WiFi MAC Models David Malone
|
|
- Denis Dawson
- 5 years ago
- Views:
Transcription
1 WiFi MAC Models David Malone November 26, MACSI Hamilton Institute, NUIM, Ireland
2 Talk outline Introducing the CSMA/CA MAC. Finite load model and its predictions. Issues with standard 82.11, leading to 82.11e. Finite load 82.11e model and its predictions. Beyond infrastructure mode networks. Why do these models work? Hamilton Institute, NUIM, Ireland 1
3 The MAC Data Ack Select Random Number in [,31] Pause Counter Resume Counter Expires; Transmit Decrement counter SIFS DIFS SIFS DIFS Figure 1: MAC operation Hamilton Institute, NUIM, Ireland 2
4 82.11 MAC Summary After transmission choose rand(, CW 1). Wait until medium idle for DIFS(5µs), While idle count down in slots (2µs). Transmission when counter gets to, ACK after SIFS (1µs). If ACK then CW = CW min else CW = 2. Ideally produces even distribution of packet transmission. Hamilton Institute, NUIM, Ireland 3
5 Modelling approaches P-persistent: approximate the back-off distribution be a geometric with the same mean. Exemplified by Marco Conti and co-authors. Asymptotic full system analysis: Bordenave, McDonald and Proutiere + Sharma, Ganesh and Key. Bianchi s mean-field Markov model: treat stations individually; network relationship between stations gives a set of coupling equations. In simplest form: constant transmission probability τ gives throughput S = nτ(1 τ) n 1, (1) and collision probability 1 p = (1 τ) n 1. (2) Hamilton Institute, NUIM, Ireland 4
6 Mean-field Markov Overview Mean field approximation: each individual station s impact on overall network is small. Assume a fixed probability of collision given attempted transmission p. Each station s back-off counter then a Markov chain. Stationary distribution gives the probability the station attempts transmission in a typical slot τ(p). Network coupling then gives a system of equations relating all stations p and τ, which determines everything. Real-time quantities determined through a relation involving the average real-time that passes during a counter decrement. Following to appear in IEEE/ACM ToN (Duffy, Leith, Malone). Hamilton Institute, NUIM, Ireland 5
7 Mean-field Markov Model s Chain (1 q)(1 p) (1 q)(1 p) (1 q) q(1 p)(1 p)(1 q) (1 q) (1 q) (1 q) (,) e (,1) e (, CW 2) e (, CW 1) e q(1 p) q q(1 p)p qp q q (,) 1 (,1) 1 (, CW 2) 1 (, CW 1) q(1 p) p (1,) 1 (1,1) 1 1 (1, 2CW 2) (1, 2CW 1) p Figure 2: Individual s Markov Chain Hamilton Institute, NUIM, Ireland 6
8 Mean-field Markov Model Solution Stationary distribution of Markov chain gives: ( τ(p, q, W, m) = η 1 q 2 ) W q2 (1 p), (3) (1 p)(1 q)(1 (1 q) W ) 1 q where ( ) η = (1 q) + q2 W (W +1) 2(1 (1 q) W ) + q(w +1) q 2 W 2(1 q) + p(1 q) q(1 1 (1 q) W ( ) ( ) p)2 pq + 2 W 2(1 q)(1 p) (1 1 (1 q) W p)2 1 p p(2p) 2W m 1 1 2p + 1. Hamilton Institute, NUIM, Ireland 7
9 Network coupling For given loads q 1,...,q n, define τ j = τ(p j,q j, W, m) and then n coupling equations: 1 p i = j i(1 τ j ). Solve to determine (p 1, τ 1 ),...,(p n,τ n ). If all packets are the same length, L bytes taking T L time on the medium, then S i = τ i (1 p i )L n i=1 (1 τ i)δ + (1 n i=1 (1 τ i))t L. Hamilton Institute, NUIM, Ireland 8
10 Relating q to offered load Taking lim q 1 models saturation. For small buffers, a crude approximation: q = min(expected slot length/mean inter-packet time,1). If packets arrive a Poisson manner with rate λ l, then q l is 1 exp( λ l Expected slot length). Possible to produce a relation of this sort that uses conditional information. Hamilton Institute, NUIM, Ireland 9
11 Model Predictions.35.3 normalised throughput normalised total offered load 16 stations (sim) (model unif) (model Pois) (model Cond) 2 stations (sim) (model unif) (model Pois) (model Cond) 24 stations (sim) (model unif) (model Pois) (model Cond) Throughput as the traffic arrival rate is varied. Results for three load relationships (uniform, Poisson and conditional) shown. Hamilton Institute, NUIM, Ireland 1
12 Model Predictions.2.18 model class 1 model class 2 simulation normalised per-node throughput total offered normalised load Normalized per-station throughput, where n 1 = 12, n 2 = 24. The offered load of a class 2 station is 1/4 of a class 1 station. Hamilton Institute, NUIM, Ireland 11
13 TCP Upload Scenario Access Point Stations with competing TCP uploads Figure 3: Competing TCP uploads. Hamilton Institute, NUIM, Ireland 12
14 TCP Uploads TCP throughput (Mbps) Number of the upstream connection Figure 4: Competing TCP uploads, 1 stations (NS2 simulation, MAC, 3s duration). Hamilton Institute, NUIM, Ireland 13
15 The 82.11e MAC The three most significant 82.11e MAC parameters on traffic prioritization are TXOP, W and AIFS. Four traffic classes per station. Station transmits for max duration TXOP (one packet without 82.11e). Per class, W is 2 n, n {,1,...}. Per class, AIFS = DIFS + kδ, k { 2, 1,, 1,...}. Hamilton Institute, NUIM, Ireland 14
16 The MAC Data Ack Select Random Number in [,W 1] Pause Counter Resume Counter Expires; Transmit Decrement counter SIFS AIFS SIFS AIFS Figure 5: MAC operation Hamilton Institute, NUIM, Ireland 15
