12 results about "Ultra dense network" patented technology

The invention belongs to the technical field of intelligent traffic, and discloses an expressway toll station 5G ultra-dense network non-inductive passing method, which comprises the following steps: firstly, carrying out vehicle connection and information transmission, when a vehicle enters a 5G base station coverage area, automatically connecting a vehicle-mounted terminal with a base station, uploading the basic information, the driving speed and the position of the vehicle to a toll station control center in real time, and sending the vehicle information to the toll station control center; then, the control center evaluates and decides the vehicle credit, the control center verifies information through edge computing nodes, the cloud server inquires payment and credit records, evaluates the vehicle credit and generates a passing or early warning instruction, finally, the control center formulates a passing decision, a barrier machine of the vehicle with good credit is lifted, and a signal lamp turns green to release the vehicle; therefore, the rapid and stable transmission and processing of the vehicle information are realized, the residence time of the vehicle at the toll station is greatly shortened, the passing efficiency of the toll station is remarkably improved, and the traffic jam is effectively relieved.

This invention discloses a user clustering method for ultra-dense networks, relating to the field of mobile communication technology. The method includes the following steps: selecting the users with the largest user interference vector values ​​in the ultra-dense network as root node users, and the remaining users as branch users; calculating and determining the relative interference vector values ​​between each branch user and the multiple root node users; when the relative interference vector value between a branch user and a certain root node user is less than the relative interference vector value between that branch user and other root node users, then that branch user is assigned to an interference vector cluster formed with that root node user, thus forming multiple interference vector clusters; calculating and determining the relative interference vector value between the interference vector value of the root node user in each interference vector cluster and the mean interference vector value of all users in the cluster, and determining the user clustering result. This invention is simple, improves system throughput and frequency utilization, and has strong applicability.

The present invention relates to the field of ultra-dense network communication technology, and specifically discloses a real-time spectrum resource optimization method and system based on deep reinforcement learning, wherein the method comprises: constructing a typical ultra-dense network downlink scenario, simulating the user's dynamic movement behavior according to a random walk model; performing system modeling analysis based on the ultra-dense network downlink scenario, generating a joint optimization model with the goal of maximizing the total system throughput; solving the optimal solution of the joint optimization model through a deep reinforcement learning algorithm, and setting the user's state function, the user's action function, and the reward function to train the user to learn the optimal solution. This application constructs a dynamic ultra-dense network environment based on a random walk model by considering the unpredictability of dynamic user movement.

The application discloses a base station energy-saving method, an energy-saving device and a readable storage medium for a super-dense network, wherein the base station energy-saving method comprises the following steps: clustering each base station in the super-dense network according to a clustering algorithm to obtain a plurality of clustering clusters; for a clustering cluster in which the number of base stations is greater than a preset number, determining a target base station according to the size of the traffic volume of the base stations in the cluster within a preset time period, and performing an energy-saving operation on the base stations other than the target base station in the cluster; and for a clustering cluster in which the number of base stations is less than or equal to the preset number, determining a plurality of dormant base stations according to the similarity of the traffic volume of the base stations in the cluster within the preset time period, and performing an energy-saving operation on the dormant base stations. Through the above method, the coordinated energy saving of the multiple base stations in the super-dense network can be realized, the interference between the dense base stations is reduced, the energy utilization rate is effectively improved on the premise of ensuring the user perception experience, and the base station operation cost of the telecommunication operator is reduced.

This invention discloses a multi-agent cooperative anti-interference method based on spectrum data aggregation. First, a multi-agent cooperative anti-interference model based on spectrum data aggregation is established. Second, a joint action is selected based on a greedy strategy. Third, the joint action is executed to obtain a reward, the current spectrum is perceived, the state transitions to the next state, and the experience is stored in the agent's experience pool. Then, random batch sampling is performed from the experience pool, the target Q-value is calculated, the gradient of the loss function is calculated, and the weight values ​​are trained and updated. The algorithm terminates when the maximum number of iterations is reached. This invention features a complete model with clear physical meaning, avoids malicious interference and mutual interference between users, significantly improves the anti-interference performance of ultra-dense networks with incomplete perceptual information, and provides a fairer frequency usage strategy.

The invention discloses a 5G ultra-dense networking interference coordination and energy efficiency optimization system, and belongs to the technical field of 5G mobile communication, the system comprises an inter-cell interference measurement module, a cooperative transmission scheduling engine, a dynamic sleep control platform and a network topology self-optimizer, the interference measurement module constructs a weighted interference graph model, and the continuous change characteristic of interference intensity is accurately quantified; the cooperative transmission scheduling engine designs a zero-forcing precoding strategy based on spectral clustering and a virtual MIMO channel model, and converts interference signals into useful signals; the dynamic dormancy control platform adopts an LSTM (Long Short Term Memory) network to predict a load, and realizes intelligent dormancy of flow perception through multi-objective optimization; the network topology self-optimizer adopts a simulated annealing algorithm to continuously optimize a network deployment structure, the spectrum efficiency and the energy efficiency of the ultra-dense network are remarkably improved, the spectrum efficiency is improved by 28%, the energy efficiency ratio is improved by 86%, and the system capacity is increased by 40% to 60%.

