CAPACITY OF POWER CONSTRAINED AD-HOC NETWORKS

Slides:



Advertisements
Similar presentations
Impact of Interference on Multi-hop Wireless Network Performance
Advertisements

The Capacity of Wireless Networks Danss Course, Sunday, 23/11/03.
The Capacity of Wireless Networks
Impact of Interference on Multi-hop Wireless Network Performance Kamal Jain, Jitu Padhye, Venkat Padmanabhan and Lili Qiu Microsoft Research Redmond.
Capacity of wireless ad-hoc networks By Kumar Manvendra October 31,2002.
Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
* Distributed Algorithms in Multi-channel Wireless Ad Hoc Networks under the SINR Model Dongxiao Yu Department of Computer Science The University of Hong.
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
Queuing Network Models for Delay Analysis of Multihop Wireless Ad Hoc Networks Nabhendra Bisnik and Alhussein Abouzeid Rensselaer Polytechnic Institute.
DYNAMIC POWER ALLOCATION AND ROUTING FOR TIME-VARYING WIRELESS NETWORKS Michael J. Neely, Eytan Modiano and Charles E.Rohrs Presented by Ruogu Li Department.
Ad-Hoc Networking Course Instructor: Carlos Pomalaza-Ráez D. D. Perkins, H. D. Hughes, and C. B. Owen: ”Factors Affecting the Performance of Ad Hoc Networks”,
Dynamic Tuning of the IEEE Protocol to Achieve a Theoretical Throughput Limit Frederico Calì, Marco Conti, and Enrico Gregori IEEE/ACM TRANSACTIONS.
The Capacity of Wireless Ad Hoc Networks
NCKU CSIE CIAL1 Principles and Protocols for Power Control in Wireless Ad Hoc Networks Authors: Vikas Kawadia and P. R. Kumar Publisher: IEEE JOURNAL ON.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Theory of Data Flow and Random Placement.
Mobility Increases Capacity In Ad-Hoc Wireless Networks Lecture 17 October 28, 2004 EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor.
2002 MURI Minisymposium Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments 2002 MURI Minisymposium Ameesh Pandya Prof.
STOCHASTIC GEOMETRY AND RANDOM GRAPHS FOR THE ANALYSIS AND DESIGN OF WIRELESS NETWORKS Haenggi et al EE 360 : 19 th February 2014.
Capacity of Ad Hoc Networks Quality of Wireless links Physical Layer Issues The Channel Capacity Path Loss Model and Signal Degradation MAC for.
High Throughput Route Selection in Multi-Rate Ad Hoc Wireless Networks Dr. Baruch Awerbuch, David Holmer, and Herbert Rubens Johns Hopkins University Department.
Special Topics on Algorithmic Aspects of Wireless Networking Donghyun (David) Kim Department of Mathematics and Computer Science North Carolina Central.
1 Power Control for Distributed MAC Protocols in Wireless Ad Hoc Networks Wei Wang, Vikram Srinivasan, and Kee-Chaing Chua National University of Singapore.
A Simple and Effective Cross Layer Networking System for Mobile Ad Hoc Networks Wing Ho Yuen, Heung-no Lee and Timothy Andersen.
EE360 PRESENTATION On “Mobility Increases the Capacity of Ad-hoc Wireless Networks” By Matthias Grossglauser, David Tse IEEE INFOCOM 2001 Chris Lee 02/07/2014.
1 Core-PC: A Class of Correlative Power Control Algorithms for Single Channel Mobile Ad Hoc Networks Jun Zhang and Brahim Bensaou The Hong Kong University.
Network Architecture (R02) #4 24/10/2013 Wireless Capacity Jon Crowcroft,
1 Mobility Increases the Capacity of Ad-hoc Wireless Networks Matthias Grossglauser, David Tse IEEE Infocom 2001 (Best paper award) Oct 21, 2004 Som C.