17 Existing 82.11e models Saturated 82.11e multi-class models. R. Battiti and Bo Li, University of Trento Technical Report DIT-3-24 (23). J.W. Robinson and T.S. Randhawa, IEEE JSAC 22:5 (24). Z. Kong, D. H.K. Tsang, B. Bensaou and D. Gao, IEEE JSAC 22:1 (24). Following (maybe!) to appear in IEEE Trans. Mob. Computing, with Clifford, Duffy, Foy, Leith and Malone. Hamilton Institute, NUIM, Ireland 16
18 Modelling 82.11e Added complications: Need hold states for AIFS. P h = (1 n 1 j=1 (1 τ(1) j ) n 2 j=1 (1 τ(2) j )) 1 + (1 n 1 j=1 (1 τ(1) j ) n 2 j=1 (1 τ(2) j )) D i=1 P i S 1 D i=1 P i S 1. (4) Hamilton Institute, NUIM, Ireland 17
19 New coupling equations 1 p (1) i = (1 τ (1) j )(P h + (1 P h ) j i 1 p (2) i = n 1 j=1 n 2 j=1 (1 τ (2) j )) (5) (1 τ (1) j ) (1 τ (2) j ). (6) j i Hamilton Institute, NUIM, Ireland 18
20 How good is it?.25 model class 1 model class 2 simulation.25 model class 1 model class 2 simulation.25 model class 1 model class 2 simulation Per station throughput (Mbps) Per station throughput (Mbps) Per station throughput (Mbps) Total offered load (Mbps) (a) D = Total offered load (Mbps) (b) D = Total offered load (Mbps) (c) D = 4 Throughput for a station in each class vs. offered load. 1 class 1 stations offering one quarter the load of 2 class 2 stations. Range of D values, the difference in AIFS between class 2 and class 1 (NS2 simulation and model predictions, 82.11e MAC, 11Mbps PHY, 1s duration. ). Hamilton Institute, NUIM, Ireland 19
21 How good is it?.25 model class 1 model class 2 simulation.25 model class 1 model class 2 simulation.25 model class 1 model class 2 simulation Per station throughput (Mbps) Per station throughput (Mbps) Per station throughput (Mbps) Total offered load (Mbps) (d) W (1) = 32, W (2) = Total offered load (Mbps) (e) W (1) = 32, W (2) = Total offered load (Mbps) (f) W (1) = 32,W (2) = 256 Throughput for a station in each class vs. offered load. There are 1 class 1 stations each offering one quarter the load of 2 class 2 stations. Range of W values (NS2 simulation and model predictions, 82.11e MAC 11Mbps PHY, 1s duration). Hamilton Institute, NUIM, Ireland 2
22 How do you use it? 6 5 W 1 =1 W 1 =2 W 1 =4 W 1 =8 W 1 =16 W 1 = W 1 =1 W 1 =2 W 1 =4 W 1 =8 W 1 =16 W 1 =32 Throughput of Data Stations (Mbps) Ratio of lost traffic to offered load Difference in AIFS Difference in AIFS (g) Data throughput (h) ACK loss 1 stations (15 byte packets) and AP transmitting (6 byte packets) at half achieved data rate. Hamilton Institute, NUIM, Ireland 21
23 Does it work? TCP Throughput (Mbps) TCP Throughput (Mbps) Number of the upstream connection Number of the upstream connection Competing TCP uploads, 12 stations experiment without and with prioritization (82.11e MAC, 3s duration). Hamilton Institute, NUIM, Ireland 22
24 Why stop with single infrastructure mode network? Basic behavior of individual stations is independent of the network in which they exist. Change the network coupling, change the network. Hamilton Institute, NUIM, Ireland 23
25 Typical mesh issue throughput (Mbs) NS r 1 NS r #conversations Example of aggregate throughput vs number of voice calls for the multi-hop 82.11b WLAN topology. Voice packets are transported between l 1 1 and l 2 1,...,l 2 N by node r 1 /r 2 which denotes a relay station with two radios. Hamilton Institute, NUIM, Ireland 24
26 Mesh Following in IEEE Comms. Letters (26), (Duffy, Leith, Li and Malone). M distinct local zones on common frequency. For n {1,..., M} local stations L n = {l n 1,...} and relay stations R n = {r n,...}. Mean field gives for each station c R n L n : 1 p c = b R n L n, b =c (1 τ b ). (7) The stationary probability the medium is idle is p idle = b R n L n (1 τ b ). The mean state length is E n = p idle σ + L(1 p idle ), where each packet takes L seconds and idle slot-length is σ seconds. Added difficulty: for each n, l L n, q l is given, but q r is not known a priori for each relay station. Hamilton Institute, NUIM, Ireland 25
27 Mesh The parameter q r is determined through relay traffic. For each n, l L n, a fixed route f l from its zone to a destination zone. f l = {l, s 1...,s m, d}. If m =, then l and d are in the same zone and no relaying occurs. We assume routes are predetermined by an appropriate wireless routing protocol. Hamilton Institute, NUIM, Ireland 26
28 Mesh For each s L n R n, let E(s) = E n. For k {1,...,m} Let Q l,sk be offered load from l arriving at s k and Q sk be the total load offered to s k. From these we calculate: S sk = τ s k (1 p sk ) E n and then assume: Q sk+1 = Q l,sk Q sk S sk to calculate the load in the next network. Hamilton Institute, NUIM, Ireland 27
29 Buffering Limitations of small buffers particularly apparent in mesh. Need to introduce queue empty probability to model queue: r n. Model queues as M/G/1. E(B(p)) = W 2(1 2p) (1 p p(2p)m ). (8) r n = min(1, B(p n )log(1 q n )) (9) Simply replaces τ(p) relation. To appear, IEEE Comms. Letters (27), (Duffy, Ganesh). Seeing some interaction between buffering and service. Hamilton Institute, NUIM, Ireland 28