The application relates to the technical field of communication, in particular to a high-density scene-oriented interference coordination communication method, aiming to solve the problems of low spectrum efficiency and excessive signaling overhead caused by multi-dimensional composite interference in an ultra-dense network. The method comprises the following steps: constructing a three-dimensional interference feature matrix in the time domain, the frequency domain and the space domain to accurately depict the interference; only broadcasting high-interference resource identifiers through a sparse interaction protocol to reduce the signaling load; jointly optimizing time-frequency resource blocks and beamforming directions to dynamically avoid interference areas; adaptively adjusting the beam null position based on the compression sensing result to suppress the interference source; and introducing a closed-loop verification mechanism to trigger strategy updating according to the actual throughput and the signal-to-interference-and-noise ratio. Through the above technical scheme, high-precision interference coordination, low-overhead scalable coordination and millisecond-level dynamic adaptation are realized, the ultra-reliable and low-latency service is guaranteed, and the system spectrum efficiency and the edge user performance are significantly improved.

The application discloses a RIS-assisted RSMA super-dense network and a coverage performance analysis method. The RIS-assisted RSMA super-dense network comprises: a plurality of micro base stations arranged in a hotspot area with dense users; the spatial positions of the micro base stations are modeled by using a Poisson cluster process, and the micro base stations are independently and identically distributed around a cluster center; a plurality of macro base stations, the spatial positions of which are modeled by using a Poisson point process, and the spatial positions of the macro base stations are independent and uniformly distributed; wherein each micro base station and macro base station is respectively equipped with RIS. The RIS-assisted RSMA super-dense network provided by the application can improve the coverage rate of the system and the regional spectrum efficiency of the system.

The present invention belongs to the field of mobile communication technology, and specifically relates to an edge computing offloading method based on dynamic user satisfaction in an ultra-dense network, the method comprising: building a multi-user ultra-dense network and initializing the network; user equipment in the network generates a computing task and sends a task offloading request to a macro base station; the macro base station obtains user equipment information and network environment information and builds a task model; the task model and network environment information are input into a trained task decision model to obtain a task offloading decision; the macro base station sends the task offloading decision to the user equipment and a micro base station respectively; the user equipment executes the task offloading according to the task offloading decision, and the micro base station allocates computing resources to perform task calculation according to the task offloading decision; the present invention improves the model of traditional tasks and measures the energy consumed by task execution so that it can meet the energy consumption requirements of users under different conditions.

The invention discloses a communication network spectrum dynamic adjustment and optimization method based on reinforcement learning, and relates to the technical field of communication network intelligent optimization. According to the communication network spectrum dynamic adjustment optimization method based on reinforcement learning, the operation state data of multiple access nodes in multiple historical sampling periods are obtained, a reinforcement learning model is trained, reasoning is carried out based on the current communication network state, and the communication network spectrum dynamic adjustment optimization is realized. According to the method, the spectrum configuration strategy of the communication network is trained through the reinforcement learning model, the historical and current operation state data of the multiple access nodes are combined, the channel allocation, frequency band switching and interference coordination scheme is dynamically optimized, and the optimal spectrum configuration action including channel allocation, frequency band switching and interference coordination is output. And the spectrum use efficiency is improved. The method is especially suitable for an ultra-dense network scene, has good adaptivity and global cooperation capability, can significantly reduce interference overlapping and resource idling phenomena, and enhances communication quality and system stability.

The invention relates to a smart power grid ultra-dense networking and parameter monitoring method based on 5G communication. The method comprises the following steps: step 1, constructing a layered and elastic ultra-dense communication network system; 2, dividing service priority slices, and constructing a virtual logic network on the physical network by adopting a dynamic resource scheduling technology; step 3, comprehensively collecting and uploading operation data based on a global intelligent sensing and parameter monitoring technology of dynamic resource scheduling; 4, based on the edge side intelligent processing and real-time control technology of the monitoring parameters, primary processing and analysis are carried out at the source of data generation, and rapid response is achieved; step 5, constructing a cloud AI analysis platform to carry out depth parameter monitoring analysis, and carrying out big data fusion and depth mining at the cloud to realize predictive maintenance and optimal scheduling; and step 6, formulating a dynamic base station dormancy mechanism to optimize energy efficiency. According to the invention, the network performance can be improved by using ultra-dense network deployment.

The invention discloses an edge calculation unloading method based on a whale group collaborative optimization strategy, and belongs to the field of Internet of Things. The invention provides a calculation unloading strategy based on an improved whale optimization algorithm for solving the problem of calculation unloading of user equipment in a mobile scene in an ultra-dense network. The method comprises the following steps: firstly, planning a computing task of user equipment in a topological sorting mode to enable the computing task to meet the premise of partial unloading; then, for a user equipment party, a problem modeling considering time and energy consumption is provided, and an unloading problem is converted into an optimization problem of a non-convex function. And finally, solving the problem by adopting an improved whale optimization algorithm. Experimental results show that the method provided by the invention has good performance in the aspects of reducing time delay and energy consumption.