1/30 Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks Wireless and Sensor Network Seminar Dec 01, 2004.
TRANSMISSION POWER CONTROL FOR AD HOC WIRELESS NETWORKS: THROUGHPUT, ENERGY AND FAIRNESS Lujun Jia; Xin Liu; Noubir, G.; Rajaraman, R.; Wireless Communications.
ENERGY-EFFICIENT FORWARDING STRATEGIES FOR GEOGRAPHIC ROUTING in LOSSY WIRELESS SENSOR NETWORKS Presented by Prasad D. Karnik.
Routing and Scheduling for mobile ad hoc networks using an EINR approach Harshit Arora Advisor : Dr. Harlan Russell Mobile ad Hoc Networks A self-configuring.
MAIN RESULT: Depending on path loss and the scaling of area relative to number of nodes, a novel hybrid scheme is required to achieve capacity, where multihop.
Outage in Large Wireless Networks with Spectrum Sharing under Rayleigh Fading MASc. Defence SYSC Dept., Carleton University 1 Arshdeep S. Kahlon, B.E.
Capacity of Large Scale Wireless Networks with Directional Antenna and Delay Constraint Guanglin Zhang IWCT, SJTU 26 Sept, 2012 INC, CUHK 1.
Multicast Scaling Laws with Hierarchical Cooperation Chenhui Hu, Xinbing Wang, Ding Nie, Jun Zhao Shanghai Jiao Tong University, China.
Chance Constrained Robust Energy Efficiency in Cognitive Radio Networks with Channel Uncertainty Yongjun Xu and Xiaohui Zhao College of Communication Engineering,
1 Low Latency Multimedia Broadcast in Multi-Rate Wireless Meshes Chun Tung Chou, Archan Misra Proc. 1st IEEE Workshop on Wireless Mesh Networks (WIMESH),
4 Introduction Carrier-sensing Range Network Model Distributed Data Collection Simulation 6 Conclusion 2.
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
Joint Routing and Scheduling Optimization in Wireless Mesh Networks with Directional Antennas A. Capone, I. Filippini, F. Martignon IEEE international.
-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.
Puzzle You have 2 glass marbles Building with 100 floors
Fundamentals of Cellular Networks (Part III)
Improving Performance of Higher Layer Protocols with MIMO based MAC
Impact of Interference on Multi-hop Wireless Network Performance
Presented by Tae-Seok Kim
Group Multicast Capacity in Large Scale Wireless Networks
Computing and Compressive Sensing in Wireless Sensor Networks
Topics in Distributed Wireless Medium Access Control
Resource Allocation in Non-fading and Fading Multiple Access Channel
On the Physical Carrier Sense in Wireless Ad-hoc Networks
Hidden Terminal Decoding and Mesh Network Capacity
A New Multipath Routing Protocol for Ad Hoc Wireless Networks
Su Yi Babak Azimi-Sadjad Shivkumar Kalyanaraman
High Throughput Route Selection in Multi-Rate Ad Hoc Wireless Networks
INFOCOM 2013 – Torino, Italy Content-centric wireless networks with limited buffers: when mobility hurts Giusi Alfano, Politecnico di Torino, Italy Michele.
Topology Control and Its Effects in Wireless Networks
The Capacity of Wireless Networks
Totally Disjoint Multipath Routing in Multihop Wireless Networks Sonia Waharte and Raoef Boutaba Presented by: Anthony Calce.
Introduction Wireless Ad-Hoc Network
Capacity of Ad Hoc Networks
Javad Ghaderi, Tianxiong Ji and R. Srikant
Pradeep Kyasanur Nitin H. Vaidya Presented by Chen, Chun-cheng
Dhruv Gupta EEC 273 class project Prof. Chen-Nee Chuah
Gaurav Sharma,Ravi Mazumdar,Ness Shroff
<month year> <doc.: IEEE doc> January 2013
<month year> <doc.: IEEE doc> January 2013
Information Sciences and Systems Lab
Lihua Weng Dept. of EECS, Univ. of Michigan
Presentation transcript:

