CDMA/IP-based System for Interoperable Public Safety Radio Communications

1. CDMA/IP-based System for Interoperable Public Safety Radio Communications Xin Wang Director: Wireless Networking and Systems Lab (WINS) Department of Electrical and Computer Engineering Stony Brook University www.ece.sunysb.edu/~xwang

2. Problems in Public Safety Systems • Two main factors limiting the reliability and availability of public safety systems: • Different agencies use incompatible systems (different frequencies, different modulation or encoding, etc). • Spectrum is limited and fragmented. • Problems of limited spectrum and incompatibility: • Can not interoperate • Cannot support wideband data and video communications • Real-time access to mug-shots, finger-prints, crime-scene • Fire-fighting, crowd- and prison control • Cannot share data among agencies

3. Short-term Solutions • Use dispatching or switch center to manually relay signals betweens systems • Requirements • Interfaces to all potential systems • Coordination and involvement of all public safety agencies • Challenges • Scalability when allocating new frequency band • Proprietary nature of public safety system

4. Long-term Solutions • Develop modular and scalable systems • Individual agencies can acquire and expand their own wireless systems without compromising compatibility • Cost offset: sharing the radio infrastructure from various agencies in a region • Use of more efficient radio technologies, especially for new frequency bands

5. CDMA/IP-based Wireless Systems • CDMA • Easy of deployment, higher capacity, improved quality, greater coverage, increased privacy and talk time • IP interface between different systems • Allowing the interoperability of different bands • Sharing the networks independent of access techniques • Easy of supporting new radio bands and new IP-based technologies while supporting existing systems • Deployment of off-the-shelf and third-party products • Multimedia, location tracking, encryption, VPN

6. IP Radio Access Network (IP RAN) Internet Performance &Services Multimedia & Messaging Server Content Location service Future Network Architecture Micro Base Station Wireless Gateways Cellular Base Station Bluetooth - Radio Hub - Wireless Local Area Networks (WLANs) Wireless Personal Area Networks (WPANs)

7. IP Radio Access Network (IP RAN) IP Radio Access Network (IP RAN) Internet A Sharing and Connection Structure Public Switched Telephone Network PDSN/SGSN PDSN/SGSN PAG (PSTN) (Packet Data Serving Node) (Packet Access Gateway) RNC RNC RNC (Radio Network Controller) BSm BS i Area 2 Area 1 BS j BS k

8. Benefit of IP RAN • More scalable, reliable, and cost effectiv • Instead of linking individual agency to switching center through private or leased lines • Enable packet-based transportation • New applications • Statistical aggregation • High bandwidth utilization, reduced cost • Support both wire-line and wireless

9. Requirements of Public Safety System • Round clock availability, secure and private communications • Quality of services (QoS) guarantee • Voice (low delay and jitter) • Data (high throughput) • Video (QoS and throughput) • Maximize resource usage under scarce spectrum • Efficient resource management while guaranteeing • Availability, emergency, QoS

10. Challenges: Air Interface • Support transmission quality • Control power and rate to achieve target Eb/Io • Power and rate allocation for circuit-based transmission (e.g., multimedia) • Adapt rate of elastic data through scheduling • Admission control for guaranteeing quality of on-going transmissions • More efficient use of spectrum • Integrated support of various traffic • real-time circuit-based and elastic packet-based

11. Challenges: IP-based Backhaul • Traffic in RAN is different from general Internet • Significant amount of traffic is delay sensitive • Voice, radio frames involved in soft handoff • Majority of handoffs involve RAN • Interruptions during hard handoffs • Delayed handoffs and resource wastage during soft handoffs • Reservation needs to be quick • Radio frame may contain both data and control • Loss and delay of control impact transmission, and reduce air interface capacity

12. Proposed Work • Resource management for air interface • Scalable backhaul management Many interactions: Resource allocation across multiple network layers Effect of air interface management and user mobility on RNA Effect of resource management in RAN on the air interface • Multicasting support: group communications • Simulator design

13. Resource Management for Air Interface • Goal • Serve both circuit-based delay sensitive applications and packet-based high speed data application • Support both user-to-user unicast and one-to-many multicast for group communications • Approaches: Cross-layer • Physical layer: power control, rate control • Link layer: scheduling • Network layer: admission control

