Performance Enhancement of
Combining QoS
Provisioning and Location
Management in
Wireless Cellular Networks
-Fei Yu, Vincent W. S. Wong, Victor C. M.
Leung
Presented by
Kiran Kumar Bankupally
What is this paper about?
• Framework for Integrating QOS provisioning
and Location Management
• Proposing a new Connection Access Control
(CAC) using the integrated Scheme.
Key Words
• MS:- mobile station
• CAC:- Connection Admission Control
• LM:- Location Management
Introduction
• Tracking user is an issue.(Location
Management)
• Requirement of diverse QOS requirements
• Due to mobility availability of resources at
connection setup doesn’t guarantee the
resource availability.
• Performance degradation due to mobile hand
offs.
Location Management
• Divided into two parts
– Paging
– Location Updating
• QOS problems
– Deal with handoffs
• Forced connection terminations due to handoff
blocking are generally more objectionable compared to
new connection blocking
– Maximize utilization to reduce handoffs of new
connections
Key motivations
• User mobility is the main problem for both LM and why
CAC is required, but all the previous works deal with
them on different sets of information.
• per-user mobility pattern can provide the basis for
effective solutions that address these two sets of
system requirements, it will be helpful to consider
them jointly and make them share information with
each other.
• both in-session and out-of-session movements are
parts of a user’s mobility pattern and using all available
mobility information will improve the performance of
both CAC and LM schemes.
Proposed Framework
Contribution
• Contribution of this work is three fold
• more efficient and cost effective solutions
because of the integration
• New path based LM scheme which uses all the
available mobility information in both location
update and paging processes
• Novel CAC scheme is proposed. Predict not only
where the MS will move, but also when the MS
will move to a new cell based on the mobility
prediction.
COMMON MOBILITY PREDICTION
SCHEME
• purpose??
– For Location management
• Minimizing paging cost
– For QOS provisioning
• Better CAC Design
• Rationale??
– Most users have a favorite path which they repeat
most of the times.
– Shows the stationarity of sequence symbols .
• Motivation??
– Optimal Data Compression methods
Model Assumptions
• Network Topology
– Previous work used hexagonal of Square lattices
for the cell arrangement which is improper
because of antenna radiation pattern and
propagation environment.
– Graph model is used here. network is modeled as
connected Graph G = <V,E>
V = set of base stations each representing a single
cell
E = adjacency between cells.
Example topology
Model Assumptions(Cont..)
• Channel Holding Time and Cell Residence
Time
– Definitions
• Channel Holding Time:- time during which a connection
occupies a channel in a given cell
• Cell Residence Time:- amount of time that the mobile
user stays in that cell
– Previous models assume them to be exponentially
distributed, independently and identically
distributed for all cells. Here they are assumed to
follow General distributions
Model Assumptions(Cont..)
• User Mobility Model
– Symmetric random walk model was used
previously which ignores the favorite path
concept. It assumed equal probability to all
neighboring cells
– Here, MSs future locations are predicted by
correlating with its movement history
Prediction overview
– Let In a cellular network, the mobility of a user
can be represented by a sequence of
symbols,C1,C2,C3.. , where Ci denotes the identity
of the cell visited by the MS
– the sequence of symbols is assumed to be
generated by an mth order Markov source, where
the states correspond to the contexts of the
previous symbols
– Probability depends on the current cell or a list
recently visited cells.
The Optimal Data Compression
Algorithm
• a dictionary-based compression algorithm that
performs incremental parsing of an input
sequence which is optimal theoritically and good
practically
• This algorithm parses input string x1, x2, ..xi into
c(i) substrings w1, w2, ..wc(i) such that for all j>0,
the prefix of the substring wj is equal to some wi
for 1<i<j
• Uses a Trie that feeds the probability information
to arithematic encoder which encodes a
sequence of probability of p using –log2(p)
Example
• The Alphabets are {a,b,c}
• Input string
– ababcabcababcabc….
– (a)(b)(ab)(c),(abc),(aba),(bc),(abc..)…
– For this example the encoder since we are
analyzing 7 values the arithematic encoder
encodes the sequence with log27 b
Trie Construction
Root
a,1
Step:-(a)babcabcababcabc….
Trie Construction
Root
a,2
b,1
Step:-(a) (b)abcabcababcabc….
Trie Construction
Root
a,2
b,1
b,1
Step:-(a) (b)(ab)cabcababcabc….
