7 results about "Ultra dense network" patented technology

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 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 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.