced’ network topology, Random Waypoint mobility model, and an FPGAbased reconfigurable device, we showed an increase in spectral efficiency of 75% at the
cost of twice (2x) the energy consumption
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The network topology, mobility model and hardware assumptions had a
significant impact on ‘moderate’ improvements (75%) in spectral efficiency
that were shown in our previous work
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Analyze spectral efficiency vs. energy consumption
tradeoffs resulting from realistic hetnet assumptions
that show a ‘significant’ improvement in spectral
efficiency
 Explore an ‘unbalanced’ network topology where resources of
one cellular operator exceed those of another operator
 Implement a clustered node movement pattern that changes a
highly unfavorable situation to a favorable situation when
network-wide resources are jointly allocated
 Study the differences in energy consumption for several
reconfigurable radio hardware assumptions:
▪ (i) ASIC-based radio
▪ (ii) FPGA-based radio
▪ (iii) Combination of ASIC and FPGA based radio
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Towards widespread adoption of HetNet system concept
 Need: Due to proliferation of smartphones, user data demand is
outpacing operator capacity
 Steps taken: (i) ‘Wi-Fi offloading’ problem is being rigorously
studied by cellular operators where cellular systems interoperate
with 802.11 Wi-Fi networks
(ii) ‘Femto-cell’ is utilized in practice today to increase spectral
efficiency by supplementing the macro-cell with an overlay of
smaller, co-operative networks
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Energy-efficient reconfigurable device architectures (MPSoC)
are being investigated
 Based on various hardware components such as ASICs, FPGAs and DSPs
Nodes (user devices) either have (ASIC-based) static radios
capable of operating in limited number of connectivity modes or
they have (ASIC+FPGA or FPGA-based) reconfigurable radios
capable of operating in any connectivity mode
• Nodes can connect to one or more Autonomous Wireless
Systems (AWS) simultaneously
• Each AWS has a controller that represents all nodes in the AWS
and that serves as gateway connecting the AWS with other
AWSs or external networks
• A Global Resource Controller (GRC) implements a centralized
scheduler that maps users to access technologies
• Wireless Virtual Link Layer multiplexes/de-multiplexes data for
users associated to multiple AWSs
• Vertical Handovers are initiated by the GRC
•
 Reconfiguration handoff - Radio reconfigures itself to operate over a
different AWS
We formulate use cases that assume presence of 2
major cellular carriers in a given area
 Use Case 1
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 No co-operation between the two carriers
 Users use multiple static radios that can connect to its own
carrier’s access technologies
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Use Case 2
 Co-operation exists between the two carriers
 Reconfigurable radios are used to support access
technologies implemented by the other carrier
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Select trade-offs between two conflicting objectives
 Maximize overall system throughput (Max sum-rate)
 Maximize fairness amongst users (Max-Min Fair)
Comes up with user to access technology mappings
every 1 second
 Scheduler properties
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 Wi-Fi resources are evenly distributed
 Cellular resources are distributed using a two-step approach:
▪ Allocate minimum required throughput  to each user using its best
cellular radios
▪ Allocate unused resources to a window of  users with best connectivity
parameters in increments of 
▪ Any user assigned total throughput of 1 Mbps is not assigned any further
cellular resources
 The parameters ,  and  help tune the fairness and overall
system throughput characteristics obtained by the scheduler
 For the results presented in this study, we use (α,β,ω) = (100k,
100k, 10)
•
•
2 * 2 km2 grid
Carrier 1 – bad coverage area
–
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Carrier 2 – good coverage area
–
•
•
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2 EVDO (3G) base-stations at the edge of the grid
1 HSPA (3G) and 1 LTE (4G) base-station at the center of the grid and 6 IEEE 802.11g (Wi-Fi)
APs spread throughput the topology
Each technology supports adaptive Modulation and Coding Scheme (MCS)
MCS mapping for each user is determined based on distance of the user from the
Base-Station
100 nomadic users (50 subscribed to Carrier 1 and 50 to Carrier 2)
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Carrier 1 users grouped in 2 clusters; one cluster located at left edge of grid and the other at
the right edge of the grid
– Carrier 2 users are grouped in 1 cluster located at the center of the grid
– Movement of each user follows Random Waypoint Model and is restricted to an area of 500 *
500 m2 from its initial starting position
Experimental Parameters
–
–
•
Network Outage: Varied between 0-25% in increments of 5%
Impact of Reconfiguration: Increased Energy Consumption and Communications Downtime
multiplied by a scalar ∈ [0,1]
Simulation run for 10,000 seconds
Carrier 1:
EVDO
Carrier 1:
WiFi
Carrier 2:
HSPA
Carrier 2:
LTE
Carrier 1:
WiMAX
Carrier 2:
WiFi
Carrier 1:
WiFi
Carrier 2:
WiFi
Carrier 1:
WiFi
Carrier 2:
WiFi
• Worst Case: For no network outage
and impact of reconfiguration equal to
1, the spectral efficiency gain for use
case 2 (1.43 bits/sec/Hz) when
compared to use case 1 (0.34
bits/sec/Hz) is 314.30%.
