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CN-121984563-A - NOMA-SIC uplink weighted sum rate maximization method based on unmanned aerial vehicle RIS

CN121984563ACN 121984563 ACN121984563 ACN 121984563ACN-121984563-A

Abstract

The invention relates to the technical field of wireless communication and intelligent agriculture intersection, in particular to a NOMA-SIC uplink weighting and rate maximizing method based on unmanned aerial vehicle RIS, by constructing an uplink system composed of a fusion center, an unmanned aerial vehicle-mounted reconfigurable intelligent surface and sensor nodes, the fusion center adopts a continuous interference elimination technology, and multi-user concurrent transmission is realized based on non-orthogonal multiple access power domain multiplexing. The method combines the split planning, the secondary transformation and the successive approximation technology, and maximizes the system weighted sum rate by alternately optimizing the phase configuration of RIS, the sensor transmitting power and the fusion center receiving beam forming vector under the constraint condition of meeting the power, the phase and the service quality. The invention fully plays the advantages of NOMA frequency spectrum multiplexing and SIC interference elimination, remarkably improves the transmission reliability and frequency spectrum efficiency, is suitable for low-time-delay high-reliability acquisition of intelligent agriculture large-scale sensor data, and can be expanded to scenes such as industrial Internet of things, emergency communication and the like.

Inventors

  • ZHAO HONG
  • SHENG KANGQI
  • ZHAN LING

Assignees

  • 桂林电子科技大学

Dates

Publication Date
20260505
Application Date
20251230

Claims (9)