30 Unfairness One conversation, N data stations 5 4 Conv. throughput, small buffer Data throughput, when conversation has small buffer Conv. throughput, large buffer Data throughput, when conversation has large buffer 64 kbps Throughput (kbps) 3 2 <-- Mean delay, conv., large buffer Delay (milli seconds) Number of data stations, N Figure 6: NS packet-level simulation results and model predictions Hamilton Institute, NUIM, Ireland 29
31 Understanding the Models Models are inexact in several ways. Constant p replaces complex Markov chain with direct sum. Throughput relationship assumes independent. Do assumptions hold or are stationary distributions similar? Hamilton Institute, NUIM, Ireland 3
32 Is p constant?.6.5 Average P(col) P(col on 1st tx) P(col on 2nd tx) 1./32 1./ Probability.3 Probability Offered Load (per station, pps, 496B UDP payload) (a) 2 Stations Average P(col) P(col on 1st tx) P(col on 2nd tx) Offered Load (per station, pps, 486B UDP payload) (b) 1 Stations Figure 7: Measured collision probabilities as offered load is varied. Hamilton Institute, NUIM, Ireland 31
33 Independence of Transmissions Throughput (Kbps) Throughput (measured) Throughput (model) Throughput (model + measured Ts/Tc) Throughput (model + measured Ts/Tc/p) Number of STA(s) Figure 8: Overall throughput in a network of saturated stations as the number of stations is varied. The measured values are compared to model predictions. Hamilton Institute, NUIM, Ireland 32
34 Conclusions 82.11/82.11e CSMA/CA models that are simple, solvable, yet complex enough to predict data throughput. Model gives insight into MAC behavior. Model gives insight into effect of 82.11e parameters. Prioritization schemes can now be designed quickly based on the model. Extensible to network scenarios. Interesting questions on buffering and foundations remain to be answered. Hamilton Institute, NUIM, Ireland 33
Giuseppe Bianchi, Ilenia Tinnirello
Capacity of WLAN Networs Summary Per-node throughput in case of: Full connected networs each node sees all the others Generic networ topology not all nodes are visible Performance Analysis of single-hop
More informationPerformance Analysis of the IEEE e Block ACK Scheme in a Noisy Channel
Performance Analysis of the IEEE 802.11e Block ACK Scheme in a Noisy Channel Tianji Li, Qiang Ni, Hamilton Institute, NUIM, Ireland. Thierry Turletti, Planete Group, INRIA, France. Yang Xiao, University
More informationPerformance analysis of IEEE WLANs with saturated and unsaturated sources
Performance analysis of IEEE 82.11 WLANs with saturated and unsaturated sources Suong H. Nguyen, Hai L. Vu, Lachlan L. H. Andrew Centre for Advanced Internet Architectures, Technical Report 11811A Swinburne
More informationGiuseppe Bianchi, Ilenia Tinnirello
Capacity of WLAN Networs Summary Ł Ł Ł Ł Arbitrary networ capacity [Gupta & Kumar The Capacity of Wireless Networs ] Ł! Ł "! Receiver Model Ł Ł # Ł $%&% Ł $% '( * &%* r (1+ r Ł + 1 / n 1 / n log n Area
More informationWireless Internet Exercises
Wireless Internet Exercises Prof. Alessandro Redondi 2018-05-28 1 WLAN 1.1 Exercise 1 A Wi-Fi network has the following features: Physical layer transmission rate: 54 Mbps MAC layer header: 28 bytes MAC
More informationOn the Validity of IEEE MAC Modeling Hypotheses
On the Validity of IEEE 82.11 MAC Modeling Hypotheses K. D. Huang, K. R. Duffy and D. Malone Hamilton Institute, National University of Ireland, Maynooth, Ireland. Corresponding author: ken.duffy@nuim.ie
More informationPerformance Evaluation of Deadline Monotonic Policy over protocol
Performance Evaluation of Deadline Monotonic Policy over 80. protocol Ines El Korbi and Leila Azouz Saidane National School of Computer Science University of Manouba, 00 Tunisia Emails: ines.korbi@gmail.com
More informationService differentiation without prioritization in IEEE WLANs
Service differentiation without prioritization in IEEE 8. WLANs Suong H. Nguyen, Student Member, IEEE, Hai L. Vu, Senior Member, IEEE, and Lachlan L. H. Andrew, Senior Member, IEEE Abstract Wireless LANs
More informationModeling Approximations for an IEEE WLAN under Poisson MAC-Level Arrivals
Modeling Approximations for an IEEE 802.11 WLAN under Poisson MAC-Level Arrivals Ioannis Koukoutsidis 1 and Vasilios A. Siris 1,2 1 FORTH-ICS, P.O. Box 1385, 71110 Heraklion, Crete, Greece 2 Computer Science
More informationPerformance analysis of IEEE WLANs with saturated and unsaturated sources
1 Performance analysis of IEEE 8.11 WLANs with saturated and unsaturated sources Suong H. Nguyen, Student Member, IEEE, Hai L. Vu, Senior Member, IEEE, and Lachlan L. H. Andrew, Senior Member, IEEE Abstract
More informationMean Field Markov Models of Wireless Local Area Networks
Mean Field Markov Models of Wireless Local Area Networks Ken R. Duffy 26 th September 2010; revised 8 th April 2010. To appear in Markov Processes and Related Fields Abstract In 1998, Giuseppe Bianchi
More informationMathematical Analysis of IEEE Energy Efficiency
Information Engineering Department University of Padova Mathematical Analysis of IEEE 802.11 Energy Efficiency A. Zanella and F. De Pellegrini IEEE WPMC 2004 Padova, Sept. 12 15, 2004 A. Zanella and F.