Trie Construction
Root
a,2
b,1
b,1
Step:-(a) (b)(ab)(c)abcababcabc….
c,1
Trie Construction
Root
a,3
b,1
b,2
c,1
Step:-(a) (b)(ab)(c)(abc)ababcabc….
c,1
Trie Construction
Root
a,4
b,1
b,3
c,1
a,1
Step:-(a) (b)(ab)(c)(abc)(aba)bcabc….
c,1
Trie Construction
Root
c,1
a,4
b,2
b,3
c,1
a,1
Step:-(a) (b)(ab)(c)(abc)(aba)(bc)abc….
c,1
Mobility Prediction Scheme
• Is similar to prediction by partial
matching(PPM) data compression algorithm
• PPM algorithm
– Basis of PPM of order m is a set of m+1 Markov
predictors.
– A Markov predictor of order j predicts the next
event based on the j immediately preceding
events
– A trie is used to store all m contexts called
mobility trie
Pseudo Code for Mobility Prediction
Scheme
Example (2)
• Input sequence:-ababcabcababcab…
– The Trie(This is not Le-Zi but Active Le-Zi)
Example (Cont..)
• Scenario:- last three cells visited is abc.
– Want to predict next cell th MS will visit.
• Method:– First estimate the probability distributions for 0,1
and 2.
P2a=1
P1a=1
P0a=5/13
P2b=0
P1b=0
P2b=5/13
P2c=0
P1c=0
P2c=3/13
Example (Cont..)
– Blending vectors {w0,w1,w2} where
– The weights can be fixed or adapt as prediction
proceeds to give more emphasis to higher models.
– Then the probabilites assignment is given by
Implementation issues
• Deciding a data structure to store a trie is
important.
– have a pointer structure similar to trie structure
– a linked list implementation
– Hashing can also be used.
• Also to reduce memory and computation
complexity size of data structure is limited.
– Explicit bound to M.
– LRU strategy
Pointer Structure
Linked List structure
LM in combined Fraework
• A path based approach is used with slight
changes.
– All available location information is used in
prediction.
– In original one location during the connection is
treated same as a normal one.
– Here during session the Location update is
done.And when out of session then wait for new
pattern.If CMR is high you always have the track
– In session LU doesn’t need much resources.
Example
Numerical Results
• Simulation Environment Features
– Number of base stations 40
– Average number of neighbors 6
– Each mobile user has 5 different paths( since
every one has a favorite path) with probabilities
0.6,0.2,0.1,0.05,0.05.
• Paths are generated by first selections two nodes at
random as origin and destination nodes and whenever
the mobile user leraves a cell it moves to a
neighbouring cell which is closest to destination
Numerical Results(Cont…)
• Cell residence Time follows i.i.d Gamma
distribution with avg tim 1/μr asds
• New connection arrival time λ per minute.
• Connection durations are exponentially
distributed with mean 1/μd which is 3 min.
• ρ = λ/μr CMR
• Location Update is done using movement based
scheme.here after every movement of 1 cell a
update is done for simplicity.
Numerical Results(Cont…)
• For comparison purposes update(original)
represents number of update messages in
original scheme and similarly for update(new)
• Same goes with paging(original) and paging
(new)
• Performance gain in updates
– PG = update(original)/ update(new)
• Performance gain in paging
– PG = paging (original)/paging (new)
Update Gain
Paging Gain
CAC in new framework
• Terms used
– CAC:- Connection Admission Control
– Phd :- probability if handoff connection being
dropped
– Pnb :- probability new Connection is dropped
– E-OTD:- Enhanced Observed Time Difference
technology
– BTS:- Base transiever Stations
– MS :- Mobile Station
CAC in new framework
• Due to in session user mobility CAC needs to
perform mobility related QOS provisioning in
cellular networks.
• Key idea is to predict next node MS visits and
try to acquire the resource before hand
considering time it takes to reach that node.
• If resource is available, it is reserved for MS to
guarantee Phd
• E-OTD technology is used in this scheme.
E-OTD and Time Interval Prediction
• Time Interval Prediction is done using this.
• Unknown MS position p =(x,y) is estimated by
using the Time Difference Of Arrival(TDOA)
measurements between the MS and known
Coordinates,BTSsof known coordinates.
• TDOA Property
– TDOA between BTS1(serving BTS) and BTSi(i=2..Nneighbouring BTS)defines a hyperbola whose focii
coincide with coordinates of two BTSs.