  1. 1. A NOMA-SIC uplink weighted sum rate maximization method based on unmanned on-board RIS, comprising the steps of: Step 1, an uplink communication system is constructed, wherein the uplink communication system comprises a fusion center, an unmanned aerial vehicle carrying a reconfigurable intelligent surface and K sensor nodes, the fusion center is provided with M receiving antennas, the reconfigurable intelligent surface consists of N reflecting units, a direct channel link exists between the fusion center and the sensor nodes, and the sensor nodes and the fusion center construct a reflecting channel link through the reconfigurable intelligent surface carried by the unmanned aerial vehicle; Step 2, establishing a channel model, wherein the channel model comprises a rice fading channel from a reconfigurable intelligent surface to a fusion center, a rice fading channel from a kth sensor node to the reconfigurable intelligent surface and a Rayleigh fading channel from a kth sensor node to the fusion center, wherein k=1, 2; Step 3, establishing an optimization problem with maximized weighted sum rate, wherein an objective function of the optimization problem is the weighted sum rate, and constraint conditions comprise a sensor node transmitting power constraint, a reconfigurable intelligent surface phase constraint and a service quality constraint; and 4, solving the optimization problem by adopting an alternative optimization algorithm combining split programming FP, secondary transformation QT and successive approximation, wherein the method specifically comprises the following sub-steps: Initializing iteration parameters, including setting iteration indexes, initializing a reconfigurable intelligent surface phase matrix, transmitting power of sensor nodes and a fusion center receiving beam forming vector, and setting a convergence threshold; Step 4.2, iteratively updating Lagrangian auxiliary variables, updating the auxiliary variables according to the signal-to-interference-and-noise ratio of the previous iteration, updating complex auxiliary variables based on secondary transformation, fixing a phase matrix, transmitting power and receiving beam forming vectors, introducing the complex auxiliary variables and converting an objective function in a division form into a sum-difference form; Step 4.3, optimizing a beam forming vector received by a fusion center, fixing complex auxiliary variables, a phase matrix and transmitting power, constructing an objective function based on a Lagrangian multiplier method and solving a closed solution; 4.4, optimizing the transmitting power of the sensor node, fixing complex auxiliary variables, receiving beam forming vectors and a phase matrix, carrying out variable replacement, constructing an objective function and solving the optimal transmitting power; Step 4.5, fixing complex auxiliary variables, receiving beam forming vectors and transmitting power, introducing and updating phase auxiliary variables, converting a phase optimization problem into a secondary constraint quadratic programming form, and solving by adopting a successive approximation method to obtain an optimal phase vector; And 4.6, convergence judgment, calculating the weighted sum rate of the current iteration, terminating the iteration and outputting an optimal solution if the convergence condition is met, and otherwise, returning to the step 4.2 to continue the iteration.
  2. 2. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, The rice fading channel model from the reconfigurable intelligent surface to the fusion center in the step 2 is that Wherein For the path loss scalar quantity, Is the rice factor; as a component of the line of sight, Is an NLOS component; The Rice fading channel model from the kth sensor node to the reconfigurable intelligent surface is as follows Wherein For the path loss scalar quantity, In the sense that the rice is a rice factor, As a component of the line of sight, Is a non-line-of-sight component; The k sensor node to fusion center Rayleigh fading channel model is that Wherein For a large scale fading coefficient, For the path loss at the reference distance, In order to be a distance from each other, Is the path loss index.
  3. 3. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, The optimization problem of establishing weighted sum rate maximization in step 3 includes the steps of: step 3.1 defining an objective function as system weighted sum rate maximization expressed as Wherein Is the weight coefficient of the kth sensor node, The signal-to-interference-and-noise ratio of the kth sensor node at the fusion center is set; Step 3.2, establishing a sensor node transmitting power constraint, wherein the transmitting power of each sensor node is required to meet the requirement of Wherein Maximum allowed transmit power for the kth sensor node; Step 3.3, establishing a reconfigurable intelligent surface phase constraint, requiring the phase of each reflection unit to satisfy And is also provided with Wherein As a set of discrete phases, n=1, 2,. -%, N; step 3.4, establishing service quality constraint, requiring the signal-to-interference-and-noise ratio of each sensor node to meet the requirement of ≥ Wherein The lowest signal-to-interference-and-noise threshold required for proper detection of the signal.
  4. 4. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, The execution process of the step 4.1 comprises the following steps: step 4.1.1, initializing iteration parameters, setting iteration index t=0, and initializing reconfigurable intelligent surface phase matrix Each reflecting unit phase slave Randomly selecting; step 4.1.2 initializing the SN transmit power vector The initial value is the maximum transmitting power of each sensor node Half of (2); step 4.1.3 initializing FC receive beamforming vector The initial value is a random unit vector; Step 4.1.4 setting Convergence threshold Value range ≤ ≤ 。
  5. 5. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, Step 4.2 comprises the steps of: Step 4.2.1, iteratively updating Lagrangian auxiliary variables to enable Calculating Lagrangian auxiliary variable according to SINR of the previous iteration ; Step 4.2.2 fixed phase matrix Transmission power And receiving a beamforming vector ; Step 4.2.3 introducing a plurality of auxiliary variables And converting the objective function in the form of a partial equation into a sum and difference form by secondary transformation.
  6. 6. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, The execution process of the step 4.3 comprises the following steps: step 4.3.1 introducing regularization parameters Ensuring matrix reversibility and solving fusion center receiving beam forming vector ; Step 4.3.2 fixing the complex auxiliary variables ; Step 4.3.3 construction of an objective function based on Lagrangian multiplier method 。
  7. 7. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, Step 4.4 comprises the steps of: step 4.4.1: Fixing a plurality of auxiliary variables ; Step 4.4.2 substitution by variables The secondary constraint of the power is converted into linear constraint, so that the solving difficulty is reduced; Step 4.4.3 reduction of the objective function to For each of Separately solve by Restoring transmit power 。
  8. 8. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, The execution process of the step 4.5 comprises the following steps: step 4.5.1 fixing the complex auxiliary variables And introducing phase auxiliary variables , ; Step 4.5.2, converting the reconfigurable intelligent surface phase optimization into a secondary constraint quadratic programming problem, and simplifying an objective function into ; Step 4.5.3 solving by successive approximation method to obtain optimal phase vector, and performing successive approximation on At the position of Performing first-order Taylor linearization at the position, and approximating the expression as Using a minimum Euclidean distance criterion Projection onto a set of discrete phases 。
  9. 9. The method for maximizing the NOMA-SIC uplink weighted sum rate based on an unmanned on-board RIS of claim 1, Step 4.6 specifically includes the following steps: Step 4.6.1 calculating the WSR of the current iteration, ; Step 4.6.2 if Or (b) The iteration is terminated; And 4.6.3, otherwise, returning to the step 4.2 to continue iteration.

