Multi-cell coordinated distributed robust beamforming method

Abstract

The invention discloses a multi-cell coordinated distributed robust beamforming method. A central problem is converted into a form of multiple subproblems and a main problem through a dual decomposition theory, and the subproblems are converted into semi-definite programming (SDP) problems through the semi-definite relaxation (SDR) and S-procedure technology in a complex field and then solved. The main problem is solved through a sub-gradient algorithm, the lagrangian dual problems of respective subproblmes are solved to obtain the lagrangian multipliers and are exchanged through a return link at each base station, and the base station calculates the sub-gradient of the main problem at a disturbance item between cells according to the obtained multiplier information of other base stations, so that the main problem can be rapidly solved through the sub-gradient algorithm. The distributed algorithm is simulated and displayed through an actual cellular system, the performance close to the central algorithm can be achieved through a little iteration, and the algorithm is high in convergence rate when the channel error is low.

Description

technical field [0001] The present invention relates to the field of coordinated multi-point processing, in particular to beamforming, and in particular to a multi-cell cooperative distributed robust beamforming method. Background technique [0002] CoMP (Coordinated Multipoint Processing) technology is based on the different degrees of sharing of channel state information (CSI) and data information by each base station. Through the cooperation of base stations between cells, the interference that was originally adjacent cells is transformed into useful information. There are two transmission modes: 1. Joint Processing; 2. Coordinated Scheduling / Beamforming. Beamforming (BF) means that multiple cells share information and jointly design precoding vectors, so as to alleviate the interference received by users at the edge of the cell and achieve the effect of improving the signal-to-interference-noise ratio (SINR). [0003] According to the type of shared information, BF impl...

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Problems solved by technology

[0008] However, the above algorithm does not take into account the feedback error of CSI. In the actual system, accurate CSI cannot be obtained even through complex training sequences.

However, BF design using ideal CSI is very sensitive to channel errors, which may cause very serious performance loss and even cause users to be interrupted.

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