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Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703

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1 Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703
Network Design 1 May 2008 Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703

2 Briefing Outline Background on Networks Network Design Issues
STCD Network Design Program 3/27/201710/21/04

3 Distribution Statement
This briefing has been previously cleared for public release. The comments recommendations and conclusions are solely those of the author, and not of the Army or DOD. 3/27/201710/21/04

4 Background on Networks

5 Mobile Ad Hoc Networks (MANETs) vs Commercial Networks
Mobile Ad Hoc Networks (MANETs) do not have a fixed infrastructure or backbone MANET connectivity is determined by node location, mobility, and RF propagation characteristics. Commercial Networks ( Digital Cellular ) have fixed infrastructure backbone of Base Stations and Cell Towers that are interconnected thru high bandwidth Fiber Optic cable. In Digital Cellular, only the “last hop” is wireless. Other Commercial wireless networks ( IEEE 802.xx ) are usually static in nature. The MANET model of networks, with extensions for multi-tier operation, is most applicable to Army Networks such as FCS and WIN-T. 3/27/201710/21/04

6 MANET Networks User Traffic and RF connectivity are in fact probabilistic and stochastic quantities User node mobility is also a stochastic quantity Node interconnection, as determined by RF propagation, leads to a time dependent, stochastic topology graph Classic Graph Theory has limited application for MANETS due to the inability of represent link to link coupling and node mobility explicitly. Hence the mathematics of networks must include both a stochastic nature as well as a combinatoric nature In WIRELESS MANETS, both the Network and the Traffic loads are stochastic in nature In WIRED Networks typically only the Traffic is stochastic Hence MANET Network Design is a HARD problem!! 3/27/201710/21/04

7 Network Analysis vs. Network Design (Synthesis)
The Analysis Problem: Given a network solution perform an performance analysis typically for User Requirements for throughput, delay, reliability Network survivability Network connectivity The Design Problem: Design is the Synthesis Problem Given what you want ( as given requirements), how do you go about creating the network solution that meets (or exceeds the requirements? This is a creative and inventive process 3/27/201710/21/04

8 Network Design Issues

9 Specific Network Design Issues
Design metrics for MANETs Connectivity ( in mobile ad hoc environment ) ETE User Requirements Survivability ( in mobile ad hoc environment ) “Optimality” or goodness of design to be used in comparisons of different designs RF Connectivity representation ( including mobility ) Steady state vs transients in network operation Closed loop vs open loop for adaptive/dynamic algorithms Scalability ( to arbitrary network sizes ) Multi-tiered Operation Widely differing RF characteristics link highly dispersed users Design solution should be “insensitive” to traffic loads, operational scenarios Typically requirements are uncertain and/or subject to change 3/27/201710/21/04

10 Specific Network Design Issues (Concluded)
Security “Optimality” in the face of uncertain requirements, scenarios, and RF environments RF Waveform Design for Jamming Environment “Validation” issues Does the design meet/exceed requirements Typically requires extensive testing “Verification” Issues Does the design have “good properties” that are common to all designs COST, COST, COST 3/27/201710/21/04

11 Network Analysis / Design Techniques
Analytic Modeling Define/develop mathematical equations for network behavior – connectivity, capacity, thruput/goodput, delay, survivability, etc Very difficult to get good closed form solutions A 6.1 and academic focus Simulation Develop Discrete Time Event Driven simulation for the Network Execute simulation over range of traffic loads, topologies, mobility patterns, failure/destruction patterns Requires a large number of runs to get solutions OPNET, Qualnet, etc 3/27/201710/21/04

12 Network Analysis/Design Techniques ( Concluded )
Prototyping Build a solution, make measurements on it. Collect a large amount of data and then use data driven modeling techniques to define necessary relationships Rutgers WIN-LAB ORBIT is good example Emulation Build a scaled or abstracted version of the network solution make measurements and develop data driven relationships 3/27/201710/21/04

13 Mathematics Potentially Applicable to Network Design and Analysis
Graph Theory Topology representation and analysis – but not sufficient for MANET Closed Loop control theory Assumes a “network operating point” that is to be maintained Need to add delay/ loss model to control model Stochastic Processes “Random” or statistical nature of traffic load, node mobility, and RF channel, Discrete Time Markov Processes, Fractals, Jackson Networks. BCMP Networks Discrete Event Transition Models Finite State machines, Petri-Nets, etc To represent control interaction with system state Optimization Theory as applied to Network Design Primal/Dual, hill climbing, annealing, Genetic Algorithms, Robust Optimization, Stochastic Optimization VERY MESSY INDEED 3/27/201710/21/04

