10 results about "Capacity maximization" patented technology

The present invention discloses a method and device for optimizing the security capacity of a drone-assisted backscatter communication system, which relates to the field of wireless communication technology. The system includes one drone, K backscatter devices and M eavesdroppers, and includes the following steps: obtaining collected energy, inputting the collected energy into the backscatter device for backscatter communication, optimizing time allocation, drone trajectory and backscatter device scheduling based on preset energy constraints and security performance constraints of backscatter communication; solving time allocation, drone trajectory and backscatter device scheduling respectively to obtain three optimal solutions, performing global iteration on the three optimal solutions to obtain a global optimal solution as the security capacity optimization result, thereby maximizing the security capacity of the minimum link.

The application provides a power distribution network layered and partitioned power balance method based on model prediction, takes cost minimization, regulation capacity maximization and response speed optimization as targets, constructs a multi-objective optimization model for initial division, solves the initial division optimization through source-load matching degree, takes the size of the net load in the region after the optimization scheduling as the basis for judging the source-load matching degree of the region, and partitions the power distribution network; the historical data collected are preprocessed, a load prediction model is constructed, the preprocessed data are input into the model, and future power load of the power distribution network partition is obtained; based on the model prediction control, the active power, the reactive power and the charging and discharging state of the energy storage system of the distributed energy are predicted on the premise of meeting the power load demand, and power supply and demand balance control is realized. Compared with the prior art, the application reduces the network loss of the power distribution network, improves the local consumption capacity, and effectively suppresses voltage fluctuation.

The invention relates to an underground 5G Massive MIMO (Multiple Input Multiple Output) transmitting antenna selection method. The selection method comprises the following steps: determining an underground 5G Massive MIMO transmitting antenna selection model according to a target problem, wherein the target problem comprises maximization of a sum capacity, maximization of a minimum characteristic value and maximization of an F norm of a transmission channel; introducing a diagonal matrix, and converting the selection model into a convex optimization problem model; and calculating and solving the convex optimization problem model to obtain an MIMO transmitting antenna selection scheme. According to the underground 5G Massive MIMO transmitting antenna selection method, the method can be used for maximizing the sum capacity, maximizing the minimum characteristic value, maximizing the F norm of the transmission channel and the like, and the underground 5G Massive MIMO transmitting antenna selection method has certain universality, low operation complexity and better solvability.

The invention discloses an RIS-based capacity optimization method for a composite frame short packet cooperative sensing system, and the method comprises the steps: building an optimization function in a multi-base-station cooperative short packet sensing system by taking the maximization of the reachable capacity of a communication user and a detection target as a target; adopting a one-dimensional search method to obtain an allocation scheme of pilot frequency block length and data block length in the composite frame structure; a first-order Taylor estimation method, a logarithm transformation method and a secondary conversion method are adopted to obtain an active and passive beam forming joint design scheme of the sensing base station and the RIS; and after iterative optimization, obtaining the optimal solution of the block length allocation and the active and passive beam forming scheme, and realizing the maximization of the total reachable capacity of the system. Based on the designed composite frame structure, under the assistance of the RIS, a block length allocation and active and passive beam forming combined design scheme is provided, ultra-reliable and low-delay communication and sensing services can be provided for a 6G vertical application scene, and the overall communication performance of a short packet cooperation sensing system is effectively improved.

To obtain a larger network capacity by setting three network hierarchies in a Clos network.SOLUTION: The network designed by the network design device 1 is provided with a plurality of first stage switches belonging to a first stage and connected to a plurality of terminals, a plurality of second stage switches belonging to a second stage and connected to the plurality of first stage switches, and a plurality of third stage switches belonging to a third stage and connected to the plurality of second stage switches. The network design device 1 includes a calculation unit 12 that calculates the number of terminals and the number of first stage switches so that the total probability of each blockage occurrence probability in a case where the number of terminals is increased by one from the maximum value of the number of terminals when the network is non-blocked does not exceed the allowable blockage rate ε and the network capacity is maximized in a network that satisfies the number of ports N of each switch, the total number a of a plurality of switches, and the terminal utilization rate p.SELECTED DRAWING: Figure 8