More informationrequests/sec. The total channel load is requests/sec. Using slot as the time unit, the total channel load is 50 ( ) = 1
Prof. X. Shen E&CE 70 : Examples #2 Problem Consider the following Aloha systems. (a) A group of N users share a 56 kbps pure Aloha channel. Each user generates at a Passion rate of one 000-bit packet
More informationA Comprehensive Study of the IEEE e Enhanced Distributed Control Access (EDCA) Function. Chunyu Hu and Jennifer C. Hou.
Report No. UIUCDCS-R-26-27 UILU-ENG-26-743 A Comprehensive Study of the IEEE 82.e Enhanced Distributed Control Access EDCA) Function by Chunyu Hu and Jennifer C. Hou April 26 A Comprehensive Study of the
More informationDetecting Stations Cheating on Backoff Rules in Networks Using Sequential Analysis
Detecting Stations Cheating on Backoff Rules in 82.11 Networks Using Sequential Analysis Yanxia Rong Department of Computer Science George Washington University Washington DC Email: yxrong@gwu.edu Sang-Kyu
More informationNICTA Short Course. Network Analysis. Vijay Sivaraman. Day 1 Queueing Systems and Markov Chains. Network Analysis, 2008s2 1-1
NICTA Short Course Network Analysis Vijay Sivaraman Day 1 Queueing Systems and Markov Chains Network Analysis, 2008s2 1-1 Outline Why a short course on mathematical analysis? Limited current course offering
More informationMax-min Fairness in Mesh Networks
Max-min Fairness in 802. Mesh Networks Douglas J. Leith, Qizhi Cao, Vijay G. Subramanian Hamilton Institute, NUI Maynooth arxiv:002.58v2 [cs.ni] 3 Mar 200 Abstract In this paper we build upon the recent
More informationFlow-level performance of wireless data networks
Flow-level performance of wireless data networks Aleksi Penttinen Department of Communications and Networking, TKK Helsinki University of Technology CLOWN seminar 28.8.08 1/31 Outline 1. Flow-level model
More informationExact Distribution of Access Delay in IEEE DCF MAC
Exact Distribution of Access Delay in IEEE 8.11 DCF MAC Teerawat Issariyakul, Dusit Niyato, Ekram Hossain, and Attahiru Sule Alfa University of Manitoba and TRLabs Winnipeg, MB, Canada. Email: teerawat,
More informationLecture 7: Simulation of Markov Processes. Pasi Lassila Department of Communications and Networking
Lecture 7: Simulation of Markov Processes Pasi Lassila Department of Communications and Networking Contents Markov processes theory recap Elementary queuing models for data networks Simulation of Markov
More informationChapter 5. Elementary Performance Analysis
Chapter 5 Elementary Performance Analysis 1 5.0 2 5.1 Ref: Mischa Schwartz Telecommunication Networks Addison-Wesley publishing company 1988 3 4 p t T m T P(k)= 5 6 5.2 : arrived rate : service rate 7
More information1. WiMAX Wi-Fi Wi-Fi (AP) Wi-Fi 1 1 AP AP WLAN [1] [1] [1] WLAN [4] WLAN WLAN WLAN 1 WLAN WLAN WLAN N =3 [1] W
[] LAN 263 8522 1-33 514-857 1577 NEC 211-8666 1753 464-861 E-mail: {aoki@, kmr@faculty., shioda@faculty., sekiya@faculty.}chiba-u.jp, k.sanada@elec.mie-u.ac.jp 一般社団法人 電子情報通信学会 信学技報 THE INSTITUTE OF ELECTRONICS,
More informationOptimization of IEEE Multirate Wireless LAN
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26 77-785 (200) Optimization of IEEE 802 Multirate Wireless LAN A V BABU LILLYKUTTY JACOB AND S ABDUL SUBHAN Department of Electronics and Communication Engineering
More informationModeling the Effect of Transmission Errors on TCP Controlled Transfers over Infrastructure Wireless LANs
Modeling the Effect of Transmission Errors on TCP Controlled Transfers over Infrastructure 8 Wireless LANs ABSTRACT Subhashini Krishnasamy Dept of Electrical Communication Engg Indian Institute of Science,
More informationSolutions to COMP9334 Week 8 Sample Problems
Solutions to COMP9334 Week 8 Sample Problems Problem 1: Customers arrive at a grocery store s checkout counter according to a Poisson process with rate 1 per minute. Each customer carries a number of items
More informationOutline Network structure and objectives Routing Routing protocol protocol System analysis Results Conclusion Slide 2
2007 Radio and Wireless Symposium 9 11 January 2007, Long Beach, CA. Lifetime-Aware Hierarchical Wireless Sensor Network Architecture with Mobile Overlays Maryam Soltan, Morteza Maleki, and Massoud Pedram
More informationTuning the TCP Timeout Mechanism in Wireless Networks to Maximize Throughput via Stochastic Stopping Time Methods
Tuning the TCP Timeout Mechanism in Wireless Networks to Maximize Throughput via Stochastic Stopping Time Methods George Papageorgiou and John S. Baras Abstract We present an optimization problem that
More informationPerformance Evaluation of Queuing Systems
Performance Evaluation of Queuing Systems Introduction to Queuing Systems System Performance Measures & Little s Law Equilibrium Solution of Birth-Death Processes Analysis of Single-Station Queuing Systems
More informationQueueing Theory I Summary! Little s Law! Queueing System Notation! Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K "
Queueing Theory I Summary Little s Law Queueing System Notation Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K " Little s Law a(t): the process that counts the number of arrivals
