Cellular concept

Syllabus
Unit 1
Review of wireless and cellular radio communication: The
cellular concept, system design fundamentals, frequency
reuse, reused distance, cluster size, channel assignment
strategies, handoff strategies, co-channel interference and
system capacity, trunking and grade of service.

Unit 2 Speech coding for wireless system applications and
broadcast systems, coding techniques for audio and voice and
popular speech codes. Brief introduction to radio channel
characterization, multi-path propagation, co-channel
interference, exponential power delay profile, propagation
effects, scattering, ground reflection, fading, long normal
shadowing, coherence bandwidth.

Unit 3 Modulation techniques for mobile and satellite
communication, their generation and detection,
performance of spectral and power efficiency. Physical
layer technique, diversity, spread, spectrum, frequency
hopping, direct sequence, adaptive equalization,
Orthogonal Frequency Division Multiplexing (OFDM).

Unit 4 MAC Protocols: IEEE 802.11 and its variants, ETSI-
HIPERLAN type 1 MAC protocol, multiple access with
collision avoidance.

Unit 5 Introduction to GEO, MEO and LEO satellite
systems, Antenna positioning in GEO and Link
calculations, wideband CDMA concepts principles.

Reference Book
1. Wilkies and Garg, Principles of GSM
technology, PHI
2. Schiller J., Mobile Communications,
Addison Wesley
3. Viterbi A, CDMA, Addison Wesley
4. Gokhle, Introduction to
Telecommunications, Delmer Thomson

1. “Wireless Communication”, T.S.
Rappaport

Review of Wireless and Cellular
Radio Communications

Introduction
• Since the mid 1990s, the cellular
communications industry has witnessed
explosive growth.

First Generation Cellular
Networks
• First generation cellular systems that relied
exclusively on
– FDMA(Frequency Division Multiple
Access)/FDD (frequency-division
duplexing)
Frequency Division Multiple Access or
FDMA is a Channel Access Method used
in multiple-access protocols as a
channelization protocol. It is important to
distinguish between users.

Networks
FDMA and frequency-division duplexing
(FDD). While FDMA allows multiple
users simultaneous access to a certain
system, FDD refers to how the radio
channel is shared between the uplink and
downlink (for instance, the traffic going
back and forth between a mobile-phone
and a base-station).

SheikhooOo

Basics: Multiple Access Methods
Frequency TDMA: Time
CMDA: Code
Division
Division
Multiple
FDMA: Multiple Access
Access
Frequency
Division Multiple
Access

Codes

Time

Networks
– Analog FM
Frequency modulation (FM) conveys
information over a carrier wave by varying
its instantaneous frequency.

Second Generation Cellular
Networks
• Second generation standards use multiple
access techniques
– digital modulation formats
– TDMA/FDD
– CDMA/FDD

Networks
The most popular second generation standards
include three TDMA and one
CDMA standard
• Global System Mobile(GSM)
Supports eight time slotted users for each
200 KHz radio channel and has been
deployed widely by service providers in
Europe, Asia, Australia, South America, and
some parts of the US.

Networks
– Interim Standard 136 (IS-136)
Also known as North American Digital Cellular
(NADC), which supports three time slotted
users for each 30 KHz radio channel and is
popular choice for carriers in North America,
South America and Australia.
– Pacific Digital Cellular (PDC)
A Japanese TDMA standard that is similar to
IS-136. More than 50 million users use this
standard.

Networks
– The popular 2G CDMA standard Interim
Standard 95 Code Division Multiple Access
(IS-95)
Also known as CDMAOne . CDMA is widely
deployed by carriers in North America, as well
as in Korea, Japan, China, South America and
Australia.

