Efficient Space-Time Block Codes Designed by a Genetic Algorithm Don Torrieri U.S. Army Research Laboratory Matthew C. Valenti West Virginia University.

Slides:

Advertisements

Similar presentations

Logical and Artificial Intelligence in Games Lecture 14

Advertisements

Genetic Programming Dan Kiely Ran Shoham Brent Heigold

CS6800 Advanced Theory of Computation

Exact and heuristics algorithms

The Impact of Channel Estimation Errors on Space-Time Block Codes Presentation for Virginia Tech Symposium on Wireless Personal Communications M. C. Valenti.

A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIME TABLING PROBLEM SUMALATHA.

Tuesday, May 14 Genetic Algorithms Handouts: Lecture Notes Question: when should there be an additional review session?

Genetic Algorithms Representation of Candidate Solutions GAs on primarily two types of representations: –Binary-Coded –Real-Coded Binary-Coded GAs must.

Genetic Algorithms: Evolving Solutions to Problems.

Non-Linear Problems General approach. Non-linear Optimization Many objective functions, tend to be non-linear. Design problems for which the objective.

COMP305. Part II. Genetic Algorithms. Genetic Algorithms.

Evolutionary Computational Intelligence

Data Mining CS 341, Spring 2007 Genetic Algorithm.

STBC BASED STTC CODES OPTIMIZED DESIGNS FOR THREE TRANSMIT ANTENNA SYSTEMS CANCES Jean-Pierre XLIM - Dépt. C 2 S 2 UMR CNRS 6172 ENSIL Parc ESTER - BP.

Population New Population Selection Crossover and Mutation Insert When the new population is full repeat Generational Algorithm.

Genetic Algorithms GAs are one of the most powerful and applicable search methods available GA originally developed by John Holland (1975) Inspired by.

1 Genetic Algorithms. CS The Traditional Approach Ask an expert Adapt existing designs Trial and error.

Learning to Advertise. Introduction Advertising on the Internet = $$$ –Especially search advertising and web page advertising Problem: –Selecting ads.

Genetic Art. Network Record Using Network Information Chicago Evanston ParisMoscow.

Crossover Operation with Different Parents Crossover Operation with Identical Parents.

Local Search and Stochastic Algorithms

1 Genetic Algorithms. CS 561, Session 26 2 The Traditional Approach Ask an expert Adapt existing designs Trial and error.

Space Time Block Codes Poornima Nookala.

Genetic Programming. Agenda What is Genetic Programming? Background/History. Why Genetic Programming? How Genetic Principles are Applied. Examples of.

Genetic Programming.

Slides are based on Negnevitsky, Pearson Education, Lecture 10 Evolutionary Computation: Evolution strategies and genetic programming n Evolution.

Travelling Salesman Problem: Convergence Properties of Optimization Algorithms Group 2 Zachary Estrada Chandini Jain Jonathan Lai.

Problems Premature Convergence Lack of genetic diversity Selection noise or variance Destructive effects of genetic operators Cloning Introns and Bloat.

Soft Computing Lecture 18 Foundations of genetic algorithms (GA). Using of GA.

© Negnevitsky, Pearson Education, Lecture 10 Evolutionary Computation: Evolution strategies and genetic programming Evolution strategies Evolution.

Genetic Algorithms Michael J. Watts

Genetic Algorithms Genetic algorithms imitate a natural optimization process: natural selection in evolution. Developed by John Holland at the University.

Arindam K. Das CIA Lab University of Washington Seattle, WA MINIMUM POWER BROADCAST IN WIRELESS NETWORKS.

More on Heuristics Genetic Algorithms (GA) Terminology Chromosome –candidate solution - {x 1, x 2,...., x n } Gene –variable - x j Allele –numerical.

Presented by: Sohaib Malik.  A radio whose functionality can be changed by changes in only the software  Key feature: ◦ Reprogramability ◦ Reusability.

Construction of Substitution Matrices

MIMO-OFDM Wireless Communications with MATLAB ® Yong Soo Cho | Jaekwon Kim Won Young Yang | Chung G. Kang Chapter 10. Antenna Diversity and Space- Time.

Evolutionary Art with Multiple Expression Programming By Quentin Freeman.

Coded Modulation for Multiple Antennas over Fading Channels

Smooth Side-Match Classified Vector Quantizer with Variable Block Size IEEE Transaction on image processing, VOL. 10, NO. 5, MAY 2001 Department of Applied.

Doc.: IEEE /1401r0 Submission November 2014 Slide 1 Shiwen He ， Haiming Wang Quasi-Orthogonal STBC for SC-PHY in IEEE aj (45GHz) Authors/contributors:

1 Genetic Algorithms K.Ganesh Introduction GAs and Simulated Annealing The Biology of Genetics The Logic of Genetic Programmes Demo Summary.

