Presentation

Andrea Goldsmith
Stanford University
Accelera, Inc.
IEEE SCV ComSoc meeting
May 8, 2013
Wireless networks are everywhere, yet…
TV White Space &
Cognitive Radio
- Connectivity is fragmented
- Capacity is limited (spectrum crunch
and interference)
- Roaming between networks is ad hoc
Software Defined Wireless Networking (SDWN):
Accelera’s Architecture
Cloud-Based Control, Management, and
Radio Resource Management Plane
ACCELERA “MAESTRO” UNIFIED CONTROL PLANE
WiFi AP
Small Cell
Wireless HW
Dual Mode
Wifi/LTE
Careful what you wish for…
Growth in mobile data, massive spectrum deficit and stagnant revenues
require technical and political breakthroughs for ongoing success of cellular
“Sorry, America: Your wireless airwaves are full”
CNNMoneyTech – Feb. 2012
The “Spectrum Crunch”
Are we at the Shannon
limit of the Physical Layer?
We don’t know the Shannon
capacity of most wireless channels
 Time-varying channels with memory/feedback.
 Channels with interference or relays.
 Uplink and downlink channels with
frequency reuse, i.e. cellular systems.
 Channels with delay/energy/$$$ constraints.
Rethinking “Cells” in Cellular
Small
Cell
Coop
MIMO
How should cellular
systems be designed?
Relay
DAS
Will gains in practice be
big or incremental; in
capacity or coverage?
 Traditional cellular design “interference-limited”






MIMO/multiuser detection can remove interference
Cooperating BSs form a MIMO array: what is a cell?
Relays change cell shape and boundaries
Distributed antennas move BS towards cell boundary
Small cells create a cell within a cell
Mobile cooperation via relaying, virtual MIMO, analog network coding.
Are small cells the solution to
increase cellular system capacity?
Yes, with reuse one and adaptive
techniques (Alouini/Goldsmith 1999)
Area Spectral Efficiency
A=.25D2p
 S/I increases with reuse distance (increases link capacity).
 Tradeoff between reuse distance and link spectral efficiency (bps/Hz).
 Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.
The Future Cellular Network: Hierarchical
Architecture
Today’s architecture
MACRO: solving
initial coverage • 3M Macrocells serving 5 billion users
issue, existing
network
10x Lower HW COST
PICO: solving
street, enterprise
& home
coverage/capacity
issue
FEMTO: solving
enterprise &
home
Picocell
Macrocell
coverage/capacity
issue
10x CAPACITY
Improvement
Femtocell
Near 100%
COVERAGE
Managing interference
between cells is hard
Traditional Macro vs. SON Enabled H-RAN
Macro BS Only
Chicago Downtown
H-RAN:
Modeling Assumptions:
Macro + Pico BS
1. Chicago Downtown model
(Calculation area: 64.5
km2)
2. 38 Macro BS sites (3
sectors)
3. 340 Pico BS (3 sectors)
4. ~66000 users were
simulated with Monte
Carlo method
H-RAN advantage
 10x CAPACITY
 10x lower $/Mbps