Networks
• Second generation were first introduced in
the early 1990s
• Evolved (Growth) from the first generation
of analog mobile phone systems (e.g,
AMPS, ETACS, and JTACS).
• Today, many wireless service providers use
both first generation and second generation
equipment in major markets

Networks
and often provide customers with subscriber
units that can support multiple frequency
bands and multiple air interface standards.
• For example, in many countries it is
possible to purchase a single tri-mode
cellular handset phone that supports CDMA
in the cellular band and PCS (personal
communications services) bands in addition
to analog first

Networks
First generation technology in the cellular
band.
• Such tri-mode phones are able to
automatically sense and adapt to whichever
standard is being used in a particular
market.

Key Specification of Leading 2G Technology

Modulation
• Modulation is the process of conveying a
message signal, that can be physically
transmitted.
• BPSK stands for Binary Phase shift keying
modulation
• GMSK stands for Gaussian Minimum Shift
Keying
• DQPSK stands for differential quadrature phase
shift keying

Difference between carriers and
channels
• When we talk about carrier, it is more related to a
signal that usually carries your data.
Signals of different frequencies are called
channels. A carrier can contain different channels.
• For example radio signals: The radio signals (FM
or AM) are carriers because they carry the voice
data along with them. But within the same range
of frequency, you can tune different stations
(different frequencies) i.e. your channels.

2.5G Mobile Radio Networks
Weaknesses of 2G
• 2G technologies use circuit-switched data
modems that limit data users to a single circuit-
switched voice channel. Data transmission in
2G are thus generally limited to the data
throughput rate of an individual user, and this
rate is of the same order of magnitude of the
data rate of the designated speech coders given
in Key Specification of Leading 2G
Technologies.

Weaknesses of 2G
• In 2G, original GSM, CDMA, and IS-136
standards which originally supported 9.6
kilobits per second transmission rates for
data messages.
• Due to relatively small data rates, 2 G
standards are able to support limited
Internet browsing and sophisticated short
messaging capabilities using a circuit
switched approach.

Weaknesses of 2G
• Short messaging Service (SMS) is a popular
feature of GSM but the wireless markets are
fragmented between many different types of
technologies and network owners, and SMS
presently only works between users of the
same network.

• The new standards represent 2.5G
technology and allow existing 2G
equipment to be modified and
supplemented with new base station add-
ons and subscriber unit software upgrades
to support higher data rate transmissions for
web browsing, e-mail traffic and location-
based mobile services.

• The 2.5 G technologies also support a
popular new browsing format language,
called Wireless Application Protocol
(WAP), that allows standard web pages in a
compressed format specifically designed for
small, portable hand held wireless devices.

Various Upgrade paths for 2G
Technologies

RTT: Radio Transmission Technology

Cellular Concept – System
Design Fundamentals

Overview

• Fixed channel assignment
• Multicoloring – co-channel interference
• General problem statement
• Genetic algorithms
• Results and details
• Fixed/dynamic channel and power assignment

Cell structure
• Implements space division multiplex: base station
covers a certain transmission area (cell)
• Mobile users communicate only via the base station
• Advantages of cell structures:
– higher capacity, higher number of users
– less transmission power needed
– more robust, decentralized
– base station deals with interference locally
• Cell sizes from some 100 m in cities to, e.g., 35 km
on the country side (GSM) - even more for higher
frequencies

Cellular architecture
One low power transmitter per cell
Frequency reuse–limited spectrum

B
Cell splitting to increase capacity
A

Reuse distance: minimum distance between
two cells using same channel for satisfactory
signal to noise ratio
Measured in # of cells in between

Problems
– Propagation path loss for signal power: quadratic or higher in
distance
– fixed network needed for the base stations
– handover (changing from one cell to another) necessary
– interference with other cells:
• Co-channel interference:
Transmission on same frequency
• Adjacent channel interference:
Transmission on close frequencies

Reuse pattern for reuse distance 2?

One frequency can be (re)used in all cells of the same color
Minimize number of frequencies=colors

Reuse distance 2 – reuse pattern

One frequency can be (re)used in all cells of the same color

Reuse pattern for reuse distance 3?

