1. 1 Radio Resource Management Roy Yates WINLAB, Rutgers University Airlie House Workshop

2. 2 What is Radio Resource Mgmt? Assign channel, xmit power for each user –Cellular networks, packet radio networks Receiver Technology User Services How does it work? How well does it work?

3. 3 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

4. 4 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

5. 5 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

6. 6 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

7. 7 CDMA System Model SIR 1 SIR i SIR N

8. 8 CDMA Signals Interference suppression: Choose c i to max SIR Power Control: Choose p i for SIR = Γ

9. 9 SIR Constraints Feasibility depends on link gains, receiver filters

10. 10 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

11. 11 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

12. 12 CDMA and Antenna Arrays s i =CDMA signature Antenna signature c i = Receiver filter Antenna weights CDMA Interference Suppression –in signal space –e.g. [Lupas, Verdu, 89] Antenna beamforming –in real space –[Winters, Salz, Gitlin 94]

13. 13 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

14. 14 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

15. 15 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 rate service, cellular price Complex solutions

16. 16 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+]

17. 17 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

18. 18 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

19. 19 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

20. 20 MMSE Signature Optimization c i MMSE receiver filter Interference s i transmit signal Iterative Algorithm: Match s i to c i Convergence?

21. 21 Optimal Signatures N users, proc gain G, N>G Signature set: S =[s 1 | s 2 | … |s N ] Optimal Signatures? –IT Sum capacity: [Rupf, Massey] –User Capacity [Viswanath, Anantharam, Tse] WBE sequences: SS t =(N/L)I are optimal –Property: MMSE filter =matched filter

22. 22 MMSE Signature Optimization RX i converges to MMSE filter c i TX i matches RX: s i = c i –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

23. 23 Unresolved Questions Multicell systems: –Capacity? Old Problem: Interference Channel –MMSE Effectiveness? Dimensionality of antenna arrays? Systems in unlicensed bands? Architectures for Data Networks?