~100%
COVERAGE
Users trying to connect
Connected users
Effective MAC Aggregate
Throughput (DL)
Effective MAC Aggregate
Throughput (UL)
Macro BS Macro + Pico optimized
66680
66680
31023
50902
1020 Mbps
12 060 Mbps
389 Mbps
4 204 Mbps
Macro BS - Cost Per Mbps
Pico BS - Cost Per Mbps (no backhaul/site acq)
$1,341/Mbps
$111/Mbps
CapEx Reduction Factor
12x
Deployment Challenges
Deploying One Macrocell
New site verification
Effort
(MD – Man
Day)
1
On site visit: site details verification
0.5
On site visit: RF survey
0.5
New site RF plan
2
Neighbors, frequency,
preamble/scrambling code plan
0.5
Interference analyses on surrounding
sites
0.5
Capacity analyses
0.5
Handover analyses
0.5
Implementation on new node(s)
0.5
Field measurements and verification
2
Optimization
2
Total activities
7.5 man days
5M Pico base stations in 2015 (ABI)
• 37.5M Man Days = 103k Man Years
•Exorbitant costs
•Where to find so many engineers?
Small cell deployments
require automated selfconfiguration via software
Basic premise of selforganizing networks (SoN)
SON for LTE small cells
Mobile Gateway
Or Cloud
Node
Installation
SoN
Server
Self
Healing
Initial
Measurements
IP Network
SON
Self
Configuration
Measurement
Server
Self
Optimization
X2
X2
Small cell BS
Macrocell BS
X2
X2
Will small cells “destroy” large ones?
 Small cells cause low interference to macrocells
 On DL, UE associates with macrocell only if power higher
 On UL, channel gain from UE to small cell large, hence lower power
required  minimal interference.
 Standards-based ICIC protects large cells
 On DL, small cell avoids transmission on PRBs when macrocell
transmits at high power, typically to cell-edge users.
 Small cell causes low interference to other PRBs due to their low
power.
 On UL, small cell lowers transmit power cell when macrocell
indicates high interference via an HII message or an OI message.
Can trade off aggressive frequency reuse against interference
Algorithmic Challenge: Complexity
 Optimal channel allocation was NP hard in
2nd-generation (voice) IS-54 systems
 Now we have MIMO, multiple frequency
bands, hierarchical networks, …
 But convex optimization has advanced a lot
in the last 20 years
Innovation needed to tame the complexity
Small cells/SoN improve robustness
Macrocell BS Failure
Small Cell BS Failure
 SON algorithm detects failures in macro/pico/femto BSs
 Dynamically adjusts TX power and antenna tilt of to
cover “orphaned” mobiles
 Similar algorithm used to shut down BSs to save energy
Can 802.11 solve the spectrum
crunch? “The Good” & “The Bad”
 Ubiquitous
 Not “Carrier-Grade”
 Free [unlicensed] spectrum
 Poor and variable Quality-of-
 Standards based [sort of]
Experience
 No seamless handoffs
 Enterprise Grade very
expensive and not scalable for
massive deployments
 Established silicon
ecosystem
 Large ODM base
WiFi Networks Today
 WiFi provides high-speed connectivity in the
home, office, and in public hotspots.
 WiFi protocols based on the IEEE 802.11
family of standards: 802.11a/b/g/n
 Next-gen 802.11ac offers peak rate over 1 Gbps.
 Designed based on access points (APs) with
50ft range and for low-density deployments.
 Severe interference in dense deployments.
Problem Statement
 The WiFi standard lacks good mechanisms to mitigate
interference in dense AP deployments
 Static channel assignment, power levels, and carrier sensing
thresholds
 In such deployments WiFi systems exhibit poor spectrum reuse
and significant contention among APs and clients
 Result is low throughput and a poor user experience
Why not use SoN for WiFi?
SoN
Controller
- Channel Selection
- Power Control
- etc.
 SoN-for-WiFi: dynamic self-organization network
software to manage of WiFi APs.
 Allows for capacity/coverage/interference mitigation
tradeoffs.
 Also provides network analytics and planning.
SoN-for-WiFi Challenges
 Algorithm complexity
 Cloud-based interface to WiFi chipsets
 Lack of synchronization
 Lack of control over some APs and other
ISM-band devices
WiFi spectrum complements scarce
and bifurcated cellular spectrum
Current solution is device-driven Wi-Fi offload
•
•
User sessions are disrupted during Offload
Require software on clients (handset, tablets,…)
•
•
•
Requires supporting innumerable number of hardware &
software combinations
Quality-of-Service (QOS) is not guaranteed
Ad-hoc offload generally ad-hoc
Solution: Network-Initiated Offload:
Benefits of Network-Initiated HO
 Exploits all-IP backbone of LTE and WiFi
 Seamless offload between LTE and WiFi
 No client on handset needed
 Simultaneous access to LTE and WiFi network
services
 Intelligent handoff LTEWiFi and WiFiLTE
based on load and network conditions
 Preserve connections on both networks and
visibility into user activity
23
Summary
 Future wireless networks will divorce HW and SW, with
commodotized HW and cloud SW to manage it
 Cellular networks must support explosive data growth
through small cells and WiFi handoff
 WiFi must “grow up” to provide a better user
experience through centralized (SoN) control
 Seamless handoff between networks is required to
enable the wireless cloud demanded by users