Reuse distance 3 – reuse pattern

Frequency planning I
• Frequency reuse only with a certain
distance between the base stations
• Standard model using 7 frequencies:
f3
f5 f2
f4 f6 f5
f1 f4
f3 f7 f1
f2

• Note pattern for repeating the same color:
one north, two east-north

Fixed and Dynamic assignment
• Fixed frequency assignment: permanent
– certain frequencies are assigned to a certain cell
– problem: different traffic load in different cells
• Dynamic frequency assignment: temporary
– base station chooses frequencies depending on the
frequencies already used in neighbor cells
– more capacity in cells with more traffic
– assignment can also be based on interference
measurements

3 cell cluster
with 3 sector antennas

f2 f2 f2
f1 f1 f1
f3 h2 f3 h2 f3
h1 h1
g2 h3 g2 h3 g2
g1 g1 g1
g3 g3 g3

Cell breathing
• CDM systems: cell size depends on current load
• Additional traffic appears as noise to other users
• If the noise level is too high users drop out of
cells

Multicoloring
• Weight w(v) of cell v = # of requested frequencies
• Reuse distance r
• Minimize # channels used: NP hard problem
• Multi-coloring = multi-frequencing
• Channel= Frequency= Color
• Hybrid CA = combination fixed/dyn. frequencies
• Graph representation: weighted nodes, two nodes
connected by edge iff their distance is < r
• same colors cannot be assigned to edge endpoints

Hexagon graphs: reuse distance 2

What is the graph for reuse distance 3?

Lower bounds for hexagonal graphs
D= Maximum total weight on any clique
Lower bound on number of channels: D
D/3 D/2 D/2

D/2 D/2 Needs 9/8D
D/2 D/6 0 channels
0 0
D/2 D/2

D/2 D/2 D/2
Odd cycle bound: induced 9-cycle, each weight D/2
Channels needed in this cycle: 9D/2
Each channels can be used at most 4 times.

Fixed allocations – reuse distance 2
D= maximum number of channels in a node or 3-cycle
Red : 1, 4, 7, 10, …Green: 2, 5, 8, 11, … Blue: 3, 6, 9, 12, …
Total # channels: 3D Performance ratio: 3

Janssen, Kilakos, Marcotte ’95: D/2 red, blue and green each
Each node takes as many channels as needed
from its own set
If necessary, RED borrow from GREEN
BLUE borrow from RED
D/2 D/2 GREEN borrow from BLUE
D/2
If a node has D/2+x channels, no
neighbor has more than D/2-x channels
3D/2 channels used, performance ratio: 3/2

4/3 approximation for reuse distance 2
• McDiarmid-Reed 97, Narayanan-Shende 97, Scabanel-Ubeda-Zerovnik 98

• Base color graph RED, GREEN, BLUE
• D/3 RED, GREEN, BLUE, PURPLE channels
• Each vertex uses at most D/3 channels from own set
• Certain ‘heavy’ vertices (>D/3 colors) borrow from
‘light’ neighbors
• Purple channels used when needed
• max 2 nodes borrow (why?); G=D/3+x, B=D/3+y
• x+y<=D/3 (why?) PURPLE
• In practice, reuse distances 3 or 4 may be used

Feder-Shende algorithm-reuse dist. 3
• Base color underlying graph with 7 colors
• Assign L channels to each color class
• Every node takes as many channels as it needs from
its base color set
• Heavy node (>L colors) borrows any unused
channels from its neighbors
• L=D/3 → algorithm with performance ratio 7/3
• Reuse distance r → perform. ratio 18r2/(3r2+20)
• 2: 2.25, 3: 3.44, 4: 4.23, 5: 4.73 (Narayanan)
• k-colorable graph → perf. ratio k/2 (Janssen-Kilakos 95)

Adjacent channel interference
Receiver filter

f1 f2 f3
interference

Co-site constraint: channels in the same cell must be
≥c0 apart
Adjacent-site constraint: channels assigned to
neighboring cells must be ≥c1 apart
Inter-site constraint: channels assigned to cells that

