cellular concept

SyllabusUnit 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 Book1. 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.


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

Time

Frequency

Codes

TDMA: Time Division Multiple Access FDMA:

Frequency Division Multiple Access

CMDA: Code Division Multiple Access


– 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


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.


– 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.


– 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.


• 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


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


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

Cell splitting to increase capacityA

B

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:

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

f4

f5

f1f3

f2

f6

f7

f3f2

f4

f5

f1

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 clusterwith 3 sector antennas

f1f1 f1f2f3

f2

f3

f2

f3h1

h2

h3g1

g2

g3

h1h2

h3g1

g2g3

g1g2g3

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= ColorChannel= Frequency= Color

• HybridHybrid 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 graphsD= Maximum total weight on any cliqueLower bound on number of channels: D

D/3

D/2 D/6

D/2

D/2 D/2

D/2

D/2

D/2D/2D/2

D/2

000

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.

Needs 9/8D channels

Fixed allocations – reuse distance 2D= 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

D/2

D/2

D/2

Each node takes as many channels as needed from its own set

If necessary, RED borrow from GREEN BLUE borrow from RED GREEN borrow from BLUE

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 interferenceReceiver filter

f1 f3f2interference

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 are r cells apart must be cr apart

Lower bounds: co-site and adjacent-site

Gamst ’86

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

c1 max{vC w(v) | C is a clique}

max {c0 w(u) + (2c1 - c0) vC,vu w(v) | C is a clique containing u} when c0 2c1

u

v x

c0c1c0<2c1

Algorithm: interleaving channels of different color classes

3-colorable graphsDistance between channels = max(c0/3, c1)

Borrowing impossible

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

Borrowing possible

Borrowed channels = change colordynamic 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, 1i,jnd[i] = number of channels demanded by cell i

• OUTPUT: S[i,k] = channel # of k-th call of cell i, 1kd[i]

• CONSTRAINTS: |S[i,k]-S[j,L]|C[i,j],1kd[i], 1Ld[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, 1km, 1jn.

• Optimization: Minimize number of interferences and satisfy demand

Our problem representation and solution space

• Each row F[j,k], 1km, is a combination of d[j] out of m elements (# of 1’s is = d[j])

• Cost function to minimize: C(F)= A+B

• A= total number of co-site constraint violations

• B= total number of adjacent and inter-site violations= 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,c0]

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 cellsp[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 different1011000110 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 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 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, 1im, 1jn: 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

Technology

generation standards

generation cellular

generation cellularnetworksthe

multipleaccess protocols

multiple frequencybands

cellular radio communication

modulation modulation

cellular band