Multi-Carrier Resource Allocation in Wireless-Powered Backscatter Networks
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Solution Overview
Problem
Existing wireless-powered backscatter communication networks face challenges in optimizing multi-carrier power allocation, time coefficients, and backscatter coefficients, leading to high energy consumption and low transmission rates in IoT devices, with existing methods only achieving local optimal solutions rather than global optimal resource allocation.
Innovation Solution
A multi-carrier resource allocation method is introduced, dividing the transmission process into two stages, optimizing transmit power, time allocation, and backscatter coefficients using a Lagrangian function and gradient descent method to maximize total transmission rate, while ensuring energy and power constraints are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional block coordinate descent method and alternate optimization are used for multi-carrier resource allocation, then the optimization process is simplified, but only local optimal value can be obtained and global optimal resource allocation policy cannot be achieved
Solution Approach 1:
The patent segments the multi-carrier resource allocation problem into two separate optimization stages: first optimizing power allocation coefficients across carriers, then optimizing time coefficients and backscatter coefficients. This segmentation allows the use of simpler iterative methods while progressively approaching global optimality through coordinated optimization of different parameter groups.
Solution Approach 2:
The patent applies preliminary action by first determining optimal power allocation coefficients before proceeding to optimize time and backscatter coefficients. This sequential approach establishes a foundation for subsequent optimization steps, enabling the system to achieve better overall performance compared to simultaneous optimization methods.
2Use of energy by moving object
If backscatter communication is used to reduce energy consumption, then device energy consumption decreases, but transmission rate is also limited
Solution Approach 1:
The patent applies parameter changes by dynamically optimizing power allocation coefficients, time coefficients, and backscatter coefficients to find the optimal balance between energy consumption and transmission rate. By adjusting these parameters across different carriers and time slots, the system maximizes transmission efficiency while maintaining low energy consumption characteristics of backscatter communication.
Solution Approach 2:
The patent introduces multi-carrier dimensionality to overcome the single-carrier transmission rate limitation. By allocating resources across multiple orthogonal frequency-division multiplexing (OFDM) carriers with different power allocation coefficients, the system achieves higher aggregate transmission rates while each individual carrier continues to operate in energy-efficient backscatter mode.
3Productivity
If multi-carrier power allocation and time coefficient optimization are performed jointly, then total transmission rate increases, but computational complexity increases
Solution Approach 1:
The patent segments the joint optimization problem into separate optimization phases: first optimizing power allocation coefficients independently for each carrier, then optimizing time coefficients and backscatter coefficients in subsequent iterations. This segmentation reduces the dimensionality of the optimization problem at each step, making the computational task more manageable while still achieving joint optimization benefits.
Solution Approach 2:
The patent employs dynamic iterative optimization where power allocation coefficients, time coefficients, and backscatter coefficients are updated sequentially in multiple iterations. This dynamic approach allows the system to converge to optimal solutions without requiring complex closed-form solutions, balancing computational complexity with performance improvement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach establishes an optimal resource allocation policy that enhances the total transmission rate by efficiently managing energy and power allocation across subcarriers, improving the overall performance of wireless-powered backscatter communication systems.
Implementation Method 1
environmental backscatter communication allows IoT devices to modulate and reflect radio-frequency signals without generating radio-frequency signals by themselves
Implementation Method 2
harvest surrounding electromagnetic energy to support circuit power consumption
Data Source
AI summary
The present invention relates to a multi-carrier resource allocation method based on a wireless-powered backscatter communication network. The method comprises following steps: S1. constructing a wireless-powered backscatter communication system; S2: according to circuit power and transmit power constraints, establishing a resource allocation optimization problem taking a maximum total transmission rate of the system as an objective function; S3: according to the objective function and constraint conditions, decomposing an optimization sub-problem taking the transmit power of the backscatter transmitter as a variable; S4: after substituting optimal transmit power of the backscatter transmitter into an original problem, decomposing a sub-problems taking an energy allocation coefficient as a variable from an original optimization problem; S5: converting non-convex problems containing coupling variables into convex problems, creating a Lagrangian function, obtaining an optimal solution form according to a KKT condition, and iteratively updating corresponding variables using a gradient descent method until convergence.