Lower bounds: co-site and adjacent-site
Gamst ’86 u

c0 max {w(u), w(v), w(x)} v x

c1 max{Σv∈C w(v) | C is a clique}
max {c0 w(u) + (2c1 - c0) Σv∈C,v≠u w(v) | C is a clique
containing u} when c0 ≤ 2c1

c0<2c1 c1 c0
Algorithm: interleaving channels of different color classes

3-colorable graphs
Distance between channels = max(c0/3, c1)
Borrowing impossible

Distance between channels = max(c0/2, c1)
Borrowing possible

Borrowed channels = change color→dynamic CA=online distributed CA
Channels with ongoing calls can(not) be borrowed = (non)recoloring
k-local algorithm: node changes channels based on weights within k cells

Desirable qualities of CA algorithms
• Minimize connection set-up time
• Conserve energy at mobile host
• Adapt to changing load distribution
• Fault tolerance
• Scalability
• Low computation and communication overhead
• Minimize handoffs
• Maximize number of calls that can be accepted
concurrently

Research problem: several power levels
at mobile hosts
• If mobile phone is ‘near’ base station, it may switch
to lower power level
• Interference from other hosts increases
• Interference of that host to other node decreases
• Are there benefits of using two power levels?
• Fixed or dynamic channel and power assignment
and multicoloring: simplest cases
• Fixed or dynamic channel and power assignment
with co-site, adjacent-site and inter-site constraints:
Genetic algorithms, simulated annealing, …

Genetic algorithms
• Rechenberg 1960, Holland 1975 …
• Part of evolutionary computing in AI
• Solution to a problem is evolved (≈Darwin’s theory)
• Represent solutions as a chromosomes = search space
• Generate initial population of solutions
(‘chromosomes’) at random or from other method
• REPEAT
• Evaluate the fitness f(x) of each chromosome x
• Perform crossover, mutation and generate new
population, using f(x) in selecting probabilities
• UNTIL satisfactory solution found or timeout

Fixed channel assignment problem
• INPUT: n = number of cells
Compatibility matrix C, C[i,j]= minimal channel
separation between cells i and j, 1≤i,j≤n
d[i] = number of channels demanded by cell i
• OUTPUT: S[i,k] = channel # of k-th call of cell i, 1≤k≤d[i]
• CONSTRAINTS: |S[i,k]-S[j,L]|≥C[i,j],1≤k≤d[i], 1≤L≤d[j], (i,k)≠(j,L)
• GOAL: minimize m= max S[i,k] = # channels
• reducable to graph coloring problem→ NP-complete
• GA solution space: m fixed, F[j,k]=0/1 if channel k
is not assigned/assigned to cell j, 1≤k≤m, 1≤j≤n.
• Optimization: Minimize number of interferences and satisfy demand

Our problem representation and
solution space
• Each row F[j,k], 1≤k≤m, is a combination of d[j] out
of m elements (# of 1’s is = d[j])
• Cost function to minimize: C(F)= A+αB
• A= total number of co-site constraint violations
• B= total number of adjacent and inter-site violations
∀ α= parameter; C(F)=0 for optimal solution
• Initial population: generate restricted combinations:
• generate random combination of d[j] X’s and m-
(c0+1)d[j] 0’s; replace each X by 100..0 (c0 0’s);
shift circularly → by random number in [0,c ]

Mutation
• Each row=cell is mutated separately
• Combinations in bit representation: x 1’s out of m bits
• Mutation with equal probability for each bit: choose one out
of x 1’s and one out of m-x 0’s at random, swap: Ngo-Li ‘98
• Mutation with different probability for each bit:
b[i]= # of conflicts of i-th selected channel
with other channels in this and other cells
p[i]=b[i]/(b[1]+…+b[x])
Repeat for 0’s: # of conflicts if that channel turned on
• Choosing bit with given probability:
Generate at random r, 0 ≤ r ≤ 1, and choose i, p[1]+…
p[i-1] ≤ r <p[1]+…+p[i]

