CDMAIPbased System for Interoperable Public Safety Radio Communications
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
Problems in Public Safety Systems v 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. v Problems of limited spectrum and incompatibility: – Can not interoperate – Cannot support wideband data and video communications Ø Real-time access to mug-shots, finger-prints, crime-scene Ø Fire-fighting, crowd- and prison control – Cannot share data among agencies
Short-term Solutions v Use dispatching or switch center to manually relay signals betweens systems – Requirements Ø Interfaces to all potential systems Ø Coordination and involvement of all public safety agencies – Challenges Ø Scalability when allocating new frequency band Ø Proprietary nature of public safety system
Long-term Solutions v 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 v Use of more efficient radio technologies, especially for new frequency bands
CDMA/IP-based Wireless Systems v CDMA – Easy of deployment, higher capacity, improved quality, greater coverage, increased privacy and talk time v 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 Ø Multimedia, location tracking, encryption, VPN
Future Network Architecture Micro Base Station Cellular Base Station Wireless Gateways Performance IP&Services Radio Access Network (IP RAN) Multimedia & Messaging Server Internet Bluetooth - Radio Hub Wireless Local Area Networks Personal Area Networks(WLANs) (WPANs) - Location service Content
A Sharing and Connection Structure Internet PDSN/SGSN PAG (Packet Data Serving Node) RNC Public Switched Telephone Network (PSTN) (Packet Access Gateway) RNC (Radio Network Controller) BSm Area 1 IP Radio Access Network (IP RAN) BS i BS j Area 2 BS k
Benefit of IP RAN v More scalable, reliable, and cost effectiv – Instead of linking individual agency to switching center through private or leased lines v Enable packet-based transportation – New applications v Statistical aggregation – High bandwidth utilization, reduced cost – Support both wire-line and wireless
Requirements of Public Safety System v Round clock availability, secure and private communications v Quality of services (Qo. S) guarantee – Voice (low delay and jitter) – Data (high throughput) – Video (Qo. S and throughput) v Maximize resource usage under scarce spectrum v Efficient resource management while guaranteeing – Availability, emergency, Qo. S
Challenges: Air Interface v Support transmission quality – Control power and rate to achieve target Eb/Io Ø Power and rate allocation for circuit-based transmission (e. g. , multimedia) Ø Adapt rate of elastic data through scheduling Ø Admission control for guaranteeing quality of on-going transmissions v More efficient use of spectrum – Integrated support of various traffic Ø real-time circuit-based and elastic packet-based
Challenges: IP-based Backhaul v Traffic in RAN is different from general Internet – Significant amount of traffic is delay sensitive Ø Voice, radio frames involved in soft handoff v Majority of handoffs involve RAN – Interruptions during hard handoffs – Delayed handoffs and resource wastage during soft handoffs – Reservation needs to be quick v Radio frame may contain both data and control – Loss and delay of control impact transmission, and reduce air interface capacity
Proposed Work v Resource management for air interface v 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 v Multicasting support: group communications v Simulator design
Resource Management for Air Interface v 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 v Approaches: Cross-layer – Physical layer: power control, rate control – Link layer: scheduling – Network layer: admission control
Rate Control v Basic rate control methods: – Fixed channel continuous transmission Ø Vary processing gain Ø Assigning multiple codes – Time-slotted scheduling Ø Allocate different number of time slots Ø Allocate different number of codes v Supporting connectivity and availability – Reduce video resolution, reduce rate of elastic data v Different tradeoffs – Combating the reduction of Eb/Io: throttling the sourcecoding rate or increasing the transmission power – Allowing for increasing bit error for less critical data – Apply more efficient error-resilient coding algorithms
Power Control v Optimal power allocations: different types of traffic, different transmission formats v 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) Ø Reduce power for elastic data traffic Ø Allocate more time slots to delay sensitive packet scheduled data
Packet Scheduling v Support different Qo. S – Literature work only considers maximize total throughput, cannot meet public safety requirement v 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 v Adaptive scheduling – Increase data rate when system load is low
Admission Control v Adaptive admission control for integrated traffic – Consider both circuit and packet transmissions – Cannot guarantee quality by purely scheduling Ø Different power for different users Ø Varying power for the same user due to varying channel conditions and traffic rate v Prioritize handoffs – Consider both soft handoff and hard handoff v Study connection level performance
Backhaul Resource Management v Effective and scalable traffic engineering v Efficient handoffs
Scalable Traffic Engineering v Aggregate resource reservation and traffic multiplexing – Reservation at cell level instead of at mobile level Ø Minimize traffic dynamics Ø Reduce management overhead – Sink-tree based aggregation at upper link – Multicasting at downlink v Ensure fairness: different cells, different agencies, different users
Efficient Handoff Management v Handoff prediction and guard channel reservation – Dual time scale guard capacity control – More efficient than direct reservation – Prediction aggregation, fairness v Increase scalability – Blocked-based reservation v Packet rerouting and sequencing – Queuing at RNC or at base stations? v Load control and resource management at downlink – More effective diversity control to reduce error rate – Multicasting to speed up rerouting
Multicasting Support v Public safety agencies require: talk or share information within a group of users v Exploit the broadcast feature of downlink channels v Multicasting for circuit-based transmission v Multicasting for time-slotted packet-based transmission
Simulator Design v Build channel model v Simulate functions at air interface v Simulator functions in the backhaul v Simulated all the proposed functions, performance evaluations
Work Completed
Work Completed So far v Data Traffic Analysis v Preliminary simulator design
Traffic Analysis in CDMA Network v Internet data traffic exhibits long range dependency compared to voice traffic – Typical data users: heavy tailed ON/OFF users, average file size 20 KB (or 2. 5 seconds burst time with 64 Kbps) –Long Range Dependent (LRD) – Typical voice users: exponential ON/OFF users, average burst time 70 ms. v CDMA network performance needs to be evaluated and protocols need to be enhanced to accommodate data traffic.
