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User scheduling and analog beam selection optimization method based on whale algorithm

A technology for simulating beams and user scheduling, which is applied in transmission monitoring, advanced technology, energy consumption reduction, etc. It can solve the problems of high dependence on the initial iteration value and high computational complexity, and achieve fast convergence, low complexity, and improved performance. Effect

Active Publication Date: 2021-11-26
GUANGZHOU UNIVERSITY
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Problems solved by technology

However, the local optimal scheme is iterative, and its solution is highly dependent on the initial iteration value; some have derived a low-complexity solution based on a greedy method, but when the system scale is large, its computational complexity is also high

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  • User scheduling and analog beam selection optimization method based on whale algorithm
  • User scheduling and analog beam selection optimization method based on whale algorithm
  • User scheduling and analog beam selection optimization method based on whale algorithm

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Embodiment Construction

[0070] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0071] Such as figure 1 As shown, this embodiment provides a user scheduling and simulation beam selection optimization method based on the whale algorithm, including the following steps:

[0072] Establish a problem model with joint optimization of user scheduling and simulation beam selection maximization and rate as the optimization goal, and transform it into a non-convex NP-hard problem model;

[0073] Transform the inequality constraints of the problem model into the form of penalty functions, and convert the binary constraints into the characteristics of the algorithm search population;

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Abstract

The invention discloses a user scheduling and analog beam selection optimization method based on a whale algorithm. The method comprises the following steps: converting an optimization problem model into a non-convex NP difficult problem model; converting inequality constraints of the problem model into a penalty function form, and converting binary constraints into features of an algorithm search population; multiplying the converted inequality constraint by an indication factor and a penalty coefficient, then superposing the multiplied inequality constraint on an original optimization target, and constructing a fitness function to obtain an analog beam set matched with the user set; carrying out simulation wave beam matching, i.e., selecting an optimal simulation wave beam for each user; and after the analog beams are matched, judging whether all the users in the user set are matched or not, and when all the users are matched, scheduling channels according to the analog beam set matched with the user set. The problem of joint optimization of user scheduling and beam selection of the hybrid mmWave system is solved, and the performance of the system is further improved.

Description

technical field [0001] The invention relates to the technical field of user scheduling and beam selection, in particular to a user scheduling and simulation beam selection optimization method based on a whale algorithm. Background technique [0002] For massive MIMO-mmWave systems, the traditional all-digital beamforming method is almost inapplicable in practical applications, because in all-digital beamforming, each antenna is equipped with a radio frequency (RF) chain, and each RF chain occupies a Dedicated baseband processor, so all-digital beamforming makes the complexity and power consumption of the system unbearable in the case of a large number of antennas. Hybrid beamforming divides beamforming into a low-dimensional digital part and an RF analog part, and is a low-cost massive MIMO technology. In the RF analog section, each RF is connected to all antennas (a subset of all antennas) through an interface, that is, a fully connected (partially connected) array structu...

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Application Information

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IPC IPC(8): H04B7/0413H04B7/0426H04B7/0456H04B17/00H04B17/391
CPCH04B7/0413H04B7/043H04B17/0087H04B7/0456H04B17/391Y02D30/70
Inventor 赵赛邹章晨唐冬黄高飞
Owner GUANGZHOU UNIVERSITY
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