Preprocessing-based branch dividing and cutting combined network optimizing and wave beam forming method
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A joint network and beamforming technology, applied in network planning, advanced technology, electrical components, etc., can solve the problem of high complexity of the beamforming system
Active Publication Date: 2015-04-29
HARBIN INST OF TECH
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[0004] The present invention is to solve the problem of high complexity of the joint network optimization and beamforming system under the existing centralized network architecture, and
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specific Embodiment approach 1
[0099] The specific embodiment one, based on preprocessing branch pruning joint network optimization and beamforming method, it comprises the following steps:
[0100] Step 1. According to the total power consumption model of the base station and the capacity overhead of the backhaul link, establish a trade-off model between power and backhaul capacity
[0101] F = Σ l = 1 L a l P l IDL + Σ l = 1 L η l Σ k = 1 K | | w lk | | ...
specific Embodiment approach 2
[0118] Specific implementation mode 2. In this specific implementation mode, the specific steps of establishing a compromise model of power and backhaul capacity described in step 1 are:
[0119]Step 1: Build a system model. Suppose there are L base stations, and each base station is equipped with N antennas, and Λ={1,...,L} represents the set of base stations. The scheduled users are K single-antenna users, and K={1,...,K} represents a user set. The multi-cell centralized network architecture is as follows figure 1 shown. Beamforming is used for each user's data, then the transmitted signal at the lth base station is
[0120] x l = Σ k = 1 K w lk s k , ∀ l ∈ Λ - - - ( ...
specific Embodiment approach 3
[0155] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT Three, in this specific implementation, the specific steps of converting the non-convex SINR constraint into a second-order cone constraint described in step two, and performing relaxation processing on the optimization problem (P1) are:
[0156] The purpose of the present invention is to achieve a trade-off between power consumption and backhaul capacity overhead, and the problem can be modeled as
[0157] P : min w lk , a l , b lk Σ ...
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Abstract
The invention discloses a preprocessing-based branch dividing and cutting combined network optimizing and wave beam forming method and relates to the field of wireless communication. In order to fully exert the advantage of combined transmission in multi-point transmission, optimize power, reduce cost of return link and reduce the implementation complexity of the system, the method comprises the following steps: firstly modeling the problem of optimization of power consumption and return link cost reduction as a non-convex hybrid integer programming problem, converting non-convex constraint to convex constraint to obtain MI-SOCP problem, then relaxing binary integer constraint and converting to an SOCP problem; then integrating a BnB method and a tangent plane method to obtain a BnC method, preprocessing part of binary integer variables to determine values of the variables, integrating the binary integer constraint with the values of the variables so as to greatly reduce the implementation complexity, optimize power and the cost of the return link. The preprocessing-based branch dividing and cutting combined network optimizing and wave beam forming method is suitable for processing signals in the field of wireless networks.
Description
technical field [0001] The invention relates to the field of wireless communication. Background technique [0002] With the explosive growth of high-data-rate application requirements, such as high-quality wireless video streaming, etc., higher requirements are placed on fifth-generation wireless communication systems (5G). It is expected that in 2020, the spectral efficiency can be greatly improved (1000 times) while minimizing power consumption. However, although the cellular network architecture can support large-area coverage and user mobility, it cannot achieve a breakthrough in spectral efficiency. In order to meet the above requirements, some new network architectures and advanced signal processing techniques are gradually proposed. With the widespread use of miniature base stations such as microcells and picocells, the distance between each base station and users has been greatly reduced. The data of the base station is received by the user through short-distance ...
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