| Título: | BER Performance Analysis of MIMO Systems Using Equalization Techniques |
| Autores: |
Gupta, Rohit Grover, Amit |
| Fecha: | 2012-11-01 |
| Publicador: | Innovative systemas design and engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article Peer-reviewed Article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | The mobile data applications has increased the demand for wireless communication systems offering high throughput, wide coverage, and improved reliability. The main challenges in the design of wireless communication systems are the limited resources, such as constrained transmission power, scarce frequency bandwidth, and limited implementation complexity—and the impairments of the wireless channels, including noise, interference, and fading effects. Multiple-Input Multiple-Output (MIMO) communication has been shown to be one of the most promising emerging wireless technologies that can efficiently boost the data transmission rate, improve system coverage, and enhance link reliability. By employing multiple antennas at transmitter and receiver sides, MIMO techniques enable a new dimension – the spatial dimension – that can be utilized in different ways to combat the impairments of wireless channels. This article focuses on Equalization techniques, for Rayleigh Flat fading channel. Equalization is a well known technique for combating intersymbol interference; moreover equalization is the filtering approach which minimizes the error between actual output and desired output by continuous updating its filter coefficients. In this paper, different equalization techniques are investigated for the analysis of BER in MIMO Systems. In this article we have discussed different types of equalizer like ZF, MMSE, ZF-SIC, MMSE-SIC, ML and Sphere decoder. The results are decoded using the ZF, MMSE, ZF-SIC, MMSE-SIC, ML and Sphere decoder (SD) technique. The successive interference methods outperform the ZF and MMSE however their complexity is higher due to iterative nature of the algorithms. ML provides the better performance in comparison to others. Sphere decoder provides the best performance and the highest decoding complexity as compare to ML. We can clearly observe that Sphere decoder gives us high performance in comparison to ML, MMSE-SC, ZF-SIC, MMSE and ZF. Keywords: Quadrature Amplitude Modulation (QAM), Quadrature Phase Shift Key (QPSK), Binary Phase Shift Key (BPSK), Minimum mean-squared error (MMSE), Maximum likelihood (ML),Bit error rate (BER), Independent identical distributed (i.i.d. ), Intersymbol interference (ISI). Successive interference cancellation (SIC), Sphere Decoder (SD), zero Forcing (ZF). |
| Idioma: | Inglés |