More informationPower Controlled FCFS Splitting Algorithm for Wireless Networks
Power Controlled FCFS Splitting Algorithm for Wireless Networks Ashutosh Deepak Gore Abhay Karandikar Department of Electrical Engineering Indian Institute of Technology - Bombay COMNET Workshop, July
More informationATM VP-Based Ring Network Exclusive Video or Data Traffics
ATM VP-Based Ring Network Exclusive Video or Data Traffics In this chapter, the performance characteristic of the proposed ATM VP-Based Ring Network exclusive video or data traffic is studied. The maximum
More informationIntroduction to Markov Chains, Queuing Theory, and Network Performance
Introduction to Markov Chains, Queuing Theory, and Network Performance Marceau Coupechoux Telecom ParisTech, departement Informatique et Réseaux marceau.coupechoux@telecom-paristech.fr IT.2403 Modélisation
More informationApproximate Queueing Model for Multi-rate Multi-user MIMO systems.
An Approximate Queueing Model for Multi-rate Multi-user MIMO systems Boris Bellalta,Vanesa Daza, Miquel Oliver Abstract A queueing model for Multi-rate Multi-user MIMO systems is presented. The model is
More informationIntroduction to queuing theory
Introduction to queuing theory Claude Rigault ENST claude.rigault@enst.fr Introduction to Queuing theory 1 Outline The problem The number of clients in a system The client process Delay processes Loss
More informationOn Selfish Behavior in CSMA/CA Networks
On Selfish Behavior in CSMA/CA Networks Mario Čagalj1 Saurabh Ganeriwal 2 Imad Aad 1 Jean-Pierre Hubaux 1 1 LCA-IC-EPFL 2 NESL-EE-UCLA March 17, 2005 - IEEE Infocom 2005 - Introduction CSMA/CA is the most
More informationA POMDP Framework for Cognitive MAC Based on Primary Feedback Exploitation
A POMDP Framework for Cognitive MAC Based on Primary Feedback Exploitation Karim G. Seddik and Amr A. El-Sherif 2 Electronics and Communications Engineering Department, American University in Cairo, New
More informationModeling and Simulation NETW 707
Modeling and Simulation NETW 707 Lecture 6 ARQ Modeling: Modeling Error/Flow Control Course Instructor: Dr.-Ing. Maggie Mashaly maggie.ezzat@guc.edu.eg C3.220 1 Data Link Layer Data Link Layer provides
More informationWhat maths can tell us about WiFi (and vice versa)?
What maths can tell us about WiFi (and vice versa)? David Malone 28 January 2009 1 The Plan Do a little mathematical modeling. Interested in 802.11 (WiFi). Example MAC layer. Detour
More informationTCP over Cognitive Radio Channels
1/43 TCP over Cognitive Radio Channels Sudheer Poojary Department of ECE, Indian Institute of Science, Bangalore IEEE-IISc I-YES seminar 19 May 2016 2/43 Acknowledgments The work presented here was done
More informationOn the MAC for Power-Line Communications: Modeling Assumptions and Performance Tradeoffs
On the MAC for Power-Line Communications: Modeling Assumptions and Performance Tradeoffs Technical Report Christina Vlachou, Albert Banchs, Julien Herzen, Patrick Thiran EPFL, Switzerland, Institute IMDEA
More informationOptimal Association of Stations and APs in an IEEE WLAN
Optimal Association of Stations and APs in an IEEE 802. WLAN Anurag Kumar and Vinod Kumar Abstract We propose a maximum utility based formulation for the problem of optimal association of wireless stations
More informationInformation in Aloha Networks
Achieving Proportional Fairness using Local Information in Aloha Networks Koushik Kar, Saswati Sarkar, Leandros Tassiulas Abstract We address the problem of attaining proportionally fair rates using Aloha
More informationTRANSMISSION STRATEGIES FOR SINGLE-DESTINATION WIRELESS NETWORKS
The 20 Military Communications Conference - Track - Waveforms and Signal Processing TRANSMISSION STRATEGIES FOR SINGLE-DESTINATION WIRELESS NETWORKS Gam D. Nguyen, Jeffrey E. Wieselthier 2, Sastry Kompella,
More informationOpen Loop Optimal Control of Base Station Activation for Green Networks
Open Loop Optimal Control of Base Station Activation for Green etworks Sreenath Ramanath, Veeraruna Kavitha,2 and Eitan Altman IRIA, Sophia-Antipolis, France, 2 Universite d Avignon, Avignon, France Abstract
More informationMultiaccess Communication
Information Networks p. 1 Multiaccess Communication Satellite systems, radio networks (WLAN), Ethernet segment The received signal is the sum of attenuated transmitted signals from a set of other nodes,
More informationPerformance of Random Medium Access Control An asymptotic approach
Performance of Random Medium Access Control An asymptotic approach Charles Bordenave CNRS & Math. Dept. of Toulouse Univ., France charles.bordenave@math.univtoulouse.fr David McDonald Math. Dept. of Ottawa
More informationMin Congestion Control for High- Speed Heterogeneous Networks. JetMax: Scalable Max-Min
JetMax: Scalable Max-Min Min Congestion Control for High- Speed Heterogeneous Networks Yueping Zhang Joint work with Derek Leonard and Dmitri Loguinov Internet Research Lab Department of Computer Science