14 Overall MANET Network Design Elements
The protocol/stack design individual protocols cross layer issues protocol parameters/configuration The “node” design Radio hardware design Antenna Modifications Waveform Modifications The Recommended Network Architecture Relay function for multi-hop networks Addition relay (like UAV or UGV) capability may be needed to achieve network goals of connectivity, capacity, and survivability Domain sizing and organization Interconnection points 3/27/201710/21/04

15 Cross Layer Design Issues (Goldsmith, et. al.)
Multiple Antennas Coding/Modulation Power Control Adaptive Link Techniques for Power Control, Scheduling, and link selection Neighbor selection / maintenance Delay/energy constrained routing 3/27/201710/21/04

16 Network Layer Design Issues
Network Initialization Time (Cold Start) Node Join Time To Existing Network Group Node Join Time Node Leave Time From Existing Network Group Node Leave Time Network Recovery Time ( after node / link failure ) Network Overhead ( packets/sec ) Processing Resource Requirements ( CPU cycles/memory) QOS/Data Handling 3/27/201710/21/04

17 Application Layer Issues
ETE delay, throughput/goodput, packet loss Reliability/Connection Management QOS/priority 3/27/201710/21/04

18 STCD Network Design Program

19 Background on Engineering Network Design Tools
Design Tools exist for wired backbone networks, such as Digital Cellular and POTS. Some design tools exist for IEEE802.xx type networks, but are typically limited only to connectivity determination and node placement. Some design Tools exist for Satellite Networks but are limited primarily to up/down link design. 3/27/201710/21/04

20 Why a Network Design Toolset?
Due to network complexity, many relationships may be required to accurately describe network behavior These relationships will need to be coupled and use in a coordinated fashion to produce a complete network design Tools/Toolsets have be used successfully in many other areas: Cell phone network design Design of Boeing 777 VLSI circuit design Layout of internal spaces on submarines But not ad hoc network design ( yet!) 3/27/201710/21/04

21 Network Design vs Network Planning
Used by Military user in Operational environment to plan deployment of existing network assets to meet operational needs in theater. Assumes that Network hardware/software is fixed and designed. Network Design Used by Engineering community protocol/stack design, node design, and network architecture provides rapid design/trade offs of design options validated thru detailed OPNET ( or equivalent ) simulations, prototyping, or experimentation 3/27/201710/21/04

22 Approaches to Network Design
Analytic Approach Human must create, discover or invent mathematical relationships/heuristics Many assumptions required for mathematical tractability Typically, one function at a time is all that can be represented mathematically ; hence many coupled relationships may be required to completely describe ad hoc networks In many areas, these analytic relationships do not exist Discrete Time Event Driven Simulation Approach The human must do the design, and the simulation does the computation; The human does the analysis of simulation results Very detailed, fine grained simulations are possible Specific solutions to specific input sets (a big calculator) Many runs needed to vary parameters and get statistically valid results Little to no insight as to network behavior or “what effects what” Prototyping / experimental testing Approach Must have a network built first Instrumentation issues to get accurate measurements Much data can be collected --> challenging data analysis SHOULD BE DONE TOGETHER IN CONSISTENT FASHION to achieve good network design 3/27/201710/21/04

23 Network Engineering Design Analytic Toolset (NEDAT)
Network output - What can the network do? Network input goals/ranges. NEDAT INPUTS NEDAT OUTPUTS NUMBER OF NET NODES LOCATION OF NET NODES RANGE OF XMIT POWER RF REPRESENTATIONS RANGE OF RF LINK RATES NET CONNECTIVITY NET END TO END CAPACITY NET SURVIVABILITY GENERIC PROTOCOLS MOBILITY PATTERNS NEDAT Development Approach “Build a little/test a little” Start with small numbers of nodes (~15) Increase number of nodes (~100 , ~500, ~1000) Add more refined behavior as Network Science results become available. ADDITIONAL NODES AT LOCATIONS NEEDED?? ACTUAL XMIT POWER ACTUAL DATA RATES PROTOCOL STACK REQUIRED PARAMETERS Tool Development Approach - “Build a little/test a little” NEDAT will produce a network design Start with small numbers of nodes (~15) Increase number of nodes (~100 , ~500, ~1000) and possibly add more refined behavior as Network Science result become available. 6.1 RESEARCH NETWORK DESIGN EQUATIONS/RELATIONSHIPS 3/27/201710/21/04

