9 results about "Geometric programming" patented technology

Disclosed in the present invention is a power allocation method based on a cell-free massive MIMO short-packet communication system. The method comprises: on the basis of a Rayleigh fading channel model under channel aging, deriving an expression for a downlink achievable rate of a cell-free massive MIMO short-packet communication system under user mobility; with the goal of maximizing a minimum user rate, proposing a downlink power allocation method; and on the basis of a successive convex approximation method and geometric programming, performing iterative solving, wherein an obtained solution is used as a final power allocation result for calculating the rate of each user. The present invention maximizes a minimum user achievable rate of users in a system, effectively solves the problem of uneven quality of service for the users, enhances system fairness, and also has a relatively high convergence rate.

The invention provides a parking path planning method. The method comprises the following steps: step 1, acquiring parking path planning information; 2, correcting the target position to obtain a corrected target position; step 3, planning a first section of parking path based on a mixed A star algorithm; 4, planning a second section of parking path based on a geometric programming algorithm; and 5, based on the first section of parking path and the second section of parking path, the vehicle is controlled to be parked. According to the method, the planning success rate can be ensured, and the overall planning efficiency can be improved.

The invention discloses a pre-training visual language model-based cross-country scene unmanned vehicle auxiliary decision-making method, and belongs to the technical field of unmanned driving. Comprising the following steps: constructing a simulation data set for training a three-dimensional target detection network; carrying out cross-modal consistency fusion and cue word design; and generating and mapping driver element actions and decisions. According to the invention, by fusing a three-dimensional target detection network and a pre-trained visual language model, introducing a thinking chain type reasoning and selective spatial information injection mechanism, and combining a trigger type reasoning process and meta-action sequence generation, high-level understanding and auxiliary decision making of a cross-country scene are realized; therefore, the defects of a traditional geometric programming algorithm in an unstructured environment are overcome.

The invention discloses an energy efficiency optimization method for an active RIS auxiliary cell-free mMIMO system, and the method comprises the steps: firstly, building a downlink transmission data and system consumption total power model of the active RIS auxiliary cell-free mMIMO system, and giving a downlink rate and energy efficiency expression; then, designing a communication node downlink transmission power allocation scheme by formulating a multi-objective function optimization problem, including maximizing a downlink rate and minimizing the total consumed power of the system; in order to solve the non-convex optimization problem, a weighted sum method and an arithmetic mean inequality are used for converting the non-convex optimization problem into a generalized geometric programming problem, and then an iteration mode is adopted for solving the non-convex optimization problem; and finally, taking the obtained optimal solution as a final power distribution scheme and calculating the energy efficiency of the system. According to the method, the total power consumed by the system is reduced while the downlink rate sum is improved, the energy efficiency of the system is improved, and the operation cost of the system is reduced.

The application relates to a parking control method and device, a vehicle and a storage medium. The method comprises the following steps: when a vehicle needs to be parked in an end head parking space and the vehicle drives to a parking point of the end head parking space, a target area for realizing the U-turn of the vehicle outside the end head parking space is determined, a corresponding U-turn path set of the vehicle is determined by using a geometric programming algorithm based on the parking point and the target area, a target U-turn path corresponding to the vehicle is determined from the U-turn path set, the vehicle is controlled to drive from the parking point to a target parking-in point according to the target U-turn path, so that the U-turn of the vehicle is completed, and then a target storage path corresponding to the vehicle is determined by using an A-star algorithm based on the target parking-in point, the vehicle is controlled to drive from the target parking-in point to the end head parking space according to the target storage path, and the parking of the vehicle is completed. The application avoids the problem that the parking time is relatively long when the vehicle is parked in the end head parking space only by using the A-star algorithm, and improves the driving experience of the user.

Disclosed in the present invention is a power allocation method based on a cell-free massive MIMO short-packet communication system. The method comprises: on the basis of a Rayleigh fading channel model under channel aging, deriving an expression for a downlink achievable rate of a cell-free massive MIMO short-packet communication system under user mobility; with the goal of maximizing a minimum user rate, proposing a downlink power allocation method; and on the basis of a successive convex approximation method and geometric programming, performing iterative solving, wherein an obtained solution is used as a final power allocation result for calculating the rate of each user. The present invention maximizes a minimum user achievable rate of users in a system, effectively solves the problem of uneven quality of service for the users, enhances system fairness, and also has a relatively high convergence rate.

This application discloses a method and apparatus for cooperative containment guidance of aircraft, relating to the field of aircraft guidance and control. The method includes acquiring the position, velocity, heading angle, interception time, navigation ratio of the pursuing aircraft, position, velocity, heading angle of the defending aircraft, target position, and target heading angle of the pursuing aircraft; determining the coordinates of the containment point using a geometric programming method; and controlling the target and the defending aircraft using a cooperative guidance method based on the coordinates of the containment point, the navigation ratio of the pursuing aircraft, the position, velocity, heading angle of the pursuing aircraft, the position, velocity, heading angle of the defending aircraft, the target position, and the target heading angle of the defending aircraft, thereby achieving cooperative containment of the pursuing aircraft. This application improves the effectiveness of the cooperative containment guidance strategy and the interception efficiency of the pursuing aircraft.

The application discloses an active RIS-assisted cell-free mMIMO system energy efficiency optimization method, first, the downlink transmission data and system total power consumption model of the active RIS-assisted cell-free mMIMO system is established, and the downlink rate and energy efficiency expression is given; then, by formulating a multi-objective function optimization problem, including maximizing the downlink rate and minimizing the total system power consumption, a communication node downlink transmission power allocation scheme is designed; in order to solve the non-convex optimization problem, the weighted sum method and the arithmetic average inequality are used to convert it into a generalized geometric programming problem, and then an iterative method is used for solving; finally, the obtained optimal solution is used as the final power allocation scheme and the system energy efficiency is calculated, the application reduces the total system power consumption while improving the downlink rate and, improves the system energy efficiency, and reduces the system operation cost.

This invention investigates the offline one-dimensional resource allocation problem of maximizing the utility of a convex increasing function system within a given time frame, under both energy causality (hard) and energy saturation (soft) constraints. Utilizing the Karush-Kuhn-Tucher condition, a modified tunneling strategy based on geometric programming is proposed, considering only causal hard constraints and saturation soft constraints. A computational method for allocating energy to various time slots is presented. This method is particularly advantageous when the geometric allocation line simultaneously encounters either the upper or lower edge of the energy constraint. This invention solves the energy allocation problem of maximizing the utility of an increasing convex function system under causal hard constraints and saturation soft constraints.