More informationA Stochastic Model for TCP with Stationary Random Losses
A Stochastic Model for TCP with Stationary Random Losses Eitan Altman, Kostya Avrachenkov Chadi Barakat INRIA Sophia Antipolis - France ACM SIGCOMM August 31, 2000 Stockholm, Sweden Introduction Outline
More informationDelay and throughput analysis of tree algorithms for random access over noisy collision channels
Delay and throughput analysis of tree algorithms for random access over noisy collision channels Benny Van Houdt, Robbe Block To cite this version: Benny Van Houdt, Robbe Block. Delay and throughput analysis
More informationANALYSIS OF THE RTS/CTS MULTIPLE ACCESS SCHEME WITH CAPTURE EFFECT
ANALYSIS OF THE RTS/CTS MULTIPLE ACCESS SCHEME WITH CAPTURE EFFECT Chin Keong Ho Eindhoven University of Technology Eindhoven, The Netherlands Jean-Paul M. G. Linnartz Philips Research Laboratories Eindhoven,
More informationA New Technique for Link Utilization Estimation
A New Technique for Link Utilization Estimation in Packet Data Networks using SNMP Variables S. Amarnath and Anurag Kumar* Dept. of Electrical Communication Engineering Indian Institute of Science, Bangalore
More informationMixed Stochastic and Event Flows
Simulation Mixed and Event Flows Modeling for Simulation Dynamics Robert G. Cole 1, George Riley 2, Derya Cansever 3 and William Yurcick 4 1 Johns Hopkins University 2 Georgia Institue of Technology 3
More informationHow do Wireless Chains Behave? The Impact of MAC Interactions
The Impact of MAC Interactions S. Razak 1 Vinay Kolar 2 N. Abu-Ghazaleh 1 K. Harras 1 1 Department of Computer Science Carnegie Mellon University, Qatar 2 Department of Wireless Networks RWTH Aachen University,
More informationCross-layer Theoretical Analysis of NC-aided. Cooperative ARQ Protocols in Correlated Shadowed Environments (Extended Version)
Cross-layer Theoretical Analysis of NC-aided Cooperative ARQ Protocols in Correlated Shadowed Environments (Extended Version) Angelos Antonopoulos, Member, IEEE, Aris S. Lalos, Member, IEEE, arxiv:408.609v
More informationSTABILITY OF FINITE-USER SLOTTED ALOHA UNDER PARTIAL INTERFERENCE IN WIRELESS MESH NETWORKS
The 8th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 7) STABILITY OF FINITE-USER SLOTTED ALOHA UNDER PARTIAL INTERFERENCE IN WIRELESS MESH NETWORKS Ka-Hung
More informationStability analysis of a cognitive multiple access channel with primary QoS constraints
tability analysis of a cognitive multiple access channel with primary o constraints J. Gambini 1,2,O.imeone 1, U. pagnolini 2,.Bar-Ness 1 andungsooim 3 1 CWCR, NJIT, Newark, New Jersey 07102-1982, UA 2
More informationCS276 Homework 1: ns-2
CS276 Homework 1: ns-2 Erik Peterson October 28, 2006 1 Part 1 - Fairness between TCP variants 1.1 Method After learning ns-2, I wrote a script (Listing 3) that runs a simulation of one or two tcp flows
More informationRandom Access Game. Medium Access Control Design for Wireless Networks 1. Sandip Chakraborty. Department of Computer Science and Engineering,
Random Access Game Medium Access Control Design for Wireless Networks 1 Sandip Chakraborty Department of Computer Science and Engineering, INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR October 22, 2016 1 Chen
More informationcs/ee/ids 143 Communication Networks
cs/ee/ids 143 Communication Networks Chapter 4 Transport Text: Walrand & Parakh, 2010 Steven Low CMS, EE, Caltech Agenda Internetworking n Routing across LANs, layer2-layer3 n DHCP n NAT Transport layer
More informationCooperative HARQ with Poisson Interference and Opportunistic Routing
Cooperative HARQ with Poisson Interference and Opportunistic Routing Amogh Rajanna & Mostafa Kaveh Department of Electrical and Computer Engineering University of Minnesota, Minneapolis, MN USA. Outline
More informationMeta-heuristic Solution for Dynamic Association Control in Virtualized Multi-rate WLANs
Meta-heuristic Solution for Dynamic Association Control in Virtualized Multi-rate WLANs Dawood Sajjadi, Maryam Tanha, Jianping Pan Department of Computer Science, University of Victoria, BC, Canada November
More informationOn the Stability and Optimal Decentralized Throughput of CSMA with Multipacket Reception Capability
On the Stability and Optimal Decentralized Throughput of CSMA with Multipacket Reception Capability Douglas S. Chan Toby Berger Lang Tong School of Electrical & Computer Engineering Cornell University,
More informationcommunication networks
Positive matrices associated with synchronised communication networks Abraham Berman Department of Mathematics Robert Shorten Hamilton Institute Douglas Leith Hamilton Instiute The Technion NUI Maynooth
More informationAnalysis of Scalable TCP in the presence of Markovian Losses
Analysis of Scalable TCP in the presence of Markovian Losses E Altman K E Avrachenkov A A Kherani BJ Prabhu INRIA Sophia Antipolis 06902 Sophia Antipolis, France Email:altman,kavratchenkov,alam,bprabhu}@sophiainriafr
More informationThese are special traffic patterns that create more stress on a switch
