Andrea Goldsmith Stanford University Accelera, Inc. IEEE SCV ComSoc meeting May 8, 2013
Andrea Goldsmith
Stanford University
Accelera, Inc.
IEEE SCV ComSoc meeting May 8, 2013
Wireless networks are everywhere, yet…
- Connectivity is fragmented
- Capacity is limited (spectrum crunch
and interference)
- Roaming between networks is ad hoc
TV White Space &
Cognitive Radio
Software Defined Wireless Networking (SDWN): Accelera’s Architecture
WiFi AP Small Cell Dual Mode
Wifi/LTE
ACCELERA “MAESTRO” UNIFIED CONTROL PLANE
Wireless HW
Cloud-Based Control, Management, and Radio Resource Management Plane
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?
Time-varying channels with memory/feedback.
We don’t know the Shannon capacity of most wireless channels
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
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.
Small Cell
Relay
DAS
Coop MIMO
How should cellular systems be designed?
Will gains in practice be big or incremental; in capacity or coverage?
Are small cells the solution to increase cellular system capacity?
Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999)
A=.25D2p
Area Spectral Efficiency
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 MACRO: solving initial coverage issue, existing network
FEMTO: solving enterprise & home coverage/capacity issue
PICO: solving street, enterprise & home coverage/capacity issue
10x Lower HW COST
10x CAPACITY Improvement
Near 100% COVERAGE
Macrocell Picocell Femtocell
Today’s architecture • 3M Macrocells serving 5 billion users
Managing interference between cells is hard
Traditional Macro vs. SON Enabled H-RAN
Macro BS Only H-RAN: Macro + Pico BS
H-RAN advantage
10x CAPACITY
10x lower $/Mbps
~100%
COVERAGE
Chicago Downtown Modeling Assumptions:
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
Macro BS Macro + Pico optimized Users trying to connect 66680 66680 Connected users 31023 50902 Effective MAC Aggregate Throughput (DL) 1020 Mbps 12 060 Mbps Effective MAC Aggregate Throughput (UL) 389 Mbps 4 204 Mbps
12x
Macro BS - Cost Per Mbps $1,341/Mbps
Pico BS - Cost Per Mbps $111/Mbps
CapEx Reduction Factor (no backhaul/site acq)
Deployment Challenges
Deploying One Macrocell Effort
(MD – Man Day)
New site verification 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 self-
configuration via software
Basic premise of self-
organizing networks (SoN)
SON for LTE small cells
Node
Installation
Initial
Measurements
Self
Optimization
Self
Healing
Self
Configuration Measurement
SON
Server
SoN
Server
Macrocell BS
Mobile Gateway Or Cloud
Small cell BS
X2
X2 X2
X2
IP Network
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.
Will small cells “destroy” large ones?
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
Stage 3 Use genetic search to find further improvements by mutating some “genes”
Innovation needed to tame the complexity
Small cells/SoN improve robustness
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
Macrocell BS Failure Small Cell BS Failure
Can 802.11 solve the spectrum
crunch? “The Good” & “The Bad”
Ubiquitous
Free [unlicensed] spectrum
Standards based [sort of]
Established silicon
ecosystem
Large ODM base
Not “Carrier-Grade”
Poor and variable Quality-of-
Experience
No seamless handoffs
Enterprise Grade very
expensive and not scalable for
massive deployments
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.
WiFi Networks Today
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
Problem Statement
Why not use SoN for WiFi?
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 Controller
- Channel Selection - Power Control - etc.
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 LTEWiFi and WiFiLTE
based on load and network conditions Preserve connections on both networks and
visibility into user activity
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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