• Best Case: For 25% network outage
and no impact of reconfiguration, the
spectral efficiency gain for use case 2
(2.73 bits/sec/Hz) when compared to
use case 1 (1.79 bits/sec/Hz) is
553.70%.
• Due to an unbalanced network
topology and clustered movement
pattern, the spectral efficiency
increase which is in the range [314.3%,
553.7] is ‘significantly’ higher
compared to [14.3%, 75%] obtained
for our previous study
314.3%
553.7%
• Energy Consumption Model:
Ptotal = arun éëb.Pdyn,FPGA + (1- b ).Pdyn,ASIC ùû
+a rec éël.Prec,FPGA + (1- l ).Prec,ASIC ùû
where:
Pdyn,FPGA represents run-time energy consumption of FPGA-based hardware
Pdyn,ASIC represents run-time energy consumption of ASIC-based hardware
Prec,FPGA represents energy consumption of FPGA-based hardware during reconfiguration
Prec,ASIC represents energy consumption of ASIC-based hardware when switching from
‘off’
to an ‘on’ mode
∝run
represents percentage of time system operates in regular mode
∝rec
represents percentage of time system operates in reconfiguration mode
β
percentage of hardware built using FGPA components
1 – β percentage of hardware built using ASIC components
λ
impact of reconfiguration
• The ratio of Pdyn,FPGA:Pdyn,ASIC is 12:1
• Prec,FPGA and Prec,ASIC values are the same
• Hardware Setting 1: Use Case 1
(Made up of completely ASIC
components, i.e. β = 0) vs. Use Case 2
(Made up of completely FPGA
components, i.e. β = 1 )
• For increase in spectral efficiency of
314.30% shown earlier, for these
hardware assumptions, the increase in
energy consumption is 104.90%
• For increase in spectral efficiency of
553.70% also shown earlier, for these
hardware assumptions, the increase in
energy consumption is 614.9%
• Most pessimistic hardware
implementation for a reconfigurable
radio in practice. Almost linear
tradeoff for worst case increase in
energy consumption
104.9%
614.9%
• Hardware Setting 2: Use Case 1
(Made up of completely ASIC
components, i.e. β = 0) vs. Use Case 2
(Made up of 50% ASIC, 50% FPGA
components, i.e. β = 0.5 )
• For increase in spectral efficiency of
314.30% shown earlier, for these
hardware assumptions, the increase in
energy consumption is 70.0%
• For increase in spectral efficiency of
553.70% also shown earlier, for these
hardware assumptions, the increase in
energy consumption is 355.40%
• Most likely hardware implementation
for a reconfigurable radio in practice.
Increase in spectral efficiency is
greater than increase in energy
70.0%
355.4%
• Hardware Setting 3:
Use Case 1
(Made up of completely ASIC
components, i.e. β = 0) vs. Use Case 2
(Made up of completely ASIC
components, i.e. β = 0 )
• For increase in spectral efficiency of
314.30% shown earlier, for these
hardware assumptions, the increase in
energy consumption is 35.10%
• For increase in spectral efficiency of
553.70% also shown earlier, for these
hardware assumptions, the increase in
energy consumption is 98.80%
• Hardware setting 3 is not really possible
in practice. But gives an estimate of
increase in energy consumption if the
only difference between two use cases is
the number of ‘reconfiguration handoffs’
experienced by devices
35.1%
98.8%
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The gains in spectral efficiency are much greater (increase from 75.5% to
553.7%) for an unbalanced network topology and clustered node
movement pattern when compared to a balanced network topology and
random waypoint movement pattern studied in our previous work
Based on the hardware choices, the increase in energy consumption can
range from 98.80% to 614.90% for the corresponding increase in spectral
efficiency of 553.7%
Depending on the number of possible modalities supported by user
devices, it might be possible to attain a tradeoff in terms of lower energy
consuming ASIC radios at the cost of decreased reconfigurable options
In the worst case, our results show a more or less linear trade-off
between spectral efficiency and power consumption
This result is an artifact of our workload assumptions that assume traffic
flows are always backlogged. In future work, we will explore more
realistic scenarios that involve on/off traffic flows