14. Rate Control • Basic rate control methods: • Fixed channel continuous transmission • Vary processing gain • Assigning multiple codes • Time-slotted scheduling • Allocate different number of time slots • Allocate different number of codes • Supporting connectivity and availability • Reduce video resolution, reduce rate of elastic data • Different tradeoffs • Combating the reduction of Eb/Io: throttling the source-coding rate or increasing the transmission power • Allowing for increasing bit error for less critical data • Apply more efficient error-resilient coding algorithms

15. Power Control • Optimal power allocations: different types of traffic, different transmission formats • Power sharing among real-time and non real-time traffic • Fixed rate transmission: iterative power control to find the minimum power to guarantee the received quality • Increased power for real-time traffic (increased load, or bad channel) • Reduce power for elastic data traffic • Allocate more time slots to delay sensitive packet scheduled data

16. Packet Scheduling • Support different QoS • Literature work only considers maximize total throughput, cannot meet public safety requirement • Study tradeoffs between time-slotted scheduling and fixed-channel continuous transmissions. Feature of scheduling: • Pros: More efficient resource usage and overall higher throughput, throughput gains from multi-user diversity • Cons: complex in guaranteeing quality • Adaptive scheduling • Increase data rate when system load is low

17. Admission Control • Adaptive admission control for integrated traffic • Consider both circuit and packet transmissions • Cannot guarantee quality by purely scheduling • Different power for different users • Varying power for the same user due to varying channel conditions and traffic rate • Prioritize handoffs • Consider both soft handoff and hard handoff • Study connection level performance

18. Backhaul Resource Management • Effective and scalable traffic engineering • Efficient handoffs

19. Scalable Traffic Engineering • Aggregate resource reservation and traffic multiplexing • Reservation at cell level instead of at mobile level • Minimize traffic dynamics • Reduce management overhead • Sink-tree based aggregation at upper link • Multicasting at downlink • Ensure fairness: different cells, different agencies, different users

20. Efficient Handoff Management • Handoff prediction and guard channel reservation • Dual time scale guard capacity control • More efficient than direct reservation • Prediction aggregation, fairness • Increase scalability • Blocked-based reservation • Packet rerouting and sequencing • Queuing at RNC or at base stations? • Load control and resource management at downlink • More effective diversity control to reduce error rate • Multicasting to speed up rerouting

21. Multicasting Support • Public safety agencies require: talk or share information within a group of users • Exploit the broadcast feature of downlink channels • Multicasting for circuit-based transmission • Multicasting for time-slotted packet-based transmission

22. Simulator Design • Build channel model • Simulate functions at air interface • Simulator functions in the backhaul • Simulated all the proposed functions, performance evaluations

23. Work Completed

24. Work Completed So far • Data Traffic Analysis • Preliminary simulator design

25. Traffic Analysis in CDMA Network • Internet data traffic exhibits long range dependency compared to voice traffic • Typical data users: heavy tailed ON/OFF users, average file size 20KB (or 2.5seconds burst time with 64Kbps) –Long Range Dependent (LRD) • Typical voice users: exponential ON/OFF users, average burst time 70ms. • CDMA network performance needs to be evaluated and protocols need to be enhanced to accommodate data traffic.

26. LRD Impact in CDMA Networks • LRD Impact on • Multi-Access Interference (MAI) • Signal to Interference and Noise Ratio (SINR) • Outage Probability • Can be used for traffic prediction • Call Admission Control (CAC) • Rate Control

27. Multi-access Interference • MAI: • Xj is user’s activity indicator: when user j is transmitting (ON), Xj=1; when user is silent (OFF), Xj=0. • Pj is power per sampling time. • with perfect power control, • Ki(u) is the equivalent number of active users transmitting with rate Ri

28. Statistics of MAI • Distribution of MAI • Instantaneous MAI I(u) is the sum of multiple independent random variables and approximates Gaussian distribution with variance • Time-scaled MAI IT(t) is defined as is the number of samples in Twhich remains as Gaussian • Long range dependency of MAI • Voice users: ON/OFF periods are exponentially distributed, then I(u) is SRD. • Data users: ON/OFF periods are heavy tailed, then I(t) is LRD. • MAI has a Weibull bounded tail distribution: ST

