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Fading channels in wireless communications systems limits the abilities of a Single-Input Single-Output system. Mutiple-Input Multiple Output (MIMO) systems use the spatial dimension to increase resistance to channel fading, thus significantly increasing the throughput of channel.
MIMO signal processing techniques such as beamforming actually use the fading channels to their advantage. For example, in multi-path fading channels the non-line-of-sight paths degrade the signal since the receiver would receive multiple copies of a signal with variation in time and strength. By taking the eigen-decomposition of the channel correlation matrix, the resulting eigenvector can be used as the beamforming vector. This produces a transmission radiation pattern that is focused in directions of that would lead to the most reliable communication, while avoiding radiating in paths that would cause signal interference.
In order, to achieve this successfully, one would need to reliable knowledge of the channel. Based on the amount of information known about the channel, determines which type of beamforming needs to be applied. The three scenarios are full channel state information (CSI), limited CSI and no CSI. For full CSI, statistical eigen-beamforming is a reliable and commonly implemented solution. For limited or quantized CSI, techniques such as Grassmannian or interpolation beamforming are typically used, and for no CSI, blind beamforming techniques are used, in which the channel state information is blindly estimated from the received signal statistics.
For perfect channel state information, the channel can be effectively cancelled, while for limited channel state only the statistics of the channel from training sequences are known. Also in systems where the hardware performance is limited, transceivers would be unable to adaptively calculate the optimal beamforming vector. Therefore, in some wireless systems codebooks are generated for used by the transceivers. Techniques such as the Grassmannian line packing algorithm finds a set N beamforming vectors that has the maximum minimum distance between any pair of vectors. Using a probability distribution function that best describes the fading channel (this is selected beforehand), and real-time statistical information of the channel, like the mean and covariance; the codebook element that would maximize the channel gain is selected.
Beamforming techniques allow the wireless communication system to realize increased range with higher antenna gain in the desired direction of communications and better connectivity. Simultaneously, the narrower beamwidth and reduced interference increases the capacity and throughput offered by the system.Therefore beamforming techniques have been widely applied in modern wireless communications, such as WiMAX, IEEE LTE, IEEE 802.11n.
For information about wireless beamforming applications, check out the following links: