A Cognitive Heterogeneous Network Joint Resource Allocation Method Based on Convex Optimization Method

A technology for cognitive heterogeneous network and resource allocation, applied in electrical components, wireless communication, etc., can solve the problem of not taking into account the different probabilities of primary users

Active Publication Date: 2018-04-10
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the different probabilities of primary users appearing in a certain time interval are not considered in the current algorithm that uses the joint allocation method to allocate bandwidth and power to maximize channel capacity, and proposes a method based on convex Cognitive approach to resource allocation in heterogeneous networks of optimization methods

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  • A Cognitive Heterogeneous Network Joint Resource Allocation Method Based on Convex Optimization Method
  • A Cognitive Heterogeneous Network Joint Resource Allocation Method Based on Convex Optimization Method
  • A Cognitive Heterogeneous Network Joint Resource Allocation Method Based on Convex Optimization Method

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specific Embodiment approach 1

[0020] Specific implementation mode 1: A method for joint resource allocation of cognitive heterogeneous networks based on a convex optimization method in this implementation mode is specifically prepared according to the following steps:

[0021] Step 1. Assume that the HWCN has been integrated, and the SU can access all RATs, so the SU can access multiple heterogeneous networks with idle spectrum resources during the end-to-end communication process; assuming that the arrival process of the PU obeys the Poisson distribution process , the arrival rate λ j ; Assuming that the PU service time is constant; in the case of M (M ≥ 1) cognitive users, construct an end-to-end system model of cognitive heterogeneous network according to the constraints, and determine the optimization goal to minimize the system communication delay Among them, t i means SU i Data transfer via Multi-RAT D i The total time required, t ij means SU i via RAT j Time to send data, i=1, 2,..., M, j=1, ...

specific Embodiment approach 2

[0029] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: in step one, the determined optimization goal is to minimize the system communication delay t in ij The specific derivation process is:

[0030] (1) Setting the objective function The constraints are: in the limited cognitive user power Limited optimized RAT bandwidth and SU i Data transfer via RAT D ij and is a fixed value D i :

[0031]

[0032] Among them, B ij , P ij ,D ij ≥0, i≤1, 2,..., M, j=1, 2,..., N;

[0033] (2) Let β ij means SU i Access to RAT j The bandwidth utilization ratio of the channel, then by Shannon formula, the capacity of each channel C ij for:

[0034]

[0035] In formula (17), N 0 Indicates the noise power spectral density, C ij means SU i Access to RAT j channel capacity;

[0036] (3) will SU i with SUj When communicating, PU j The delay caused by sending out the communication request is den...

specific Embodiment approach 3

[0042] Specific embodiment 3: The difference between this embodiment and specific embodiment 1 or 2 is that in step 2, in a given SU i via RAT j amount of data transferred case, the constraints are RAT j Limited amount of available bandwidth B j and SU i The limited amount of available power P j , to verify the objective function The concave-convexity of the objective function simplifies f 1 (B, P), the proof function f 1 (B, P) is a convex function and the specific process is:

[0043] (1) Prove the concavity and convexity of f(B, D, P):

[0044]

[0045] To verify the objective function Concave-convexity, simplify the objective function to f(B, D, P), where B is B in the objective function ij The simplification of , that is, the bandwidth allocated by the RAT to the SU communication, D is the D in the objective function ij Simplification of , which means the amount of data transmitted by SU through RAT; P is P in the objective function ij A simplification o...

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Abstract

A method for joint resource allocation of cognitive heterogeneous networks based on a convex optimization method. The invention relates to a method for joint resource allocation of cognitive heterogeneous networks based on a convex optimization method. The present invention aims to solve the problem that the current algorithm that uses the joint allocation method to allocate bandwidth and power to maximize channel capacity does not take into account the different probabilities of primary users appearing within a certain time interval, and proposes a method based on convex optimization. A Cognitive Approach to Heterogeneous Networks' Joint Resource Allocation. The method is through step 1, determining the optimization goal to minimize the system communication delay; step 2, using the Newton iteration method to solve the objective function in the case of substitution, and obtaining the optimal solution; and step 3, obtaining the optimal numerical solution Dij of Dij * and other steps to achieve. The invention is applied to the field of cognitive heterogeneous network joint resource allocation of convex optimization method.

Description

technical field [0001] The invention relates to the field of joint resource allocation of cognitive heterogeneous networks based on a convex optimization method. Background technique [0002] With the rapid development of radio technology, users will need a variety of wireless communication methods to meet the needs of life, and Wireless Local Area Networks (WLAN), Third Generation Universal Mobile Telecommunications Systems, 3G-UMTS), IEEE802.11, Worldwide Interoperability for Microwave Access (WiMAX) and other wireless communication technologies have differences in quality of service (Quality of Service, QoS), delay, cost, etc. , so users can choose to access different networks according to their needs. A multi-radio access technology (Multi-Radio Access Technology, Multi-RAT) system is a network that can support multiple radio access technologies to achieve multiple services in a network. Through a terminal that can access Multi-RAT, users can simultaneously Access to d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W72/04H04W72/08
Inventor 石硕梁楠顾学迈叶亮刘通周才发王泽蒙田斯朱师妲
Owner HARBIN INST OF TECH
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