An efficient method to achieve high data-rate coverage in wireless communication is to use multiple antennas both at the transmitter and the receiver, since it makes it is possible to exploit the spatial degrees of freedom offered by multipath fading inside the wireless channel in order to provide a substantial increase in data rates and reliability of wireless transmission.
In the downlink, there are three basic approaches for utilizing the antenna: diversity, multiplexing and beamforming. With beamforming, the radiation pattern of the antennas may be controlled by transmitting a signal from a plurality of elements with an element specific gain and phase. In this way, radiation patterns with different pointing directions and beam widths in both elevation and azimuth directions may be created.
The gains from adjusting the beam shapes used for transmissions come from both increased received power (increased SNR) as well as a possibly lower interference (increased SINR) in a multi cell scenario. However, how much of these gains may be realized depends on how well the transmitting antenna system can direct the energy to the target users, and how well it avoids emitting energy to the interfered users.
The area of beamforming is usually divided in two parts, namely user specific beamforming (UE-BF) and cell specific beamforming (CS-BF). With user specific beamforming, the transmit beam used is chosen to optimize the channel between an eNB and a single user which is the method to use when transmitting user specific data. With CS-BF, beam are chosen to support all users within the cell, which is a method suitable for transmitting control information or other broadcast signals. Hence a cell-specific beam will generally cover a larger solid angle wider than a user specific beam.
In present wireless communication systems and frequency division duplexing FDD systems in particular, the user specific beamforming is typically implemented through the use of codebooks. There are both proprietary codebooks as well as standardized. When using codebook based transmissions, each user (which knows the codebook prior to transmission) may estimate what the gain would be for each code word and then feedback information of this to the eNB.
Cell specific beamforming, on the other hand, is standard transparent. Further, since the beams are supposed to suit all users within a cell, the best beam shape cannot be measured and optimized with a limited feedback from a few selected users. Therefore, one commonly assumed method to optimize cell specific beams is through the use of self-organizing network (SON) algorithms, sometimes called reconfigurable antenna system self-organizing networks (RAS-SON) algorithms. Such algorithms may typically measure some second order effect of changes in beam shapes, and optimize the beam shapes based on these. For example, one node may form some candidate cell specific beams, and then try these settings/beams in the network during a limited period of time, and evaluate which of these settings/beams that gives the best capacity or system throughput. This procedure is then repeated for various nodes/areas throughout the network to tune the overall setting and thus increase the overall network performance
These types of RAS-SON algorithms are blind/semi-blind and hence they become relatively slow (depending on the amount of time for which each setting is evaluated). This will particularly be the case when the beam shapes of multiple cells are to be improved, as is typically the case in cellular networks.
Cell specific beamforming, and specifically optimization of the cell specific beam shapes, is typically done to define and isolate the cells from each other. Well isolated cells facilities the UE to make a better choice of serving cell for communication.
Thus, current cell shaping methods are typically blind/semi blind in the sense that the antenna patterns at one or more sites are changed slightly, and then they are evaluated for some period of time. To avoid instability in systems this period has to be long enough to be statistically representative of the traffic situation. This results in slow algorithms.
Further, since arbitrary combinations of weights in an array to generate arbitrary beam shapes is far too large (for large arrays) to test all, only a smaller restricted subset is usually considered. Such beam shapes, for example fixed beam width and some certain tilt settings, may not be optimal for neither received signal nor interference suppression.