future cellular networks - ict befemto
TRANSCRIPT
Holger Claussen
Department Head, Autonomous Networks & Systems ResearchBell Labs
1. April 2012
Future Cellular Networks
1. Traffic Growth in Wireless Networks
2. The Future of Small Cell Networks
3. Future Macrocellular Networks
AGENDA
4. Industry Trends
5. Conclusions
Traffic growth for communication networks
10-1
100
101
102
103
Traffic (Tb/s)
Wireless Voice
P2P
Wireless data
grows fastest
2010 2015 202010
-2
10
YearData from: RHK, McKinsey-JPMorgan, AT&T, MINTS, Arbor, ALU, and
Bell Labs Analysis: Linear regression on log(traffic growth rate) versus log(time) with Bayesian learning to compute uncertainity.
Transition to LTE will increase capacity trough more spectrum, better scheduling, and MIMO, but these gains are not sufficient.
Energy consumption of networks will become an increasing problem. “More of the same” will not be good enough.
Historic Capacity Gains in Wireless Networks
Wireless Network Capacity Gains 1950-2000
15x by using more spectrum (3 GHz vs 150 Mhz)
5x from better voice coding5x from better voice coding
5x from better MAC and modulation methods
2700x from smaller cells
Total gain 1 million fold
Source: William Webb, Ofcom.
Small Cells - A Necessary Topology Evolution for Future Data Growth
Moving to hierarchical cell structures with small cells can:
• Significantly increase the capacity in the same bandwidth
• Significantly reduce the energy consumption of networks
Separate carrier for femtocells
• private access
• public access
Co-channel operation on a single shared
carrier
• private access NOT FEASIBLE due to high
Frequency deployment options for femtocells today and in the future
Today
Future
Increasing spectral efficiency per area
• private access NOT FEASIBLE due to high interference – coverage holes exist around femtocells with restricted access if no alternative carrier is available
• public access, potential HO issues for fast moving users
Co-channel operation with one shared & one
clean macrocell carrier
• private access – requires one clean macrocell carrier to serve UEs that are in range of femtocells with restricted access
• public access
Technical feasibility of co-channel operation
1
Adding co-channel
femtocells has only very
little impact on the
macro cell throughput
Adding more femtocells
does not affect their
throughput significantly
Results
• Co-channel deployment of femtocells in a macrocellular network is possible without significant impact on the macrocell performance.
• This allows efficient spatial frequency re-use.
• Femtocell throughputs indoors are very high since the wall separation to interference sources results in a high SINR. 64-QAM support recommended.
• Power self-optimization for both DL and UL of the
0 2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
← 64-QAM
rate 1/2
← 16-QAM
rate 1/2
← 4-QAM
rate 1/2
Downlink throughput 3dB from the capacity limit [Mbit/s]
CDF
macro cell, N = 0
macro cell, N = 10
macro cell, N = 100
femto cell, N = 10
femto cell, N = 100
The femtocells are capable of high data rates when
64-QAM or higher modulation is used
Minimum throughput
is around 1Mbps
Example: Downlink results with
shadow fading, bandwidth = 3.84 MHz
• Power self-optimization for both DL and UL of the femtocell is necessary to ensure a low impact on the macrocellular network and to achieve a consistent cell range independent from the distance to the macrocell.
• For co-channel operation with only one available carrier, public access for femtocells is required.
References
[1] H. Claussen, “Performance of macro- and co-channel femtocells in a hierarchical cell structure," in Proc. 18th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Athens, Greece, Sept. 2007.
[2] H. Claussen, “Co-channel operation of macro- and femtocells in a hierarchical cell structure," International Journal of Wireless Information Networks, vol. 15, no. 3, pp. 137-147, Dec. 2008.
[3] H. Claussen, L. T. W. Ho, and L. G. Samuel, “An overview of the femtocell concept," Bell Labs Technical Journal, vol. 13, no. 1, pp. 221-245, May 2008.