CAPACITY OF POWER CONSTRAINED AD-HOC NETWORKS Prof. Rohit Negi Arjunan Rajeswaran Presented by Jin Heo * The slides are from the authors and modified by Jin Heo

Background – what has been done, how, results ? Outline Introduction – what, why is it a hard problem ? Background – what has been done, how, results ? Recent Research – why, novelty, results ?

Introduction ad hoc wireless networks Infrastructure Last hop is wireless High Cost, maintenance Data rates known Reliable Ad hoc Multi-hop wireless Low cost, maintenance Supported data rate ? Reliability ? Cellular : Key point: Wireless is simply a last-hop link in a primarily wired (telephone/internet) network. Basically for un-tethering. High cost in deployment, signaling billing etc. Ad-hoc : key point : All wireless links – possibly many links till reach infrastructure or not at all. : NO infrastructure. Many variations : mobility , capabilities, traffic patterns, node placement.

Introduction ad hoc wireless networks Consider model with low spectral efficiency Arbitrary large bandwidth Power constrained  Interference becomes relatively negligible! Two applications UWB Sensor network The result is that throughput increases with node enter the network

Capacity of UWB ad-hoc wireless networks Summary Capacity of UWB ad-hoc wireless networks Previous Result : bits/s Our result : bits/s under alternate Communication model (UWB) n is the node density distance loss exponent

Design problem overview source 1 destination 1 This shared wireless channel is scheduled into links by the MAC (link layer) Routes : set of links from each source to destination Created by the routing layer destination 2 source 2 Aim : maximize the uniform throughput per node r (bits/s) ( fairness, uniform capabilities ) Uniform throughput – the throughput achieved by all nodes in the network Two Control Parameters to maximize throughput – Routing and MAC Routing (choosing sets of links) – MAC (scheduling access of shared channel, generates the links)  Strong interaction Definition of uniform throughput – the throughput achieved by all nodes in the network : fairness Now the aim is to maximize this uniform throughput We have the MAC and Routing to control. Variables MAC : scheduling access of the shared channel to the competing nodes. MAC creates the links over which routing may optimize. Routing – scheme to relay data packets form the source to destination in many hops – all wireless ? Strong interaction because the routes decide the link capacities so let us look at a simpler problem – only MAC

Design problem MAC Original ad-hoc network 1 2 3 Flow contention graph Link Contention MAC (in simplest case) ~ MAC scheduling problem to a Flow contention graph: Graph Coloring NP complete Complicated due to Routing - MAC interaction Formally : optimization problem – intractable Exact solution : very hard Need some kind of order bound as a function of number of nodes! MAC problem – the scheduling problem may be made explicit by a conversion to a Flow contention graph where each link is represented by a node and the contention by an edge between them. So now we need a coloring of the flow contention graph which would be the schedule solving the MAC problem, where the number of colors for a node proportional to the required capacity of the link. So, how do we analyze this problem ?? The idea is rather than find an exact value we need some kind of order bound ?? So we can at least say it is increasing or decreasing as a function of some variable ( number of nodes n )??

Background1 – what has been done, how, results ? Outline Introduction – what, why is it a hard problem ? Background1 – what has been done, how, results ? Recent Research – why, novelty, results ? 1. P.Gupta, P.R. Kumar, “Capacity of Wireless Networks,” IEEE Trans. Information Theory, Vol.46, March 2000.

Background overview Assumptions Nodes(n) location Xi : identical, independent uniform Unit area : sphere’s surface Homogenous nodes : rate r(n) , power P Destination : random independent Control : MAC, routing Here n nodes are assumed to be iid on the surface of a sphere. Thus it is a random network !! Surface of a sphere is assume to avoid the edge effects you would see on a plane – later it is shown that the same results hold for a plane too Identical nodes are assumed so it is typical of sensor networks …. So if the network is random what is under our control in the design – MAC and routing Network is a particular sample form the underlying distribution of nodes on the unit area. The network is random so is the capacity and hence capacity is a random variable – note the definition With this definition we shall now on call throughput capacity as capacity ( fro simplicity) note IT IS NOT SHANNON CAPACITY. Aim : provide order bounds on the capacity as a function of n . c>0 c’<infinity It is the time average bits/s per source destination pair.

Background overview Assumptions Metric Nodes(n) location Xi : identical, independent uniform Homogenous nodes : rate r(n), power P Unit area : sphere’s surface Destination : random independent Control : MAC, routing Metric Finding order bounds on the UNIFORM THROUGHPUT CAPACITY r(n) It is the time average bits/s per source destination pair. Here n nodes are assumed to be iid on the surface of a sphere. Thus it is a random network !! Surface of a sphere is assume to avoid the edge effects you would see on a plane – later it is shown that the same results hold for a plane too Identical nodes are assumed so it is typical of sensor networks …. So if the network is random what is under our control in the design – MAC and routing Network is a particular sample form the underlying distribution of node son the unit area. The network is random so is the capacity and hence capacity is a random variable – note the definition With this definition we shall now on call throughput capacity as capacity ( fro simplicity) note IT IS NOT SHANNON CAPACITY. Aim : provide order bounds on the capacity as a function of n . c>0 c’<infinity It is the time average bits/s per source destination pair.

Background communication model Wireless link : T bits/s Interference Model : Protocol : interference radius, guard zone of Transmissions is successful if every other receiver is out of interference range of the receiver Physical : propagation loss , If SNR > threshold, the receiver can receive packets Transmitter Simultaneous Transmitter The wireless link has w bits/s and so is a communication theoretic perspective Interference models tell us when a node Xk interferes with a transmission form node Xi to Xj The guard zone of delta works for the high SNR threshold case since then the interference region is greater than the transmission region and so 1+delta is valid. Also works when you have some threshold of SNR for operation and assuming a single interferer. Receiver

