Introduction
The deployment of multiple antennas in wireless communication systems is a promising method for the future high data rate wireless systems. It is shown that without increasing the total power consumption, this method bring the possibility to reach very high bandwidth efficiency compared to single antenna systems for a Rayleigh fading channel; where the transmission medium is enriched with scatterers .
In a MIMO system, multiple element antenna arrays are deployed at both the transmitter and the receiver. In such systems, the transmitting data stream is first demultiplexed into parallel data sub-streams and then transmitted by each antenna element. In these systems, there exist a separate path between each transmit-receive antenna pairs, where in a rich scattering environment, these channels can be considered statistically independent. To detect the transmitted signal at the receiver, the knowledge of these channels is necessary. Different estimation methods can be used to estimate the channel matrix at the receiver. These methods are mainly training based methods, superimposed raining methods, and the methods that estimate the channel matrix blindly.
However, the presence of noise and interference, outdated data (especially when the channel is changing very fast), and quantization error are sources of channel estimation inaccuracy. In the sense of symbol error rate (SER), the optimum detection method in MIMO system receivers is the maximum likelihood (ML) detector. ML performance can be obtained with lower computational complexity using the sphere detection (SD) algorithm almost in many cases of interest. Practically, the inevitable imperfect information about the channel increases the SER and corrupts the system performance. In this project,a briefly study the effect of channel estimation error on the performance of sphere detection method, where we analytically propose two modified and robust methods to cope with the destructive effect of channel errors on the performance of sphere detector.
The deployment of multiple antennas in wireless communication systems is a promising method for the future high data rate wireless systems. It is shown that without increasing the total power consumption, this method bring the possibility to reach very high bandwidth efficiency compared to single antenna systems for a Rayleigh fading channel; where the transmission medium is enriched with scatterers .
In a MIMO system, multiple element antenna arrays are deployed at both the transmitter and the receiver. In such systems, the transmitting data stream is first demultiplexed into parallel data sub-streams and then transmitted by each antenna element. In these systems, there exist a separate path between each transmit-receive antenna pairs, where in a rich scattering environment, these channels can be considered statistically independent. To detect the transmitted signal at the receiver, the knowledge of these channels is necessary. Different estimation methods can be used to estimate the channel matrix at the receiver. These methods are mainly training based methods, superimposed raining methods, and the methods that estimate the channel matrix blindly.
However, the presence of noise and interference, outdated data (especially when the channel is changing very fast), and quantization error are sources of channel estimation inaccuracy. In the sense of symbol error rate (SER), the optimum detection method in MIMO system receivers is the maximum likelihood (ML) detector. ML performance can be obtained with lower computational complexity using the sphere detection (SD) algorithm almost in many cases of interest. Practically, the inevitable imperfect information about the channel increases the SER and corrupts the system performance. In this project,a briefly study the effect of channel estimation error on the performance of sphere detection method, where we analytically propose two modified and robust methods to cope with the destructive effect of channel errors on the performance of sphere detector.
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