This invention discloses a capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflector. In a heterogeneous cooperative sensing integrated system composed of multiple sensing base stations and one single-function communication base station, an optimization function is constructed with the goal of maximizing the sum of achievable capacity for communication users and detection targets. A convex optimization method is used to derive the allocation scheme between the sensing integration time and the pure communication time throughout the entire transmission cycle. A first-order Taylor estimation method, a logarithmic transformation method, and a quadratic transformation method are used to derive the design schemes for the transmitting beamforming of the sensing base station and the reflection and refraction coefficient matrices of the omnidirectional intelligent reflector. After iterative optimization, the optimal solutions for the time allocation scheme, the transmitting beamforming design scheme, and the reflection and refraction coefficient matrix design scheme are obtained, thereby maximizing the sum of achievable capacity for communication users and detection targets in the heterogeneous cooperative sensing system.

The invention discloses a TD3N-based MIMO-FSO power allocation method, which adopts a deep reinforcement learning technology, provides a deep reinforcement learning-based power allocation algorithm for a changing atmospheric environment based on an MIMO-FSO system in a multiplexing mode, and reasonably allocates power resources according to channel state information at different moments, so that the power allocation efficiency is improved. The capacity maximization under the limitation of the total power and the peak power is realized, and the spectrum efficiency of the system is improved. In addition, the algorithm also fully considers the influence of atmospheric turbulence, atmospheric attenuation, noise factors and the like on the MIMO-FSO system, so as to ensure that the system can maintain excellent communication performance under real and changeable environmental conditions.

The invention provides a user selection and power distribution method and system based on space division multiplexing, and the method comprises the steps: obtaining system parameters based on a multi-user MIMO scene, and the system parameters comprise a channel matrix, total transmitting power, noise power, the number of base station antennas, the total number of users, and the number of users to be selected; calculating an equivalent power gain of each user in a multi-user scene based on the channel matrix; constructing a corresponding search tree based on the single-user equivalent power gain as a priority reference in a single-user scene; constructing an optimization problem of joint user selection and power distribution which takes the user subset and the power distribution as joint optimization variables and takes the maximum sum of the reachable rates of all served users as an objective function; and solving the optimization problem of the joint user selection and power distribution, and outputting the optimal user subset and the optimal power distribution scheme. According to the method, the optimal user set can be dynamically selected and the power is allocated under the constraint of limited transmitting power, so that the system capacity is maximized.

The present application discloses a method, device, equipment and medium for allocating dynamic scattering communication spectrum resources, which relates to the field of communication technologies. The method includes: determining a scattering site network and a link set composed of each scattering site within a target planning range; dividing the target spectrum resources into multiple non-interfering sub-resource blocks according to the number of links and link types in the link set; based on each sub-resource block, initially allocating spectrum resources to each link according to the basic rules for spectrum resource allocation; the basic rules for spectrum resource allocation are determined according to the link type and link relationship; the link relationship includes the ratio of the link length to the station distance and the link angle; based on the link quality theory set, starting from the initial allocation of spectrum resources to each link, every set time, the learning ability of the Bayesian network is used to infer the spectrum resource allocation strategy, and with the maximization of link capacity as the optimization goal, the spectrum resource allocation of each link is adjusted. The present application can improve the utilization rate of scattering communication spectrum resources.

The application discloses an optimal power allocation method for maximizing system capacity based on random modulation, which comprises the following steps: after a signal is randomly modulated, a discrete lookup table of the MMSE curve of the signal is established; an optimization model is established, so that the area surrounded by the inverse function of the MMSE function of the cross-domain orthogonal approximate message passing and the coordinate axis is maximized, and the optimal power allocation scheme for maximizing the limited capacity of the system is obtained; the inverse function of the MMSE function is obtained through the discrete lookup table, and the optimal power allocation scheme for maximizing the system capacity is obtained by solving the optimization model by using a convex optimization tool. The application can provide the optimal power allocation scheme for maximizing the replica limited capacity in a communication system with almost any discrete input in almost any actual channel.