– Two Hyprebolas are minimum to estimate MS
position.
E-OTD and Time Interval
Prediction(Cont…)
• TDOA is defined as Geometric Time Difference
(GTD)
where
tRxi and tTxi are, respectively, the reception and
transmission epochs of the burst from the ith
BTS.
E-OTD and Time Interval
Prediction(Cont…)
• Further simplifying
where OTD = Observed Time Difference
RTD = Real Time Difference
In absence of errors the position can be accurately
measured by
E-OTD and Time Interval
Prediction(Cont…)
• Because of noise Eq1 doesn’t hold good.
• linear regress setup can be used to smooth
the data for more accurate velocity and
position estimation of an MS
• K- previous estimations are used to obtain the
MSs current estimated velocity and position
• represents estimated locations at subsequent
time points tn.
E-OTD and Time Interval
Prediction(Cont…)
• Velovity is given by
E-OTD and Time Interval
Prediction(Cont…)
E-OTD and Time Interval
Prediction(Cont…)
• Let ta(i,j) denote the time when MS in cell i
will arrive at cell j, and td(i,j)denote the time
when the MS in cell i will depart from cell j.
The values can be calculated as
• Where d(p(t),j) is the distance between the
current position p(t) and the boundary of cell i
and j, and d(j) is the route distance inside cell
j.
CAC scheme
• Idea:- Verify the feasibility of accepting new
and handoff connections under the
conditions of guaranteeing the QOS of
existing connections and maximizing the
utilization
• Achieved by the predictions of where an MS
will visit using the scheme and when an MS
will visit
• P(i,j,ta,td)the probability that an MS original in
cell i will visit cell j during the time interval
taand td.
CAC Scheme(Cont..)
• Connection duration follows Exponential
distribution with rate ud.
• Where P(i,j) is calculated from Trie.
CAC Scheme(Cont..)
• When an MS is active in cell ,we can obtain
the most likely cell-time (MLCT) of that MS, a
cluster of cells and time where and when the
MS will most likely visit in the future.
• MLCT is defined as
• Required bandwidth to be reserved in cell j for
the expected handoff of m from cell i
CAC Scheme(Cont..)
• Reserved bandwidth at a cell j is given by
where M is a set of MSs which will visit cell j from a set of
I cells during the time interval
• Free Bandwidth Bf
How CAC works
• Let
denote the minimum value of
free bandwidth in cell j during the time interval
• When a new connection arriving at MS m with a
bandwidth requirement B(m) requires admission
to cell i, the CAC algorithm first checks if the
current free bandwidth of cell i can support the
connection.
• connection is rejected if the cell does not have
enough free bandwidth
How CAC works(cont…)
• Otherwise, CAC will check the availability of
free bandwidth in the MLCT of this MS. The
checking result can be written as
• Condition for admission of new Connection
Where α is the admission threshold and should be controlled
adaptively.
• For MS m, calculate
which denotes the
target value of handoff dropping probability
• If
the admission threshold is
decreased by E, a design parameter; otherwise is
increased by E.
Results
• Extra assumptions
– Cell ha fixed link capacity of 30 Bandwidth
Units(BU)
– Avg. cell diameter is 1 KM.
– Connection is either a voice(requires 1 BU) or
video (requires 4 BU) with probabilitues Rv and 1Rv(voice ratio).
– Offered load is calculated as
• Assumptions cont..
– Phd = 1%
– E (adaptive Factor) = 0.02
– Error in position estimation follows normal
distribution N(0,51) with accuracy level of 50 m in
67% cases.
– Simulations start with no previous data
– Phd(hand off dropping prob.), Pnb(new
connection blocking probability) and utilization
are calculated after 100-h simulation time.
Variation of Phd and Pnb withoffered
load at different connection arrival
rate
Phd and Pnb as functions of simulation
time
Comparision with Guard Channel
Scheme
• In GC, 4 BUs are reserved for handoff
connections.
• In OKS98, bandwidth is reserved in all neighboring cells
whenan MS has a new connection or handoffs to a new cell
• In YL01 [7], only in-session but not out-of-session mobility
information is collected and used for prediction.
More Comparisions
Conclusion
• more realistic assumptions
• presented a novel framework of combining
QoS provisioning and LM using all available
mobility information.
• By predicting where and when an MS will
hand off, we can design more efficient channel
allocation schemes and prefetching protocols
for continuous media streaming in wireless
cellular environment
Questions??