Results with Ocelot Coverage EngineScenario Overview for coverage analysis
Simulated test scenario Predicted transmission for various standard wall
types versus cosine of angle of incidence
Realistic house floor plans used for the simulation
Results with Ocelot Coverage EngineCoverage Analysis Results
Covera
ge p
robability
Covera
ge p
robability
Private Access model Public Access model
Co-channel operation of femtocells and macrocells is possible without causing coverage problems when public access is allowed or one clean carrier is available
Distance from femto [m] Distance from femto [m] Results
• Co-channel operation with a private access results in coverage holes for un-registered users.
• With a public access model or when a clean carrier is available the coverage probability around co-channel femtocells is very high.
References
[1] J. D. Hobby and H. Claussen, “Deployment options for femtocells and their impact on existing macrocellular networks,” Bell Labs Technical Journal, vol. 13, no. 4, pp. 145-160, Feb. 2009.
Impact of femtocells on the network energy consumption
• Telecommunications is a large consumer of energy (e.g. Telecom Italia uses 1% of Italy’s total energy consumption, NTT uses 0.7% of Japan’s total energy consumption)
• Increasing costs of energy and international focus on climate change issues have resulted in high interest in improving the efficiency in the telecommunications industry
Opportunity:Small cells have the potential to reduce the transmit power required for serving a user by a factor in the order of 103
compared to macrocells.
Problem: Most femtocells today are not serving users but are still consuming power:
50 Millon femtos x 12W = 600 MW 5.2 TWh/a
Comparison: - Nuclear Reactor Sizewell B, Suffolk, UK: 1195MW- Annual UK energy production: ~400 TWh/a
Source: BBC News - How the world is changing
Reducing energy consumptionIdle mode procedures for femtocellsWhen femtocells become more widely deployed, their energy consumption becomes a concern.
Idle mode procedures can:
• Significantly reduce energy consumption
• Reduce power density in the home
• Reduce mobility procedures and associated signalling
• Reduce interference caused by pilot transmissions
Further work is required for street level deployments
References:
Femtocell activation based on noise rise from active UE
allows to activate the femto only for serving a call
Femtocell energy consumption - Today Femtocell energy consumption – Optimized design
References:
[1] I. Ashraf, L. T. W. Ho, and H. Claussen, “Improving energy efficiency of femtocell base stations via user activity detection," in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Sydney, Australia, Apr. 2010.
[2] H. Claussen, I. Ashraf, and L. T. W. Ho, “Dynamic idle mode procedures for femtocells," Bell Labs Technical Journal, to be published in 2010.
Measurements for noise rise controlled idle modes - Residential house
Measureme
nt p
oint
(1) UMTS Vodafone
PN = −−−−90dBm
(2) UMTS Three
PN = −−−−90dBm
(3) GSMO2
PN = −−−−81dBm
(4) GSMVodafone
PN = −−−−81dBm
Signal Analyzer
Ref 0 dBm Att 0 dB*
A
Center 1.923 GHz Span 5 MHz500 kHz/
3DB
RBW 100 kHz
VBW 300 kHz
SWT 2.5 ms
1 PK
MAXH
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
Noise rise during call to macrocell
Measurement equipment and example result
Antenna
Measureme
nt p
oint S1
Signal P1
[dBm]S2
Signal P2
[dBm]S3
Signal P3
[dBm]S4
Signal P4
[dBm]
1 6/7 −−−−79 6/6 −−−−61 5/5 −−−−22 4/5 −−−−25
2 6/7 −−−−61 6/6 −−−−52 3/5 −−−−18 3/5 −−−−15
3 6/7 −−−−67 5/6 −−−−57 3/5 −−−−28 3/5 −−−−22
4 6/7 −−−−74 6/6 −−−−70 3/5 −−−−30 3/5 −−−−31
5 6/7 −−−−67 6/6 −−−−65 3/5 −−−−33 3/5 −−−−34
6 6/7 −−−−82 6/6 −−−−75 4/5 −−−−28 4/5 −−−−27
7 6/7 −−−−79 6/6 −−−−68 3/5 −−−−32 3/5 −−−−40
8 6/7 −−−−69 4/6 −−−−57 4/5 −−−−29 4/5 −−−−26
Result: All calls for both GSM and UMTS are easily detectable
P1...P4 are the measured noise powers during a call of the test mobile to the macrocell.
