Equalization techniques

Equalization techniques fall into two broad categories: linear and nonlinear. The linear techniques are generally the simplest to implement and to understand conceptually. However, linear equalization techniques typically suffer from more noise enhancement than nonlinear equalizers, and are therefore not used in most wireless applications.

EQUALIZATION

Equalizer design must typically balance ISI mitigation with noise enhancement, since both the signal and the noise pass through the equalizer, which can increase the noise power. Nonlinear equalizers suffer less from noise enhancement than linear equalizers, but typically entail higher compensation to mitigate the resulting ISI. Since the wireless channel varies over time, the equalizer must learn the frequency or impulse response of the channel (training) and then update its estimate of the frequency response as the channel changes (tracking). The process of equalizer training and tracking is often referred to as adaptive equalization, since the equalizer adapts to the changing channel. Equalizer training and tracking can be quite difficult if the channel is changing rapidly.

Evolution of Cellular System

Multiple Input Multiple Output (MIMO)

Over the past few years, it has been shown that using multiple antennas can significantly increase the capacity and robustness of communication systems in fading environments. Capacity grows with the number of antennas used. Approximately twice the amount of information can be communicated using two transmit antennas and two receive antennas, without spending any extra time, bandwidth, nor power.

The multiple input multiple output (MIMO) scheme deploys multiple antennas at the transmitter or/and at the receiver. The data stream from a single user is demultiplexed and fed into the respective transmitting antennas all of which radiate in the same frequency band. By sharing the same frequency band the spectral efficiency is improved. The receiver is assumed to have ideal channel estimates so it can separate and decode the symbols transmitted from each antenna. The ability to separate out the symbols is due to the fact that in a scattering environment, the signals received at each receiving antenna from each transmitting antenna appear to be uncorrelated.

Wireless!!!!!!!

Wireless communications is, by any measure, the fastest growing segment of the communications industry. Cellular systems have experienced exponential growth over the last decade and there are currently around two billion users worldwide. Indeed, cellular phones have become a critical business tool and part of everyday life in most developed countries, and are rapidly supplanting antiquated wireline systems in many developing countries. In addition, wireless local area networks currently supplement or replace wired networks in many homes, businesses, and campuses. Many new applications, including wireless sensor networks, automated highways and factories, smart homes and appliances, and remote telemedicine, are emerging from research ideas to concrete systems. The explosive growth of wireless systems coupled with the proliferation of laptop and palmtop computers indicate a bright future for wireless networks, both as stand-alone systems and as part of the larger networking infrastructure. However, many technical challenges remain in designing robust wireless networks that deliver the performance necessary to support emerging applications.