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en:projects:master_semester:raj01 [2009/09/17 16:26] rkkrishn created |
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| ---- dataentry project ---- | ---- dataentry project ---- | ||
| - | title : Advanced decoding algorithms for MIMO systems | + | title : Lattice codes for Gaussian networks |
| contactname: Raj Kumar | contactname: Raj Kumar | ||
| contactmail_mail: raj.kumar@epfl.ch | contactmail_mail: raj.kumar@epfl.ch | ||
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| contactroom: BC 160 | contactroom: BC 160 | ||
| type : master semester | type : master semester | ||
| - | status : available | + | state : unavailable |
| created_dt : 2009-09-17 | created_dt : 2009-09-17 | ||
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| - | Wireless systems employing multiple antennas at both the transmitter and receiver (multiple-input multiple-output (MIMO) systems) have the potential to dramatically increase both the rates and reliability of transmission. MIMO systems are a part of the recently standardized IEEE 802.11n standard for Wireless LANs, and the IEEE 802.16 WiMax standard. | + | Determining the capacity of wireless networks remains a “holy grail” of multi-user Information theory, but several recent advances have been made in this regard. While most traditional achievability results in information theory have relied upon the tool of random coding, recent results have shown several interesting cases where structured codes such as linear and lattice codes outperform purely random codes. |
| - | A key challenge towards the widespread implementation of large MIMO systems is the development of low-complexity decoding algorithms. An optimal decoder for MIMO systems reduces to a closest lattice point search over a subset of a lattice, that is implemented using class of decoders known as “sphere decoders”. Recently, several low complexity versions of such algorithms (such as regularized lattice decoders and their lattice reduction aided linear counterparts) have been shown to be information theoretically optimal, at high signal-to-noise ratios (SNR). The aim of this project will be to: | + | The objective of this project is to: |
| - | * Understand sphere decoding algorithms for MIMO systems | + | * Understand the use of lattice codes for communicating over Gaussian networks |
| - | * Study and compare through simulations the performance of these techniques at practical values of SNR that are commonly encountered in actual systems | + | * Extend a recent result showing that lattice coding is optimal for a particular class of Gaussian relay networks to more general families of networks |
| - | * Use insights gained from such a study to try to improve these algorithms | + | * Explore connections with the recently proposed deterministic channel model |
| - | Prerequisites: | + | This project will be suitable to a student with a theoretical inclination. |
| - | * Knowledge of digital communication | + | Prerequisites: Solid background in Information Theory and Mathematics. |
| - | * Good mathematical aptitude | + | |
| - | * Knowledge of Matlab | + | |