S1...S4 is the signal strength indicator displayed by the mobile.
Future Small Cell DeploymentsEnterprise Femtocells / Street level deployments
Click on this video to start
The femtocell concept will be extended to support enterprise applications & street level deployments
This requires several changes:
• Support for more active users
• Higher power
• Different self-optimization algorithms
• Connects via Ethernet
Example: Distributed Coverage Optimization
Algorithm uses local measurements as inputs and adjusts coverage to balance the needs of following objectives:
• minimise coverage holes
• balance load
• minimise overlap and leakage
References
[1] I. Ashraf, H. Claussen, L.. T. W. Ho, “Distributed Radio Coverage Optimization in Enterprise Femtocell Networks”, in Proc. International Conference on Communications (ICC), 2010.
[2] L. T. W. Ho, I. Ashraf, and H. Claussen, “Evolving femtocell coverage optimization algorithms using genetic programming," in Proc. 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC),Tokyo, Japan, Sept. 2009.
Coverage
Future Small Cell DeploymentsCost-effective deployment of large number of cells
Concept: Residential Picocells
• Slightly increase coverage of Femtocells to a cell radius of around 60m (Residential Picocells).
• Increase support to 8 users.
• Use small cell deployed by the user to supplement macrocell coverage
• Allow public access for users of the same operator.
IP Internet
PSTN
GGSN
SGSN
MSCOperator IP Network
operator.
• Use the user’s internet connectionas backhaul.
• This results in no costs for the cell deployment, the site, electricity, and backhaul for the operator.
Questions
What is the financial impact?
What is the impact on the total energy
consumption of the network?
RNC
Macro-cellNode B Residential
Picocell
Traditional UMTS Architecture Small Cell Architecture
Residential Picocell Controller/Gateway
Residential Picocells can significantly reduce the total annual network costs Results
• Macro-cellular networks become less economically viable with an increasing demand of high data rate services due to high operational expenses
• This problem can be addressed by user-deployed publicly accessible residential picocells
• A large fraction of the user demand can be covered by installing home base stations in only a small fraction of the customer’s homes
• Residential picocell deployment in combination with a macro- 0 20 40 60 80 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
fraction of installed HBS
fraction of user coverage
40% market share30% market share
20% market share
10% market share
• Residential picocell deployment in combination with a macro-cellular network for area coverage can potentially reduce the annual network costs by 30% to 70% (2007 technology) [1].
fraction of installed HBS
0 20 40 60 80 1000
5
10
15x 10
6
percentage of customers with Home-BSAnnual network cost [$]
16 users/macrocell
32 users/macrocell
64 users/macrocell
128 users/macrocell
256 users/macrocell
Significant potential for
network cost reduction
References
[1] H. Claussen, L. T. W. Ho, and L. G. Samuel, “Financial analysis of a pico-cellular home network deployment," in Proc. IEEE International Conference on Communications (ICC), Glasgow, UK, June 2007, pp. 5604-5609.
Residential Picocells enable significant improvements in energy efficiency Results
• A mixed macro- and residential picocell architecture can significantly reduce the energy consumption of cellular networks for high data rate user demand in urban areas where macrocells are capacity limited
• The power consumption can be reduced by up to 60% for high data rate demand in urban areas (2007 technology) [1],[2].