29. Instantaneous SINR • Instantaneous SINR • Distribution • SINR has the distribution with impact combining N0andKi • Long range dependency • Voice users • N0 and Ki are both SRD, N0+Ki -> SRD and SINR -> SRD. • Data users • N0 is SRD and Ki is LRD, N0+Ki -> LRD and SINR -> LRD

30. Time-scaled SINR • Time-scaled SINR: average over a time window • Noise N0T has a Gaussian distribution with variance • KiT also follows a Gaussian distribution • Voice users: variance decreases fast with T • Data users: variance decreases slow with T as H>0.5 • SINR has a “Gaussian like”distribution which is the reverse of WN0T/Pi +KiT (Gaussian distribution)

31. Outage Probability • Outage probability • The probability that the average SINR or time scaled SINR in a packet transmitting time is smaller than a threshold  degraded quality • Also decay slow.

32. Prediction in CDMA Networks • Active users K prediction • Predict K in the next window Tm based on historical values • Fixed Period (FP) vs. Variable Period (VP) prediction • Prediction is useful for • Rate control: in a relatively small T • Call admission control: in a relatively large T FP vs VP

33. Fixed Period Prediction vs. Variable Period Prediction • Fixed Period Prediction: (existing, simple) • Predict the next value based on the average value in pervious m windows. • Only count a finite number of historical values • Historical values are added to prediction with the same weights. • Variable Period Prediction (more accurate) • Predict the next value based on all previously measured values with proper weights • All historical values are added to the prediction • Multi time-scale prediction • Historical values are properly weighted in the prediction • Recursive algorithm, consumes less memory

34. Rate Control • Adjust user’s sending rate based on active user K prediction in a relatively smaller window T (2-10sec.) • Suppose the system can support at most Km (equivalent) active users (transmitting at maximum rate Rm), adjust user’s sending rate according to prediction: • If , increase each user’s rate with • If , decrease each user’s rate with

35. Call Admission Control • Admit new users based on prediction of network performance in a relatively large T (e.g., 5min). • CAC for voice users • Based on average performance • The users that the network can admit is at most is the activity indicator • CAC for data users • Based on number of active users predicted in the next period • If , then admit, otherwise reject.

36. User Throughput Throughput: Rate Control CAC

37. Conclusion for Traffic Analysis • Both MAI and SINR are LRD in a CDMA network with heavy tailed ON/OFF data users • Strong auto-correlation in MAI and SINR could be used for prediction in rate control and CAC • Variable period prediction scheme is proposed and proved to be better than the existing fixed period prediction in terms of • More accurate • Consumes less memory • Achieves better performance in rate control and CAC

38. Basic Simulator Design • Language: ANSI C++ • The network topology • Approximated as a square mesh. • Event Generator (Most important is handoff event) • Call arrival and departure are generated used Poisson distribution • Handoff events are triggered on the basis of power measurements. • Event queue and scheduling: tree-based • Need more efficient event scheduler

39. Simulator (cont’d) • Mobility model • Random Way Point • Power Measurement • Calculated based on mobile location • Channel Model • Fading, shadowing, path loss, interference • Network Model: • Mobile object, cell object • UMCast: major network functions with references • ALL mobile objects • ALL Cell objects • Stat class • Challenges: • How to run event generator and algorithm in parallel • Trade off scalability and event granularity

40. Basic Functions in Simulator • Call initiation • Call arrival • Call departure • Power measurement • Handoff prediction • Guard capacity management • Admission control • Performance statistics

41. On-going Work • Multicasting support for downlink circuit based transmissions (support of multimedia such as voice and video for group communications) • How to address heterogeneous requirements of users • How to transmit to different terminals? • How to guarantee quality for users with different channel conditions? • How to guarantee multicast traffic quality? • How to guarantee un-interrupted communications for each talk group? • How to tradeoff multicast and unicast transmissions? • Admission control for integrated circuit-based continuous media transmission and slotted-packet-based data • How to formulate resource consumption model? • How to interact with rate control and power control?

42. Future Work • The remaining of the proposal