Crossover
• Regular GA crossover:
1011000110 ⇒ 1001111000
0101111000 ⇒ 0111000110
• Ngo-Li ’98: A and B two parents, each row separately,
preserve # of 1’s in each row:
push 10 and 01 columns in stack if top same;
pop for exchange if top different
1011000110 ⇒ 1001101000
0101111000 ⇒ 0111010110

• Problem: # of swaps varies

New crossover
• t= number of desired swaps in a row
• Mark positions in two combinations that differ
• let s 10’s and s 01’s are found
• Choose t out of s 10 at random and ⇒ 01
• Choose t out of s 01 at random and ⇒ 10
• Example: 1011000110 ⇒ 1001010010
0101111000 ⇒ 0111110110
s=4 t=2 $^$ ^^^$$ # **#
# **# offspring
selected columns

Crossover needs further study
• Problem: independent changes in each row=cell will
destroy good channel assignments of parents
• Two good solutions may have nothing in common
• Try experiments with mutation only
(may be crossover has even negative impact !?)
• Evaluate impact of each column change by cost
function and apply weighted probabilities for
column selections
• Best value for t as function of s? t=s/2? Small t?

Combinatorial evolution strategy
• Sandalidis, Stavroulakis and Rodriguez-Tellez ’98
• Generate λ individuals and evaluate them by f
• Select best individual indiv; indiv1=indiv; counter=0; t=0;

• REPEAT t=t+1
• IF counter=max-count THEN apply increased mutation rate
(destabilize to escape local minimum)
• Generate λ individuals from indiv1 and evaluate them by f
• Select best individual indiv2
• IF indiv2 better than indiv1 THEN {counter=0; indiv=indiv2} ELSE
{counter=counter+1; indiv1=indiv2}
• UNTIL termination
• Applied for fixed, dynamic and hybrid CA

CES for dynamic channel assignment
• n=49 cells, m=49 channels, call arrives at cell k
• F[j,i]=0/1 if channel i is not assigned/assigned to
cell j, 1≤i≤m, 1≤j≤n: current channel assignment for ongoing calls
• Reassignment of all ongoing calls at cell k (channel for
each call may change) to accommodate new call
• V[k,i] = new channel assignment for cell k
• CES minimizes energy function that includes: interference of new
assignment, reusing channels used in nearby cells, reusing channels
according to base coloring scheme, and number of reassignments
• Centralized controller
• CES for Hybrid CA and for borrowing CA in FCA

Simple heuristics for FCA
• Borndorfer, Eisenblatter, Grotschel, Martin ’98
(4240 total demand, m=75 channels, Germany)
• DSATUR: key[i]= # acceptable channels remained in cell i,
cost[i,j]= total interference in cell i if channel j is selected
• Initialize key[i]= m; cost[i,j]=0; ∀i,j
• WHILE cells with unsatisfied demand exist DO {
• Extract cell i with unsatisfied demand and minimum key[i];
• Let j be available channel which minimizes
cost[i,j];
• Update cost[x,y] ∀x,y by adding interference (i,j)
• Update key[x] ∀x, reduce demand at cell i }

Hill climbing heuristic for FCA
• Borndorfer, Eisenblatter, Grotschel, Martin ’98
• Two channel assignments are neighbors if one can be obtained from
the other by replacing one channel by another in one of cells.
• PASS procedure for assignment A={(cell,channel)}:
• Sort all (i,j)∈A by their interference in decreasing order
• FOR each (i,j)∈A in the order DO
• Replace (i,j) by (i,j’) if later has same or lower interference
• Hill climbing for FCA: initialize A; A’=A
• REPEAT
• A=A’; A’= PASS(A)
• UNTIL A’=A or interference(A’)≥interference(A)

Cellular concept

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