Myths about Microbursts What are Microbursts? Microbursts are traffic patterns where traffic arrives in small bursts. While almost all network traffic is bursty to some extent, storage traffic usually
More informationMultimedia Communication Services Traffic Modeling and Streaming
Multimedia Communication Services Medium Access Control algorithms Aloha Slotted: performance analysis with finite nodes Università degli Studi di Brescia A.A. 2014/2015 Francesco Gringoli Master of Science
More informationA Stochastic Control Approach for Scheduling Multimedia Transmissions over a Polled Multiaccess Fading Channel
A Stochastic Control Approach for Scheduling Multimedia Transmissions over a Polled Multiaccess Fading Channel 1 Munish Goyal, Anurag Kumar and Vinod Sharma Dept. of Electrical Communication Engineering,
More informationQueuing Networks: Burke s Theorem, Kleinrock s Approximation, and Jackson s Theorem. Wade Trappe
Queuing Networks: Burke s Theorem, Kleinrock s Approximation, and Jackson s Theorem Wade Trappe Lecture Overview Network of Queues Introduction Queues in Tandem roduct Form Solutions Burke s Theorem What
More informationMultiaccess Problem. How to let distributed users (efficiently) share a single broadcast channel? How to form a queue for distributed users?
Multiaccess Problem How to let distributed users (efficiently) share a single broadcast channel? How to form a queue for distributed users? z The protocols we used to solve this multiaccess problem are
More informationAn Admission Control Mechanism for Providing Service Differentiation in Optical Burst-Switching Networks
An Admission Control Mechanism for Providing Service Differentiation in Optical Burst-Switching Networks Igor M. Moraes, Daniel de O. Cunha, Marco D. D. Bicudo, Rafael P. Laufer, and Otto Carlos M. B.
More information6 Solving Queueing Models
6 Solving Queueing Models 6.1 Introduction In this note we look at the solution of systems of queues, starting with simple isolated queues. The benefits of using predefined, easily classified queues will
More informationOn the Throughput-Optimality of CSMA Policies in Multihop Wireless Networks
Technical Report Computer Networks Research Lab Department of Computer Science University of Toronto CNRL-08-002 August 29th, 2008 On the Throughput-Optimality of CSMA Policies in Multihop Wireless Networks
More informationPerformance Analysis of Priority Queueing Schemes in Internet Routers
Conference on Information Sciences and Systems, The Johns Hopkins University, March 8, Performance Analysis of Priority Queueing Schemes in Internet Routers Ashvin Lakshmikantha Coordinated Science Lab
More informationTWO PROBLEMS IN NETWORK PROBING
TWO PROBLEMS IN NETWORK PROBING DARRYL VEITCH The University of Melbourne 1 Temporal Loss and Delay Tomography 2 Optimal Probing in Convex Networks Paris Networking 27 Juin 2007 TEMPORAL LOSS AND DELAY
More informationOptimal Physical Carrier Sense for Maximizing Network Capacity in Multi-hop Wireless Networks
Optimal Physical Carrier Sense for Maximizing Network Capacity in Multi-hop Wireless Networks Kyung-Joon Park, Jihyuk Choi, and Jennifer C. Hou Department of Computer Science University of Illinois at
More informationOn the Throughput, Capacity and Stability Regions of Random Multiple Access over Standard Multi-Packet Reception Channels
On the Throughput, Capacity and Stability Regions of Random Multiple Access over Standard Multi-Packet Reception Channels Jie Luo, Anthony Ephremides ECE Dept. Univ. of Maryland College Park, MD 20742
More informationA Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks
A Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks by Doo Il Choi, Charles Knessl and Charles Tier University of Illinois at Chicago 85 South
More informationSignalling Analysis for Adaptive TCD Routing in ISL Networks *
COST 272 Packet-Oriented Service delivery via Satellite Signalling Analysis for Adaptive TCD Routing in ISL Networks * Ales Svigelj, Mihael Mohorcic, Gorazd Kandus Jozef Stefan Institute, Ljubljana, Slovenia
More informationTimestepped Stochastic Simulation. of WLANs
Timestepped Stochastic Simulation 1 of 82.11 WLANs Arunchandar Vasan and A. Udaya Shankar Department of Computer Science and UMIACS University of Maryland College Park, MD 2742 CS-TR-4866 and UMIACS-TR-27-19
More informationThroughput and Fairness Analysis of based Vehicle-to-Infrastructure Data Transfers
211 Eighth IEEE International Conference on Mobile Ad-Hoc and Sensor Systems Throughput and Fairness Analysis of 82.11-based Vehicle-to-Infrastructure Data Transfers Raffaele Bruno, Marco Conti Institute
More informationModelling the Arrival Process for Packet Audio
Modelling the Arrival Process for Packet Audio Ingemar Kaj and Ian Marsh 2 Dept. of Mathematics, Uppsala University, Sweden ikaj@math.uu.se 2 SICS AB, Stockholm, Sweden ianm@sics.se Abstract. Packets in
More informationOn the stability of flow-aware CSMA
On the stability of flow-aware CSMA Thomas Bonald, Mathieu Feuillet To cite this version: Thomas Bonald, Mathieu Feuillet. On the stability of flow-aware CSMA. Performance Evaluation, Elsevier, 010, .