Description

NOMA-SIC uplink weighted sum rate maximization method based on unmanned aerial vehicle RIS Technical Field The invention relates to the technical field of wireless communication and intelligent agriculture intersection, in particular to a NOMA-SIC uplink weighting and rate maximizing method based on unmanned aerial vehicle RIS. Background With the deep advancement of digital transformation of agriculture, intelligent agriculture has become an important direction for improving the production efficiency of agriculture and realizing refined planting and sustainable development. The method is characterized in that key environment parameters such as soil humidity, air temperature and humidity, illumination intensity, plant diseases and insect pests and the like are collected in real time by deploying large-scale low-power consumption sensor nodes in a farmland environment, and monitoring data are reliably transmitted to a remote fusion center by means of a wireless communication technology, so that data support is provided for agricultural decisions such as irrigation, fertilization, plant diseases and insect pests control and the like. However, typical farmland communication scenarios have the characteristics of wide area, complex environment, distributed nodes, etc., which brings serious challenges to wireless data transmission, and are mainly characterized in the following aspects: Firstly, in terms of coverage and link reliability, farmland areas can reach hundreds of mu, and obstacles such as crop shielding, topography fluctuation and the like exist, so that most of sensor nodes and fusion centers are non-line-of-sight propagation links, and signal attenuation is serious. Second, in terms of large-scale node access, modern smart agricultural systems require deployment of tens to hundreds of sensor nodes for a single farmland. The traditional orthogonal multiple access technology distributes independent communication resources for different nodes in a time division or frequency division mode, so that the spectrum utilization rate is generally low, and the concurrent access requirements of large-scale sensor nodes cannot be effectively supported. Moreover, in terms of low power consumption and real-time balance, the sensor nodes are usually powered by batteries, and require a duration of more than 6 months, so that the maximum transmission power of the sensor nodes is usually not more than 20dBm. The low power transmission leads to the degradation of the channel link quality, and farmland environment monitoring is required to ensure certain information transmission instantaneity. The traditional low-complexity optimization algorithm cannot meet the real-time requirement and ensure the communication performance. To address the challenges described above, in recent years, a fusion approach of unmanned aerial vehicles, reconfigurable smart surfaces, and non-orthogonal multiple access techniques has attracted considerable attention. The unmanned aerial vehicle can flexibly hover 50-100 meters above a farmland by virtue of high maneuverability, effectively avoid crop shielding, construct a high-quality line-of-sight reflection link for the sensor node and the fusion center, and remarkably expand communication coverage. The reconfigurable intelligent surface is composed of hundreds of low-power passive reflection units, and the phase of each reflection unit can be dynamically regulated and controlled through software programming, so that the wireless propagation environment is intelligently reconfigured, and the system channel gain is improved by 3-6 dB. The non-orthogonal multiple access (NOMA) technology realizes parallel transmission of multiple users on the same time-frequency resource through power domain multiplexing, and combines with the continuous interference cancellation (SIC) technology of the receiving end to separate user signals, so that the spectrum efficiency can be improved by more than 50% compared with the traditional orthogonal multiple access scheme. The NOMA system decodes strong signals at the receiving end in sequence by reasonably distributing user power and utilizing SIC and eliminates interference of the strong signals to weak signals, which is a key for realizing large-scale access and high spectrum efficiency. However, interference management of multi-user power domain multiplexing in the NOMA uplink system has the following defects that residual interference accumulation is caused by non-ideal Serial Interference Cancellation (SIC), the adaptability of power allocation and user pairing is insufficient, and the stability of interference control is damaged by channel dynamic change. Disclosure of Invention The invention aims to provide a NOMA-SIC uplink weighting and rate maximizing method based on unmanned aerial vehicle RIS, which constructs an uplink system consisting of a fusion center, an unmanned aerial vehicle reconfigurable intelligent surface and a sensor node, wherein the fusi