LRD Impact in CDMA Networks v LRD Impact on – Multi-Access Interference (MAI) – Signal to Interference and Noise Ratio (SINR) – Outage Probability v Can be used for traffic prediction – Call Admission Control (CAC) – Rate Control
Multi-access Interference v MAI: – Xj is user’s activity indicator: when user j is transmitting (ON), Xj=1; – Pj is power per sampling time. – with perfect power control, – Ki(u) is the equivalent number of active users transmitting with rate
Statistics of MAI v Distribution of MAI – Instantaneous MAI I(u) is the sum of multiple independent random v – Time-scaled MAI IT(t) is defined as ST is the number of samples in T which remains as Gaussian v Long range dependency of MAI – Voice users: ON/OFF periods are exponentially distributed, then I( – Data users: ON/OFF periods are heavy tailed, then I(t) is LRD. – MAI has a Weibull bounded tail distribution:
Instantaneous SINR v Distribution – SINR has the distribution with impact combining N 0 and Ki v Long range dependency – Voice users Ø N 0 and Ki are both SRD, N 0 +Ki -> SRD and SINR -> SRD. – Data users Ø N 0 is SRD and Ki is LRD, N 0 +Ki -> LRD and SINR -> LRD
Time-scaled SINR v Time-scaled SINR: average over a time window v Noise N 0 T has a Gaussian distribution with variance v Ki. T also follows a Gaussian distribution – Voice users: variance decreases fast with T – Data users: variance decreases slow with T as H>0. 5 v SINR has a “Gaussian like”distribution which is the revers
Outage Probability v 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.
Prediction in CDMA Networks v Active users K prediction – Predict K in the next window Tm based on historical values – Fixed Period (FP) vs. Variable Period (VP) prediction v Prediction is useful for – Rate control: in a relatively small T – Call admission control: in a relatively large T FP vs VP
Fixed Period Prediction vs. Variable Period Prediction v Fixed Period Prediction: (existing, simple) – Predict the next value based on the average value in pervious m windows. Ø Only count a finite number of historical values Ø Historical values are added to prediction with the same weights. v Variable Period Prediction (more accurate) – Predict the next value based on all previously measured values with proper weights Ø All historical values are added to the prediction Ø Multi time-scale prediction Ø Historical values are properly weighted in the prediction Ø Recursive algorithm, consumes less memory
Rate Control v Adjust user’s sending rate based on active user K prediction v Suppose the system can support at most Km (equivalent) acti – If , increase each user’s rate with – If , decrease each user’s rate with
Call Admission Control v Admit new users based on prediction of network performa v CAC for voice users – Based on average performance – The users that the network can admit is at most is the activity indicator v CAC for data users – Based on number of active users predicted in the next p – If , then admit, otherwise reject.
User Throughput: Rate Control CAC
Conclusion for Traffic Analysis v Both MAI and SINR are LRD in a CDMA network with heavy tailed ON/OFF data users v Strong auto-correlation in MAI and SINR could be used for prediction in rate control and CAC v 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
Basic Simulator Design v Language: ANSI C++ v The network topology – Approximated as a square mesh. v 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. v Event queue and scheduling: tree-based – Need more efficient event scheduler
Simulator (cont’d) v Mobility model – Random Way Point v Power Measurement – Calculated based on mobile location v Channel Model – Fading, shadowing, path loss, interference v Network Model: – Mobile object, cell object – UMCast: major network functions with references Ø ALL mobile objects Ø ALL Cell objects Ø Stat class v Challenges: – How to run event generator and algorithm in parallel – Trade off scalability and event granularity
Basic Functions in Simulator v Call initiation v Call arrival v Call departure v Power measurement v Handoff prediction v Guard capacity management v Admission control v Performance statistics
On-going Work v 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? v Admission control for integrated circuit-based continuous media transmission and slotted-packetbased data – How to formulate resource consumption model? – How to interact with rate control and power control?
Future Work v The remaining of the proposal
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