Pilot Scheduling Method Based on Mutual Pollution Index in Distributed MIMO System

Abstract

The invention discloses a pilot frequency scheduling method based on mutual pollution index in a distributed MIMO system. It is denoted as U, and the user antenna set that has not completed the pilot allocation is denoted as the pilot allocation situation and is denoted as {s k}, where s k Indicates the pilot sequence used by the kth antenna; 2. Select the antenna with the largest mutual pollution index with all user antennas, denoted as k 0 , assign orthogonal pilot sequences to k 0 , update the set {s k}, U and until the allocation of all orthogonal pilot sequences is completed; 3. Continue to select the antenna with the largest mutual pollution index with all user antennas in , denoted as k 0 , calculate k 0 and the mutual pollution index between the antenna sets using the same pilot in U, reuse the pilot sequence of the antenna set with the smallest mutual pollution index, and update the set {s k}, U and 4. Check whether it is an empty set, if yes, it means that all antennas have completed pilot scheduling, otherwise, go back to step 3.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to a pilot resource scheduling method in a wireless communication system, in particular to a pilot scheduling method based on a mutual pollution index in a distributed MIMO system. Background technique [0002] Distributed multiple input multiple output (Multiple Input Multiple Output, MIMO) system, as an antenna deployment method of massive MIMO system, is one of the key technologies to realize the fifth generation communication system (5G), which aims to achieve wider coverage of the system, More stable link and higher spectral efficiency and energy efficiency. As the name implies, the distributed MIMO system is to distribute the antennas of the original base station in the cell, that is, in the distributed MIMO system, the antenna units equipped with one or more antennas are scattered in the cell, and are received by optical fibers or dedicated radio frequency links....

AI Technical Summary

Problems solved by technology

Using the traversal algorithm to select the optimal scheme among all possible pilot allocation schemes can minimize the pilot pollution, but with the increase of the number of antennas, the complexity of the algorithm increases sharply, which does not have practical application value

Using the greedy algorithm, for each orthogonal pilot sequence, the antenna with better spatial dispersion is selected to reuse the pilot. The possibility of selecting an antenna with better spatial dispersion among the antennas will continue to decrease, that is, the user antenna allocated later will have a larger channel estimation error, and the performance is not ideal

Random allocation algorithm, that is, randomly select a certain number of user antennas to multiplex the same orthogonal pilot, its complexity is very low, but its performance is also extremely poor

Considering the complexity and performance comprehensively, there is an algorithm that tries to use different orthogonal pilot sequences for each antenna one by one, and selects the pilot with the smallest channel estimation error. The performance of this algorithm is better than the greedy algorithm and the random allocation algorithm. However, with The increase in the number of antennas, the computational complexity is still not very ideal

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