Network simulations and tools. Dmitry Petrov magister.fi or jyu.fi
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1 Network simulations and tools Dmitry Petrov magister.fi or jyu.fi
2 How may networks be studied? Measurements from real devices / networks Measurements from real devices Protocol analyzers, i.e. sniffers Operator conducted drive tests with specialized equipment Networks / devices may not exist for state-of-the art wireless technologies Test networks Access to all equipment / software Open-source software Test networks may not be large enough Computer simulations Faster to achieve results Easier to analyze with different assumptions How reliable are the results? Mathematical analysis Pen n paper, Matlab, Mathematics, etc Magister Solutions Ltd
3 Magister Solutions From science into business In-house mobile software development R&D Training Analytics Mobile Applications Active Active Active New Educational, academic and expert networks Universities Corporate development EDU cluster VTT Magister Solutions Ltd
4 Essentially, all models are wrong, but some are useful [George E. P. Box and Norman R. Draper. Empirical Model-Building and Response Surfaces. Wiley, 1987] Magister Solutions Ltd
5 When to use simulations? When the analytical model/solution is not possible or feasible Many times, simulation results are used to verify analytical solutions in order to make sure that the system is modeled correctly using analytical approaches. Dynamic systems, which involve randomness and change of state with time Complex dynamic systems, which are so complex that when analyzed theoretically will require too many simplifications. In such cases, it is not possible to study the system and analyze it analytically Magister Solutions Ltd
6 What are simulations used for? Generally Test improvements and their gains To identify bottlenecks in a process To prevent under of over-utilization of resources Provide a safe and relatively cheaper (in term of both cost and time) test bed to evaluate the side effects Optimize the performance of the system Magister Solutions Ltd
7 Simulation scenarios With wrap-around radio conditions of the actual simulation area are replicated around the actual simulation area where UEs are located Non-regular layout Magister Solutions Ltd
8 R&D examples DC capable UE Multipoint Tx Spectrum 1 Spectrum 2 5 MHz 50 PA3 Channel (Softer Handover Users) 45 Mean Softer HO User BR Gain (%) Users per cell Magister Solutions Ltd
9 Tokyo example Goal: Performance benchamarking between 3G HSDPA and next generation LTE system Challenges: It is hard to collect needed statistics from commercial networks It is not affordable to build large enough test networks In relation to LTE, there was only limited commercial products available Magister Solutions Ltd
10 References Case Tokyo Simulation based approach was selected Like many vendors, operators as well as the scientific communities do for studying the wireless cellular network performance Digital network planning data over Tokyo map was used in the simulator Realistic conditions through non-regular network layout and propagation Magister Solutions Ltd
11 Available simulators Most suitable: Network Simulator-2 (NS-2) Network Simulator-3 (NS-3) OMNeT ++ openwns Other open JiST GoMoSim Proprietary OPNeT QualNet NetSim Company owned Magister Solutions Ltd
12 Simulator selection criteria Parameter NS-2 NS-3 OMNeT++ openwns License GNU GPLv2 GNU GPLv2 Free for academy and education LGPL Wireless technologies Support/develop ment Real time integration Platform C++ and OTcl C++ and Python C++ and NED Python with C++ libraries Performance Magister Solutions Ltd
13 NS 3 Organization Node class, NetDevice, Address types (Ipv4, MAC, ), Queues, Socket, Packet sockets routing node common Internet stack tests helper core devices mobility simulator applications Mobility models (static, random walk, etc.) Packets, Packet tags, Packet headers, Pcap/Ascii file writing Smart pointers, Dynamic type systems, Attributes, Callbacks, Tracing, Logging, Random Variables Events, Schedulers, Time arithmetic Magister Solutions Ltd
14 NS3: Main objects, attention to realism Socketslike API Node class Application class Protocol stack Packets Topology helpers Net device class Channel class Model nodes like real computer Support key interfaces such as sockets API Magister Solutions Ltd
15 Discrete-event simulation The idea is to jump from one event to another Events are recoded in a future event list (FEL) Each event notice is composed at least to data: time, type Event routine or handler to process each event Each event may change the system state and generate new event notices Magister Solutions Ltd
16 Simulator types Different ways of dividing simulators Running principle: discrete event, continuous, Level of detail: static, quasi-static, dynamic, Modeling purpose: wireless radio access, wired, Confidentiality: proprietary, open Magister Solutions Ltd
17 Simulation dynamics System simulators Static simulations Snapshot Quasi-Static Simulations Dynamic simulations Algorithms Simplified and limited algorithms, e.g no outer power control Extensively modelled RRM Fully modelled RRM Path Loss Fixed Fixed Varied every timeslot (slot, TTI, etc.) Time Domain No Yes Yes Mobility Static Static Moving randomly Computational Complexity Fast Middle Slow USE Cases Interference study between systems, etc. RRM study excluding mobility, etc. Mobility Study, RRM Study, etc Magister Solutions Ltd
18 Link and system level simulators (Mobile) network simulators can be roughly categorized in link level and system level simulators Link level simulators model in high detail the radio interface between a MT and BS. Link level simulators operate on chip or symbol level System level simulators model a full network usually including multiple BSs and number of mobile stations. Slot-level modeling is sufficient for system simulation (1 slot = 2560 chips = ms) Link level simulator output works as input for system level simulators using mapping tables or curves Magister Solutions Ltd
19 Example of link level curves Magister Solutions Ltd
20 Propagation, slow and fast fading Magister Solutions Ltd
21 Mobility models Random walk Random waypoint Magister Solutions Ltd
22 Traffic models Defined by standardization organizations Infinite buffer FTP Constant Bit Rate (CBR) HTTP models (bursty) AMR codecs for VoIP packet call packet call N d D pc (Reading Time) main object embedded objects Packet calls D pc Packets of file 1 Packets of file 2 Packets of file Magister Solutions Ltd
23 Metrics and performance indicators Cumulative distribution function (CDF) Line plots Bar plots Map plots Traces Magister Solutions Ltd
24 Generic architecture Parameters Scenarios Simulations...other Run/debug/visualise encoder decoder Results Post-processing tool Player (NetAnim, PyViz, ) Magister Solutions Ltd
25 Demos s Mesh network Directed antennas Energy models Energy + air-time routing LTE network
26 Thank you for attention!
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