1 Radio Resource Management Roy Yates WINLAB, Rutgers University Airlie House Workshop.
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Transcript of 1 Radio Resource Management Roy Yates WINLAB, Rutgers University Airlie House Workshop.
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Radio Resource Management
Roy Yates
WINLAB, Rutgers University
Airlie House Workshop
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What is Radio Resource Mgmt?
• Assign channel, xmit power for each user– Cellular networks, packet radio networks
Receiver TechnologyUser Services
How does it work?How well does it work?
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Fixed Channel Allocation (FCA)
• Assign orthogonal channels to cells– to meet coarse interference constraints
• e.g. adjacent cells cannot use same channel
– Allocation depends on offered traffic/cell• offline measurements
– graph coloring • OR - not radio
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FCA Problems
• Traffic in each cell?
• Coarse interference constraints– Interference depends on detailed propagation
• Microcells require too many measurements
• Better heuristics offer small performance benefits
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Dynamic Channel Allocation
• Queueing network models– No measurements, partial state information
• max packing, borrowing– [Everitt 89] [Cimini, Foschini, I, Miljanic, 94]
– Measurements: • Least Interference, Maxmin SIR?
• Common Wisdom:– DCA for light loads, FCA for high loads
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Impact of Qualcomm IS-95
• 1 channel: no frequency planning
• CDMA research became practical– Existence proof that power control could work– Any interference suppression helps
• Multiuser Detection
• Emphasis on signal measurements
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CDMA System Model
Nc
1c
ic11 sp
iip s
kkp s
SIR1
SIRi
SIRN
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CDMA Signals
ijj
tijj
itiii
i
ti
ijj
tijjji
tiiiii
jjjjj
ph
phSIR
bphbphy
bph
22
2
noise
ceInterferenSignal Desired
sc
sc
ncscsc
nsr
• Interference suppression: Choose ci to max SIR
• Power Control: Choose pi for SIR = Γ
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22 :sconstraint SIR
ijjj
tij
iti
ii psch
scp
1 iff Feasible G
Gpp :formVector
SIR Constraints
• Feasibility depends on link gains, receiver filters
)( :General In pIp
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Simple Power Control
• Algorithm: – Each user uses minimum transmit power to
meet SIR objective
• Monotonicity: – Lowering your transmit power creates less
interference for others
• Consequence: Powers converge to a global minimum power solution
))(()1( tItpjj
p
)'()(' pIpIpp
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Adaptive Power Control
• SIR Balancing– [Aein 73, Nettleton 83, Zander 92, Foschini&Miljanic 93]
• Integrated BS Assignment – [Hanly 95, Yates 95]
• Macrodiversity– [Hanly 94]
• Link Protection/Admission Control– [Bambos, Pottie 94], [Andersin, Rosberg, Zander 95]
• Note: Adaptive PC analysis is deterministic
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CDMA and Antenna Arrays
• si =CDMA signature Antenna signature
• ci = Receiver filter Antenna weights
• CDMA Interference Suppression– in signal space
– e.g. [Lupas, Verdu, 89]
• Antenna beamforming– in real space
– [Winters, Salz, Gitlin 94]
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Linear Filtering with Power Control
• 2 step Algorithm:– [Rashid-Farrokhi, Tassiulas, Liu], [Ulukus, Yates]
– Adapt receiver filter to maximize SIR• Given powers, use MMSE filter [Madhow, Honig 94]
– Given receiver, use min transmit power to meet SIR target
• Converges to global minimum power solution
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Wireless Voice vs Wireless Data
• Voice– Delay sensitive
• msec OK
– Maximum rate
– Minimize the probability of outage
• Data– Delay insensitive
• sec OK? hours OK?
– No Maximum Rate
– Maximize the time average data rate
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Wireless Data
• Current Data Standards– Cellular modem, CDPD (AMPS)– IS-99/IS-707 (for IS-95)– GPRS (for GSM)
• Proposed Solutions:– EDGE, space time codes– 3G WCDMA
Low rateservice,cellular price
Complexsolutions
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Optimizing Data Services
• Channel Quality (link gain) is stochastic– Rayleigh and shadow Fading, – Distance propagation
• Use more power when the channel is good
• Reduce power when the channel is bad– Water filling in time
• [Goldsmith 94+]
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Optimizing Wireless Data Networks
• Anytime/Anywhere is a design choice– good for voice networks– reduces system capacity
• users near cell borders create lots of interference
• Infostations: Low cost pockets of high rate service
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Unlicensed Bands
• FCC allocated 3 bands (each 100 MHz) around 5 GHz
• Minimal power/bandwidth requirements
• No required etiquette
• How can or should it be used?– Dominant uses?
• Non-cooperative system interference
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Interference Avoidance
• Old Assumption: Signatures of users never change• New Approach: Adapt signatures to improve SIR
– Receiver feedback tells transmitter how to adapt.
• Application: – Fixed Wireless
– Unlicensed Bands
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MMSE Signature Optimization
ci MMSE receiver filter
Interference
si transmit signal
Iterative Algorithm: Match si to ci
Convergence?
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Optimal Signatures
• N users, proc gain G, N>G
• Signature set: S =[s1 | s2 | … |sN]
• Optimal Signatures?– IT Sum capacity: [Rupf, Massey]
– User Capacity [Viswanath, Anantharam, Tse]
• WBE sequences: SSt =(N/L)I are optimal– Property: MMSE filter =matched filter
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MMSE Signature Optimization
• RX i converges to MMSE filter ci
• TX i matches RX: si = ci
– Some users see more interference, others less
– Other users iterate in response
• Preliminary Result:– Users at 1 BS converge to optimal WBE signatures
• Interference Avoidance– Generalizations to arbitrary systems
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Unresolved Questions
• Multicell systems:– Capacity?
• Old Problem: Interference Channel
– MMSE Effectiveness?
• Dimensionality of antenna arrays?
• Systems in unlicensed bands?
• Architectures for Data Networks?