A Study of Lattice Reduction Detection Techniques for LTE Systems
(2016) EITM02 20132Department of Electrical and Information Technology
- Abstract
- MIMO (Multiple Transmit and Multiple Receive) antenna techniques are widely used in the most recent wireless communication standards. For example, LTE (Long-Term Evolution), WiMAX (Worldwide Interoperability for Microwave Ac- cess) and IEEE 802.11n have recently been rolled out across the world. In any communication system, the ML (Maximum Likelihood) receiver provides optimal error rate performance, but it turns out to be difficult to implement in system with 4 antennas or more.
This thesis studies ways to approach the optimal performance for MIMO chan- nels with M-QAM (4-QAM, 16-QAM and 64-QAM) in Rayleigh MIMO channels with low complexity. While linear detectors like ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) have very low... (More) - MIMO (Multiple Transmit and Multiple Receive) antenna techniques are widely used in the most recent wireless communication standards. For example, LTE (Long-Term Evolution), WiMAX (Worldwide Interoperability for Microwave Ac- cess) and IEEE 802.11n have recently been rolled out across the world. In any communication system, the ML (Maximum Likelihood) receiver provides optimal error rate performance, but it turns out to be difficult to implement in system with 4 antennas or more.
This thesis studies ways to approach the optimal performance for MIMO chan- nels with M-QAM (4-QAM, 16-QAM and 64-QAM) in Rayleigh MIMO channels with low complexity. While linear detectors like ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) have very low computational complexity, they suffer from noise enhancement and ISI (Inter Stream Interference) respectively.
LR (Lattice Reduction) coupled with ZF or MMSE will give us near optimal results close to that of the ML receiver. Same diversity order like ML is also found. Basically, LR is aiming to find a basis of the channel matrix as orthogonal as possible. A basis is composed by a set of linearly independent vectors. A big improvement can be achieved by replacing linear detectors with LR techniques.
Correlated channels will be studied for comparison reasons. To evaluate the impact of channel correlation, Kronecker Model with three different correlation factors will be discussed.
In future work section, some cutting edge algorithms will be mentioned, for example: BKZ (Block Korkine Zolotarev) Algorithm. (Less) - Popular Abstract
- One common knowledge is that the impact of wireless communication will be undisputed in the future. In modern society, wireless communication applications are spread out everywhere: ETC (Electronic Toll Collection) system, mobile phone data transmission and remote control system, wireless charging and MBAN (mobile body area networks) are commonly used nowadays.
Since our everyday life is inseparable with wireless communication, in modern neighborhoods, there are buildings, trees and moving vehicles influencing the transmitting signals, thus it’s been a focus of attention about how to improve wireless transmission into a more stable, reliable and faster transmission method, and it will always be in the future decades.
In this thesis,... (More) - One common knowledge is that the impact of wireless communication will be undisputed in the future. In modern society, wireless communication applications are spread out everywhere: ETC (Electronic Toll Collection) system, mobile phone data transmission and remote control system, wireless charging and MBAN (mobile body area networks) are commonly used nowadays.
Since our everyday life is inseparable with wireless communication, in modern neighborhoods, there are buildings, trees and moving vehicles influencing the transmitting signals, thus it’s been a focus of attention about how to improve wireless transmission into a more stable, reliable and faster transmission method, and it will always be in the future decades.
In this thesis, we will introduce LR (Lattice Reduction) algorithm combined with ZF and MMSE detection methods, for the purpose of getting a sub-optimal performance. The case that people get best signal connection with less errors is with ML (Maximum Likelihood) filter, however it consumes more time and is more complicated to implement. When people talk with their friends by phone in urban areas, it’s has high probability that they will lost connection with each other with ZF and MMSE methods. With the application of LR, which leads us to a more reliable transmission with less errors and less complexity.
With different number of transmit and receive antennas, we compared three different kinds of modulation methods and their performances in two different channel models: Rayleigh fading and LTE (Long Term Evolution) channels. With this LR method applied, you are able to share your life with your friends with less delay, meanwhile it’s less likely that the message will be sent to a wrong person.
Another leading technique of reduction BKZ can be studied to make your sharing experience as quick as flash, this reduction method is the best basis reduction algorithm in practice, which will definitely give us a more enjoyable wireless communication life. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8873221
- author
- Wang, Liping LU
- supervisor
- organization
- course
- EITM02 20132
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- LTE, MIMO, Lattice Reduction, MMSE, ZF, ML, Kronecker Model, Rayleigh Fading Channel
- report number
- LU/LTH-EIT 2016-497
- language
- English
- id
- 8873221
- date added to LUP
- 2016-05-19 14:02:05
- date last changed
- 2016-05-19 14:02:05
@misc{8873221,
abstract = {{MIMO (Multiple Transmit and Multiple Receive) antenna techniques are widely used in the most recent wireless communication standards. For example, LTE (Long-Term Evolution), WiMAX (Worldwide Interoperability for Microwave Ac- cess) and IEEE 802.11n have recently been rolled out across the world. In any communication system, the ML (Maximum Likelihood) receiver provides optimal error rate performance, but it turns out to be difficult to implement in system with 4 antennas or more.
This thesis studies ways to approach the optimal performance for MIMO chan- nels with M-QAM (4-QAM, 16-QAM and 64-QAM) in Rayleigh MIMO channels with low complexity. While linear detectors like ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) have very low computational complexity, they suffer from noise enhancement and ISI (Inter Stream Interference) respectively.
LR (Lattice Reduction) coupled with ZF or MMSE will give us near optimal results close to that of the ML receiver. Same diversity order like ML is also found. Basically, LR is aiming to find a basis of the channel matrix as orthogonal as possible. A basis is composed by a set of linearly independent vectors. A big improvement can be achieved by replacing linear detectors with LR techniques.
Correlated channels will be studied for comparison reasons. To evaluate the impact of channel correlation, Kronecker Model with three different correlation factors will be discussed.
In future work section, some cutting edge algorithms will be mentioned, for example: BKZ (Block Korkine Zolotarev) Algorithm.}},
author = {{Wang, Liping}},
language = {{eng}},
note = {{Student Paper}},
title = {{A Study of Lattice Reduction Detection Techniques for LTE Systems}},
year = {{2016}},
}