Chapter 9 Genetic Algorithms.  Based upon biological evolution  Generate successor hypothesis based upon repeated mutations  Acts as a randomized parallel.

Quantifying Population Extinction and Examining the Effects of Different Mutation Rates Jason Stredwick Farshad Samimi Wei Huang Matt Luciw Matthew Rupp.

 Genetic Algorithms  A class of evolutionary algorithms  Efficiently solves optimization tasks  Potential Applications in many fields  Challenges.

Genetic Algorithms What is a GA Terms and definitions Basic algorithm.

ECE 103 Engineering Programming Chapter 52 Generic Algorithm Herbert G. Mayer, PSU CS Status 6/4/2014 Initial content copied verbatim from ECE 103 material.

Genetic Algorithms. The Basic Genetic Algorithm 1.[Start] Generate random population of n chromosomes (suitable solutions for the problem) 2.[Fitness]

Space Time Codes. 2 Attenuation in Wireless Channels Path loss: Signals attenuate due to distance Shadowing loss : absorption of radio waves by scattering.

D Nagesh Kumar, IIScOptimization Methods: M8L5 1 Advanced Topics in Optimization Evolutionary Algorithms for Optimization and Search.

Doc.: aj Submission November 2014 Slide 1 Shiwen He ， Haiming Wang Quasi-Orthogonal STBC for IEEE aj （ 45GHz ） Authors/contributors:

Genetic Algorithms. Underlying Concept  Charles Darwin outlined the principle of natural selection.  Natural Selection is the process by which evolution.

Agenda  INTRODUCTION  GENETIC ALGORITHMS  GENETIC ALGORITHMS FOR EXPLORING QUERY SPACE  SYSTEM ARCHITECTURE  THE EFFECT OF DIFFERENT MUTATION RATES.

Genetic Algorithms. Overview “A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining.

Genetic Programming Using Simulated Natural Selection to Automatically Write Programs.

10/19/20051 Turbo-NFSK: Iterative Estimation, Noncoherent Demodulation, and Decoding for Fast Fading Channels Shi Cheng and Matthew C. Valenti West Virginia.

Genetic Algorithms. Solution Search in Problem Space.

Breeding Swarms: A GA/PSO Hybrid 簡明昌 Author and Source Author: Matthew Settles and Terence Soule Source: GECCO 2005, p How to get: (\\nclab.csie.nctu.edu.tw\Repository\Journals-

Genetic Algorithm(GA)

Genetic Algorithm. Outline Motivation Genetic algorithms An illustrative example Hypothesis space search.

Genetic Algorithms and Evolutionary Programming A Brief Overview.

 Presented By: Abdul Aziz Ghazi  Roll No:  Presented to: Sir Harris.

Hirophysics.com The Genetic Algorithm vs. Simulated Annealing Charles Barnes PHY 327.

Genetic Algorithm (Knapsack Problem)

Using GA’s to Solve Problems

Selected Topics in CI I Genetic Programming Dr. Widodo Budiharto 2014.

Space Time Codes.

Set 3 Genetics Vocabulary.

Numerical Algorithms Quiz questions

Presentation transcript:

Efficient Space-Time Block Codes Designed by a Genetic Algorithm Don Torrieri U.S. Army Research Laboratory Matthew C. Valenti West Virginia University

Space-Time Block Codes Orthogonal STBC provides full diversity at full rate and linear ML decoding but exists only for 2 antennas. Some STBCs preserve full diversity and full rate but have more complex decoding. STBC may be evolved to have full rate decoupled decoding at cost of diversity.

STBC

Generator Matrix

MDC-QO (4, 4, 4) STBC

Dispersion Matrices

Orthogonality Condition

Orthogonality Requirements

Cost function

Genetic Algorithm String of genes specifies the entries of dispersion matrices of particular STBC Parents breed children Genes of child are identical to a parent except at randomly chosen crossover positions, and mutations are generated Selection entails replacement of parent or culling of least fit Cloning and immigration moves genes from one pool to another

Parent Selection Random selection Preferred parenting Eugenic selection Alpha-male parenting

Cost vs. generation

(4, 3, 4) codes & QPSK

(6, 3, 6) codes & QPSK

SE = 3bits/s/Hz, 3 antennas

SE = 3bits/s/Hz, 4 antennas

Turbo-coded Performance

Conclusions Genetic algorithm produces STBCs optimized for decoupled decoding. When spectral efficiency is specified, outer code is used, and fading is severe, evolved codes outperform orthogonal STBCs. Alpha-male parenting and parallel execution using cloning and immigration expedite evolution.