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Cellular concept Cellular concept Presentation Transcript

  • 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, PHI2. Schiller J., Mobile Communications, Addison Wesley3. Viterbi A, CDMA, Addison Wesley4. Gokhle, Introduction to Telecommunications, Delmer Thomson1. “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.
  • First Generation Cellular 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 MethodsFrequency TDMA: Time CMDA: Code Division Division Multiple FDMA: Multiple Access Access Frequency Division Multiple AccessCodes Time
  • First Generation Cellular Networks– Analog FMFrequency modulation (FM) conveysinformation over a carrier wave by varyingits instantaneous frequency.
  • Second Generation Cellular Networks• Second generation standards use multiple access techniques – digital modulation formats – TDMA/FDD – CDMA/FDD
  • Second Generation Cellular NetworksThe most popular second generation standardsinclude three TDMA and oneCDMA 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.
  • Second Generation Cellular 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.
  • Second Generation Cellular 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.
  • Second Generation Cellular 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
  • Second Generation Cellular 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
  • Second Generation Cellular 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.
  • 2.5G Mobile Radio Networks 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.
  • 2.5G Mobile Radio Networks 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.
  • 2.5G Mobile Radio Networks• 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.
  • 2.5G Mobile Radio Networks• 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 TechnologiesRTT: 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 capacityA 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 colorMinimize number of frequencies=colors
  • Reuse distance 2 – reuse patternOne 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 f2f1 f1 f1 f3 h2 f3 h2 f3 h1 h1 g2 h3 g2 h3 g2g1 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 2What 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/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/2Odd cycle bound: induced 9-cycle, each weight D/2Channels needed in this cycle: 9D/2Each channels can be used at most 4 times.
  • Fixed allocations – reuse distance 2D= maximum number of channels in a node or 3-cycleRed : 1, 4, 7, 10, …Green: 2, 5, 8, 11, … Blue: 3, 6, 9, 12, …Total # channels: 3D Performance ratio: 3Janssen, 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 channels3D/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 interferenceCo-site constraint: channels in the same cell must be≥c0 apartAdjacent-site constraint: channels assigned toneighboring cells must be ≥c1 apartInter-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 c0Algorithm: interleaving channels of different color classes
  • 3-colorable graphsDistance between channels = max(c0/3, c1)Borrowing impossibleDistance between channels = max(c0/2, c1)Borrowing possibleBorrowed channels = change color→dynamic CA=online distributed CAChannels with ongoing calls can(not) be borrowed = (non)recoloringk-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)