Randomization sampling lattice decoding method based on limited distance decoding

Abstract

The invention relates to a randomization sampling lattice decoding method based on limited distance decoding. The method comprises the steps of initializing settings, establishing an MIMO system lattice decoding model, conducting lattice reduction and QR decomposition on a channel matrix sequentially, conducting sampling decoding on received signals according to the depth-first search strategy to obtain candidate lattice points in a lattice reduction region, and decoded output. According to the decoding method, under the situation that the sampling frequency set initially is fixed, the decoding performance approaches the decoding performance of a randomization sampling decoding algorithm with the increasing of the length of a candidate lattice point list set initially and the increasing the of the number of nodes in a tree, and the performance the same as that of the randomization sampling decoding algorithm can be finally obtained with an access node number and a candidate lattice point number smaller than those of the randomization sampling decoding algorithm; the common breadth-first search strategy is replaced with the depth-first search strategy, the performance close to the optimum performance is obtained with a smaller search node number, complexity is reduced, and decoding efficiency is improved.

Description

technical field [0001] The invention belongs to the field of radio communication, and relates to a derandomized sampling lattice decoding method based on finite distance decoding. Background technique [0002] Multiple-input multiple-output (MIMO) systems with multiple antennas have been applied to modern wireless communications. The multi-antenna technology can break through the Shannon capacity limit under the single-input single-output system, and double the capacity of the communication system. A reliable and computationally efficient decoding algorithm is an important factor for optimal system performance. [0003] In MIMO systems, maximum likelihood (ML) decoding provides optimal error performance, but it suffers from exponentially increasing complexity with respect to the number of transmit antennas, so its complexity is too high to obtain Wide range of applications. In order to realize low-complexity decoding, linear decoding with low complexity is applied, such a...

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Problems solved by technology

[0008] In order to overcome the disadvantages of derandomized sampling decoding algorithm due to the increase in complexity caused by more search nodes, which affects the decoding efficiency, the present invention proposes a new derandomized sampling lattice decoding method for finite distance decoding. The depth-first search strategy achieves optimal performance with fewer search nodes, reduces decoding complexity, and improves decoding efficiency

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