24 Network Engineering Design Analytic Toolset (NEDAT)
Detailed OPNET Simulation used to “validate” Network Design Produced by NEDAT Scenario/Traffic Load Connectivity, Capacity, Survivability Discrete Time Event driven OPNET Simulation Network Design High Level Node Representation NEDAT Goals Met? No Design Equations Heuristics From Network Science Ideas for Refinement Yes Recommend Lab/Field Experimentation 3/27/201710/21/04

25 What is Cognitive Networking (CN)?
Cognitive Networks (CN) are characterized by advanced hardware and software that Interacts proactively with the environment (RF, traffic load, mobility, mission profile, etc) Uses learning, knowledge representation, estimation, predictive and optimization techniques for (near) real time network control (i.e. protocols), and network and spectrum management Provides enhanced performance (user thruput, delay, loss, survivability) and spectral efficiency over “conventional” techniques Conventional techniques use fixed algorithms with variable parameters, while Cognitive Networks use variable algorithms with variable parameters in stable, non-oscillatory, adaptive fashion (MILITARY) Cognitive Networking is the application of the above techniques to the MANET Technology 3/27/201710/21/04

26 Cognitive Networking vs “Traditional” Networking
Cognitive Networking will solve the well known MANET networking problems in a “different” and better way when compared to “traditional” approaches Interact, learn, and respond to networking environment Algorithms change their computation logic (based on the environment) as well as usual algorithm parameters Provide less overhead thru the use of advanced techniques (i.e. compute, don’t communicate) such as predictive, estimation, local reasoning, etc. The Networking problems to be addressed remain the same Network Ops, Control/Management Protocols / Cross layer stack design Security Multi-tiered Network Transport 3/27/201710/21/04

27 Why Cognitive Networking Design (CND) is hard?
Mobile Ad Hoc Networking design is hard in general; the addition of cognitive capabilities increase the design space/options Many options and alternatives need to be examined and explored Traditional simulation (i.e. OPNET) approaches have limitations regarding model development, fidelity, scalability and are actually analysis and not design tools Many of learning, estimation, reasoning, optimization and other techniques have not been applied to the highly mobile, large scale, distributed, dynamic ad hoc networking for the hostile battlefield environment. 3/27/201710/21/04

28 Why a CN Design Tool? MANETs in general are hard to design due to the large design space Using Cognitive Networking technology only increases the design space options and adds complexity to the process with the expectation of enhanced performance and efficiency of operation. Using a modeling approach to present both cognitive and non-cognitive functions in an engineering tool environment will allow rapid and effective exploration of a large number of design options and alternatives. Using a modeling approach, critical issues such as scalability, performance, and behaviors may be explored and investigated at minimal cost without committing to large amounts of physical hardware or expensive testing 3/27/201710/21/04

29 Cognitive Network Design (CND) Considerations
Design metrics for MANETs Connectivity ( in mobile ad hoc environment ) End-to-end User Requirements Survivability ( in mobile ad hoc environment ) “Optimality” or goodness of design to be used in comparisons of different designs Knowledge oriented representation of RF connectivity (including mobility), network operation/behaviors Effectiveness of learning/prediction techniques in dynamic environment Steady state vs. transients in network operation (stability issues) Design solution should be “insensitive” to traffic loads, operational scenarios/requirements and environments Typically these are uncertain and/or subject to change 10/21/04 3/27/201710/21/04

30 CN Design Tool Software Functional Architecture
Knowledge Database Prediction/Estimation Learning Functional Model Repository Analytic Tool-box Input Module Design Manager Output Module ??? DES Adaptor External DES 3/27/201710/21/04

31 The Payoff of Cognitive Networking
The ability to say, with a high level of confidence that the “network” will work in the military dynamic environment A potential to perform cost-performance trade offs for various cognitive network design through analysis for large military ad-hoc networks A new capability to “optimize the design” for performance functionality, and capabilities of mobile ad-hoc networks A better understanding of complex network behaviors New capability to optimize engineering design of large, multi-tiered, highly mobile, ad hoc networks 10/21/04 3/27/201710/21/04 31

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