Wireless beamforming techniques have been widely applied in wireless communications, such as WiMAX, IEEE LTE, IEEE 802.11n. These techniques allow modern wireless communication systems to realize increased range with higher antenna gain in the desired direction of communications and better connectivity. Simultaneously, the resulting narrower beamwidth and reduced interference increases the capacity and throughput offered by an antenna beamforming system.
Fading channels in wireless communications systems limit 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 antenna 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 antenna beamforming vector. This produces a transmission radiation pattern that is focused in directions 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 reliable knowledge of the channel.
The amount of information known about the channel determines which type of beamformer needs to be applied. Three scenarios are possible – 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 use by the transceivers. Techniques such as the Grassmannian line packing algorithm find a set of N beamforming vectors that has the maximum minimum distance between any pair of vectors. The codebook element that would maximize the channel gain is selected using the a priori probability distribution function that best describes the fading channel and real-time statistical information of the channel such as the mean and covariance.
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