7 results about "Cellular traffic" patented technology

The invention provides a cellular data low-frequency trend self-adaptive extraction method, which is used for automatically extracting an evolution trend from nonlinear and strong-fluctuation cellular flow data. According to the method, firstly, missing values in an original sequence are restored through Akima spline interpolation, and continuity and smoothness of a data structure are restored; and then constructing a joint objective function to minimize the permutation entropy of the candidate trend and maximize the Pearson correlation coefficient of the candidate trend and the original sequence, and then guiding the adaptive confirmation of the order and modal quantity parameters of a B spline in TVFEMD decomposition, thereby extracting a low-frequency trend component with the highest representation force, and realizing unsupervised and automatic trend extraction. Finally, the provided method can perform adaptive modeling under various time scales such as day, week, month, year and the like, outputs trend components of corresponding time scales, and can be applied to application scenes such as capacity planning, holiday and festival load analysis, energy-saving scheduling and service prediction in a cellular network. The method has the advantages of generalization, full-automatic parameter adjustment and the like, the trend extraction efficiency and stability of the cellular data are remarkably improved, and reliable trend information support is provided for intelligent operation and maintenance and decision optimization of the cellular data.

The technology described herein is directed towards accurate low-complexity decision tree-based traffic predictor models, such as decision tree regressor models for use by base stations. Each model is rapidly retrained per base station using data relevant to the base station. To improve accuracy, statistically significant feature data is selected by performing hypothesis testing on candidate features to filter out features that cannot satisfy a statistical significance threshold (e.g., p-value). The decision tree regressor model is recursively grown based on the selected features' feature values and their traffic level labels. Predicted traffic level data is determined by traversing the trained decision tree to reach a leaf node associated with the prediction data. Resource allocation can be based on the prediction. In addition to time-series training data, spatial training data can be used. Real time traffic monitoring by a radio unit for operating in an autonomous management mode is also facilitated.

The invention discloses a long-term traffic prediction method and system for a large-scale cellular network. The long-term traffic prediction method for the large-scale cellular network comprises the following steps: processing an original cellular traffic sequence; periodic signal extraction is carried out on the processed original cellular traffic sequence, and signal decomposition is carried out according to periodic signals; and performing long-time flow prediction according to a signal decomposition result. According to the method, the heterogeneous traffic sequences are clustered into homogeneous groups and are independently predicted, so that mutual interference among different distribution sequences is effectively eliminated, and the traffic prediction precision of the large-scale cellular network is remarkably improved.

The invention discloses a cellular network flow time sequence prediction method and system based on time-space-frequency fusion, and relates to the technical field of communication network intelligent analysis, and the method comprises the steps: carrying out the adaptive learning of an implicit association relationship between different cells based on a graph structure; performing frequency decomposition on a historical flow sequence to obtain dominant periodic components and high-frequency disturbance characteristics, performing joint modeling on spatial characteristics and frequency domain representation to generate a flow prediction value of a future time slice, and adopting an improved loss function in a training process to obtain a flow prediction value of the future time slice; therefore, the fluctuation characteristics of the cellular flow can be better adapted and the training stability of the model can be improved. According to the method, the spatial dependency relationship and the frequency domain structure of the cellular traffic can be described simultaneously, the accuracy and stability of traffic prediction are improved, and the method is suitable for complex network scenes such as multi-base-station collaborative prediction.

For example, an apparatus may be configured to cause wireless communication station (STA) to set a cellular Quality of Service (QoS) index value in a cellular QoS index field to indicate a predefined setting of a set of a plurality QoS parameters for a cellular QoS traffic flow to be communicated by the STA over a Wireless Local Area Network (WLAN). For example, the apparatus may be configured to cause the STA to transmit a Stream Classification Service (SCS) request to an Access Point (AP) of the WLAN, the SCS request including an SCS descriptor element, the SCS descriptor element including the cellular QoS index field.

Aspects of the subject disclosure may include, for example, a method in which a processing system obtains first cellular traffic data associated with an existing cell site of a communication network, first transportation data associated with a first geographic region including the existing cell site, and first household data regarding households located in the first geographic region; the first household data comprises cellular plan data, broadband plan data, demographic data and / or economic data. The processing system constructs a predictive model for cellular traffic applicable to a candidate cell site in a second geographic region, using the cellular traffic data and the first transportation data and / or the first household data. The processing system predicts second cellular traffic at the candidate cell site, using as inputs to the model second transportation data and second household data associated with the candidate cell site. Other embodiments are disclosed.