• With more dynamic idle mode control and efficient power scaling with load a 46x efficiency improvement is possible
4
5
6
7
8
9
10x 10
4
Total Power [W
]
Macrocells Only
PIFm=15%, PIF
s=15%, γ
m=0.4, γ
s=0.4
PIFm=30%, PIF
s=30%, γ
m=0.2, γ
s=0.3
PIFm=50%, PIF
s=50%, γ
m=0.2, γ
s=0.05
46x scaling with load a 46x efficiency improvement is possible in 2016.
• Operators with high market share benefit more from the advantages since high small cell coverage is achieved with a lower fraction of customers with small cells.
References:
[1] H. Claussen, L. T. W. Ho, and F. Pivit, “Leveraging advances in mobile broadband technology to improve environmental sustainability," Telecommunications Journal of Australia, vol. 59, no. 1, pp. 4.1-4.18, Feb. 2009.
[2] H. Claussen, L. T. W. Ho, and F. Pivit, “Effects of joint macrocell and residential picocell deployment on the network energy efficiency," in Proc. 19th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Cannes, France, Sept. 2008.
2010 2011 2012 2013 2014 2015 20160
1
2
3
Year
Total Power [W
]
46x
improvement
for 2016
lightRadio Cube and Active Antenna Array
Picocell –Macrocell solutions
Enables intelligent antenna
techniques
lightRadio Cube
Active Antenna Array
Digitallink
or Cloud
techniques
Avoids Cable losses (3 dB)
RF
Ethernet or CPRI
Circular Antenna Array Compact higher-order sectorization strategies
Beam 2
Beam3
Beam 1
.
.
.
24 column Array. Diameter: ~21” @2.45 GHz
Bell Labs prototype
Each beam/sector is generated
using 7 adjacent antenna elements.
Performance comparable with MU-MIMO
Large Scale Antenna SystemsReducing Energy ConsumptionOnly one antenna panel is powered to simulate a call to an end-user. All powered but only at a fraction
End-user
Power used = 16W
© 2011 GreenTouch Consortium
End-user
Power used = 1W
Proof of Concept Demonstration, London 2011.Collaborators: Bell Labs, Freescale, Huawei, imec, Samsung
• Many applications are moving to computing clouds to reduce costs and allow dynamic scalability of apps.
• Networks have to support this trend and this inturn will generate new business for our customers.
• Traditional telecom functions will move to computing clouds. This requires developing combined networking and computing solutions.
Industry TrendsNetwork Virtualization and Cloud Computing
Current open problems (not exhaustive list):
• Missing dependability/real-time performance prevents us from running telco functions in the cloud.
• Missing optimization of cloud infrastructure to meet multiple SLAs of more demanding networked cloud apps (e.g. virtual telco)
• Security: missing confidentiality of data and code is a major showstopper for many commercial cloud applications.
Industry TrendsEvolution towards fully Autonomous Networks
Planning and optimisation is
performed manually through
network planning engineers
and drive testing.
Algorithms for auto-
configuration and self-
optimisation are developed
manually, and used to
automate the network
configuration and optimisation.
Algorithms are generated
using automated means,
(genetic programming).
Design process is done offline,
and resulting algorithm is
implemented in the network.
The algorithm generation process is
now distributed and performed locally
and continuously by the network nodes
themselves. Nodes now have ability to
autonomously specialise their individual
behaviour according to environment.
Summary & Conclusions
• Small cells are a necessary topology evolution for future data growth.
• Reserving carriers for femtocells will not be acceptable in the future since this restricts the macrocellular capacity too much.
• Energy efficiency becomes critical when small cells become widely deployed
• The femtocell concept will be extended to enterprise and outdoor applications
• A promising direction for the future evolution of small cells is to change their
objective from providing coverage in the home to supplementing macrocellular objective from providing coverage in the home to supplementing macrocellular
coverage.
• Light Radio enables many interesting radio concepts for Pico- to Macrocell
applications, and significantly reduces the infrastructure footprint.
• Many telco functions can move to computing clouds. Security and confidentiality are key for commercial success.
• Future networks will become more intelligent and highly autonomous