CDMA/IP-based System for Interoperable Public Safety Radio Communications Xin Wang Director:...
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Transcript of CDMA/IP-based System for Interoperable Public Safety Radio Communications Xin Wang Director:...
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 Universitywww.ece.sunysb.edu/~xwang
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
Short-term Solutions
Use dispatching or switch center to manually relay signals betweens systems– Requirements
Interfaces to all potential systemsCoordination and involvement of all public safety
agencies
– ChallengesScalability when allocating new frequency bandProprietary nature of public safety system
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
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
Future Network Architecture
-Wireless Local Area Networks(WLANs)
-
Bluetooth
Wireless Personal Area Networks(WPANs)
Cellular
Micro Base Station Wireless
Gateways
Base Station
Multimedia & MessagingServer
Content
Locationservice
IP RadioAccessNetwork(IP RAN)
Performance &Services
Radio Hub
Internet
A Sharing and Connection Structure
BS i
BSm
RNC
(PSTN)
BS j
Internet
IP RadioAccessNetwork(IP RAN)
PAG
Area 1 Area 2
IP RadioAccessNetwork(IP RAN)
RNC
PDSN/SGSNPDSN/SGSN
RNC
Public Switched Telephone Network
(Radio Network Controller)
(Packet Access Gateway)
BS k
(Packet Data Serving Node)
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
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
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
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
Proposed Work
Resource management for air interface Scalable backhaul management
Many interactions:
Resource allocation across multiple network layersEffect of air interface management and user mobility on RNAEffect of resource management in RAN on the air interface
Multicasting support: group communications Simulator design
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
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
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
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
Admission Control
Adaptive admission control for integrated traffic– Consider both circuit and packet transmissions– Cannot guarantee quality by purely scheduling
Different power for different usersVarying 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
Backhaul Resource Management
Effective and scalable traffic engineering
Efficient handoffs
Scalable Traffic Engineering
Aggregate resource reservation and traffic multiplexing– Reservation at cell level instead of at mobile
level Minimize traffic dynamicsReduce management overhead
– Sink-tree based aggregation at upper link– Multicasting at downlink
Ensure fairness: different cells, different agencies, different users
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
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
Simulator Design
Build channel modelSimulate functions at air interfaceSimulator functions in the backhaulSimulated all the proposed functions,
performance evaluations
Work Completed
Work Completed So far
Data Traffic Analysis Preliminary simulator design
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.
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
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
j
N
ijjji PuXuI
,1
)()(
)()()(,1
uKPR
RuXPuI ii
N
ijj i
jjii
jjii RPRP //
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 T which 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:
T
T
vS
SvuT
Ti uI
SvI
1)1()(
1)(
ST
Instantaneous SINR
Instantaneous SINR
Distribution– SINR has the distribution with impact combining N0
and Ki
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
)(/)()(
0 uKPWuN
GuSINR
ii
ii
Time-scaled SINR
Time-scaled SINR: average over a time window
Noise N0T has a Gaussian distribution with
variance 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
SINR has a “Gaussian like”distribution which is the reverse of WN0
T/Pi +KiT (Gaussian distribution)
)(/)()(
0 vKPWvN
GvSINR
Tii
TiT
i
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.
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 TFP vs VP
Fixed Period Predictionvs. 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 valuesHistorical 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 weightsAll historical values are added to the predictionMulti time-scale predictionHistorical values are properly weighted in the
predictionRecursive algorithm, consumes less memory
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
um rKiK )1(ˆ
jum RiK
iKrK
)(
)1(ˆ
lm rKiK )1(ˆ
jlm R
iK
rKiK
)(
)1(ˆ
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.
)1(ˆ nK
0)1(ˆ KnK
)(][
11)1(
][
11 0
000 P
WN
SINR
G
XEK
XEM
X
User Throughput
Throughput:
Rate Control CAC
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
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
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
Basic Functions in Simulator
Call initiationCall arrivalCall departurePower measurementHandoff predictionGuard capacity managementAdmission controlPerformance statistics
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?
Future Work
The remaining of the proposal