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?