More informationA Mathematical Model of the Skype VoIP Congestion Control Algorithm
A Mathematical Model of the Skype VoIP Congestion Control Algorithm Luca De Cicco, S. Mascolo, V. Palmisano Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari 47th IEEE Conference on Decision
More informationMethodology for Computer Science Research Lecture 4: Mathematical Modeling
Methodology for Computer Science Research Andrey Lukyanenko Department of Computer Science and Engineering Aalto University, School of Science and Technology andrey.lukyanenko@tkk.fi Definitions and Goals
More informationA Virtual Queue Approach to Loss Estimation
A Virtual Queue Approach to Loss Estimation Guoqiang Hu, Yuming Jiang, Anne Nevin Centre for Quantifiable Quality of Service in Communication Systems Norwegian University of Science and Technology, Norway
More informationCapacity management for packet-switched networks with heterogeneous sources. Linda de Jonge. Master Thesis July 29, 2009.
Capacity management for packet-switched networks with heterogeneous sources Linda de Jonge Master Thesis July 29, 2009 Supervisors Dr. Frank Roijers Prof. dr. ir. Sem Borst Dr. Andreas Löpker Industrial
More informationChannel Selection in Cognitive Radio Networks with Opportunistic RF Energy Harvesting
1 Channel Selection in Cognitive Radio Networks with Opportunistic RF Energy Harvesting Dusit Niyato 1, Ping Wang 1, and Dong In Kim 2 1 School of Computer Engineering, Nanyang Technological University
More informationDynamic Power Allocation and Routing for Time Varying Wireless Networks
Dynamic Power Allocation and Routing for Time Varying Wireless Networks X 14 (t) X 12 (t) 1 3 4 k a P ak () t P a tot X 21 (t) 2 N X 2N (t) X N4 (t) µ ab () rate µ ab µ ab (p, S 3 ) µ ab µ ac () µ ab (p,
More informationNetwork Traffic Characteristic
Network Traffic Characteristic Hojun Lee hlee02@purros.poly.edu 5/24/2002 EL938-Project 1 Outline Motivation What is self-similarity? Behavior of Ethernet traffic Behavior of WAN traffic Behavior of WWW
More informationPerformance Analysis of a Network of Event-Based Systems
3568 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 61, NO. 11, NOVEMBER 2016 Performance Analysis of a Network of Event-Based Systems Chithrupa Ramesh, Member, IEEE, Henrik Sandberg, Member, IEEE, and Karl
More informationMessage Delivery Probability of Two-Hop Relay with Erasure Coding in MANETs
01 7th International ICST Conference on Communications and Networking in China (CHINACOM) Message Delivery Probability of Two-Hop Relay with Erasure Coding in MANETs Jiajia Liu Tohoku University Sendai,
More informationAnalysis of random-access MAC schemes
Analysis of random-access MA schemes M. Veeraraghavan and Tao i ast updated: Sept. 203. Slotted Aloha [4] First-order analysis: if we assume there are infinite number of nodes, the number of new arrivals
More informationComputer Networks ( Classroom Practice Booklet Solutions)
Computer Networks ( Classroom Practice Booklet Solutions). Concept Of Layering 0. Ans: (b) Sol: Data Link Layer is responsible for decoding bit stream into frames. 0. Ans: (c) Sol: Network Layer has the
More informationAn engineering approximation for the mean waiting time in the M/H 2 b /s queue
An engineering approximation for the mean waiting time in the M/H b /s queue Francisco Barceló Universidad Politécnica de Catalunya c/ Jordi Girona, -3, Barcelona 08034 Email : barcelo@entel.upc.es Abstract
More informationStability Analysis in a Cognitive Radio System with Cooperative Beamforming
Stability Analysis in a Cognitive Radio System with Cooperative Beamforming Mohammed Karmoose 1 Ahmed Sultan 1 Moustafa Youseff 2 1 Electrical Engineering Dept, Alexandria University 2 E-JUST Agenda 1
More informationIntegrity-Oriented Content Transmission in Highway Vehicular Ad Hoc Networks
VANET Analysis Integrity-Oriented Content Transmission in Highway Vehicular Ad Hoc Networks Tom Hao Luan, Xuemin (Sherman) Shen, and Fan Bai BBCR, ECE, University of Waterloo, Waterloo, Ontario, N2L 3G1,
More informationQueues and Queueing Networks
Queues and Queueing Networks Sanjay K. Bose Dept. of EEE, IITG Copyright 2015, Sanjay K. Bose 1 Introduction to Queueing Models and Queueing Analysis Copyright 2015, Sanjay K. Bose 2 Model of a Queue Arrivals
More informationPerformance Effects of Two-way FAST TCP
Performance Effects of Two-way FAST TCP Fei Ge a, Sammy Chan b, Lachlan L. H. Andrew c, Fan Li b, Liansheng Tan a, Moshe Zukerman b a Dept. of Computer Science, Huazhong Normal University, Wuhan, P.R.China
More informationMaximum Sum Rate of Slotted Aloha with Capture
Maximum Sum Rate of Slotted Aloha with Capture Yitong Li and Lin Dai, Senior Member, IEEE arxiv:50.03380v3 [cs.it] 7 Dec 205 Abstract The sum rate performance of random-access networks crucially depends
More information