Background intuition r(n) is MAC vs Routing tr(n) - transmission range/power L – mean source-destination distance MAC vs Routing Reduces to Generates bits/s Available capacity Generated Traffic Hence Mean number of hops to destination Capacity loss due to interferences Here provide the intuition about how MAC and the Routing need to be traded off Only then can we justify the voronoi tessellation and the chosen sizes for uniform convergence. Let L be the mean source destination distance can be figured out right – a constant So now number of hops = l/ Tr trafiic generated is l* r/tr Each node has T ? No relay has T/tr^2 and so the tradeoff Upper bound is a limit on tr – above a threshold – else implies loss of connectivity

traffic to be carried available capacity Background capacity Lower bound (achievable) : use the a cellular like scheme traffic to be carried available capacity Upper bound : using transmission radius transmission radius lower bound for connectivity Order Bound : r(n) is Describe the bound briefly –

Background – what has been done, how, results ? Outline Introduction – what, why is it a hard problem ? Background – what has been done, how, results ? Recent Research – why, novelty, results ?

Recent Research overview Communication model : UWB (ultra wideband systems) limited power large bandwidth MAC : CDMA is optimal At least as good as any other algorithm in this communication model Routing : power constrained routing R. Negi and Arjunan – The Capacity of power constrained ad-hoc networks Note the difference an increasing function of n

Recent Research communication model power spectral density Power : constrained to P0 Medium loss Bandwidth: arbitrarily large W large w.r.t data rate – low spectral effeciency Examples : UWB (802.15.3a), sensors Link operates at Shannon Capacity Power and data rate adaptation according to the link condition Bandwidth scaling is one and the large is another As a function of n you will require bandwidth to make interference negligible This tells you growth rate as a function of n But to make it hold it needs to be large – how large – approximation of shannon capacity should hold This gives absolute values.

Recent Research PHY - interference SINR = Signal / (Noise + Interference) Noise = noise density * bandwidth In bandwidth-constrained scenario, SINR is dominated by interference In low spectral efficiency, SINR is mainly affected by ambient noise And also that Xi is transmitter and Xj is receiver and the rest are interferers

Recent Research PHY - interference Noise interference Bandwidth scaling : Makes W arbitrarily large  interference becomes negligible w.r.t ambient noise Interference negligible  no scheduling ! CDMA MAC is optimal, compared to FDMA/TDMA And also that Xi is transmitter and Xj is receiver and the rest are interferers

Recent Research PHY –link capacity What is the link capacity – The Shannon capacity for a link with Gaussian noise and interference sources: W log(1+SINR) Capacity is LINEAR in allotted power Rate/Capacity : adapts to link The link capacity is bounded due to the power constraint And also that Xi is transmitter and Xj is receiver and the rest are interferers

Recent Research routing Relaxed power constraint (upper bound) : routing can be decoupled from power constraint of each node Power on a route : Optimal Route : r(n) can be very large : each hop arbitrarily high capacity ! Number of hops is limited : hop length is bounded Note mention that the relaxed power constraint is assumed to derive an upper bound Also that this implies a power adaptation / control and since power and rate are connected a rate adaptation : results in route decoupling So basically each source destination attempts to use minimum power : resulting in minimum overall power for the network since no constraint on any node. Thus this solution maybe scaled for the appropriate maximum uniform throughput capacity.

Recent Research capacity Upper bound : relaxed power constraint Power is bounded by the average Number of hops (K) : bounded by tessellation Distance term : bounded by average source destination distance Lower bound : extend the previous scheme: traffic to be carried < available capacity Order bound : (soft order neglecting log terms) r(n) is Here in the bounding of the distance the problem is that Pessimistically – the number of nodes could be just one and so the distance is order 1^alpha - very bad for capacity. Optimistically – it could be all nodes and then the distance term is (1/n)^alpha * n too good for capacity So what do MOST or rather almost all routes actually see – calculate the number of nodes and bound it by the average distance between source and destination. IN these cases mostly the routes are all close to the average …………… We are designing for the worst case …to ensure that ever node will satisfy its minimum criterion that is why it is hard.

Capacity of UWB ad-hoc wireless networks Conclusion Capacity of UWB ad-hoc wireless networks Previous Result : bits/s Our result : bits/s under alternate Communication model (UWB) n is the node density distance loss exponent GAIN The gain comes two fold : 1. The infinite bandwidth washes away interference ( in the previous case basically washes away noise) in our case the uwb washes away interference. The second gain is that the large bandwidth gives us an explicit relationship between the capacity and power ie we exploit the fact that a node close by could be transmitting at high rate and so we need not be designing it for the farthest node like in the previous communication model Gives us the gain of \sqrt(n) ^alpha.

Information order bounds Intuitively f grows no faster than g Make a note as a function of naturals etc Basically growth rates Intuitively f,g grow at the same rate