Kun Chao

Mobility load balancing aware radio resource allocation scheme for LTE-Advanced cellular networks

Mobility load balancing (MLB) is widely used to address the uneven load distribution problem. The basic idea is that a hot-spot cell selects less-loaded neighbouring cells as assistant cells, and thenshiftsitsedge users to assistant cells via the handover region adjustment. However, shifted users receive the reduced signal power after MLB, which may result in the poor link quality problem for shifted users. In order to deal with this problem, this paper proposes a mobility load balancing aware radio resource allocation (MLBRRA) scheme. In the MLBRRA scheme, the assistant cell jointly considers the MLB factor of the shifted user and that of the hot-spot cell, as well as the proportional fairness scheduling factor. More specifically, the assistant cell preferentially allocates radio resources to shifted users, which are suffering poor link quality or previously served by a hot-spot cell with large handover region. Simulation results show that the proposed MLBRRA scheme can effectively deal with the poor link quality problem in terms of handover failure and call dropping. The proposed scheme can also reduce the call blocking probability.

Self-optimised Coordinated Traffic Shifting Scheme for LTE Cellular Systems

Mobility load balancing is widely used in LTE cellular systems to deal with the uneven load distribution. Its basic idea is to shift traffic from a hot-spot cell to less-loaded neighbouring cells, called partners. Conventional schemes focus on the hot-spot cell’s load reduction and pay less attention to the performance of partners. This paper proposes a self-optimised coordinated traffic shifting scheme. In the proposed scheme, the coordination among partners is considered. Meanwhile, the shifted traffic is adjusted dynamically according to the load balancing (LB) performance. Simulation results show the proposed scheme can keep low call blocking probability of partners. It can also keep the number of Ping-Pong LB and the LB handover dropping probability at low levels.

Self-optimised joint traffic offloading in heterogeneous cellular networks

Traffic offloading is a widely used technique to address the unbalanced traffic distribution between pico cells and macro cells in heterogeneous cellular networks. However, the shifted users may result in the macro cell receiving large traffic from multiple pico cells and then becoming heavily loaded. This phenomenon is called the exacerbation problem in this paper. In order to address this problem and balance load effectively, this paper proposes a self-optimised joint traffic offloading (JTO) scheme. The JTO scheme jointly employs two traffic offloading techniques, including cell biasing technique to offload traffic between pico cell and macro cell, and mobility load balancing among macro cells. Simulation results show the JTO scheme can effectively deal with the cell exacerbation problem, in terms of handover failure and call dropping. The JTO scheme can also reduce the call blocking probability.

Mining target users for mobile advertising based on telecom big data

The mobile advertising industry in China has developed rapidly in recent years. Many companies and brands tend to employ mobile advertising in order to reach the target customers accurately. However, the conversion rates associated with the advertising campaigns are usually quite low due to the low quality of the datasets and impropriate predictive model. In this paper, we propose a novel mobile advertising system architecture based on telecom big data analytics. The defined multi-dimensional user portrait is introduced for user label oriented ad display strategy or as the basic database for the further user classification algorithm. We also adopt the widely used logistic regression algorithm in this paper to improve the target accuracy. The result of use case, which is calculated from the real-time collected cellular network data, also shows the superior performance of the proposed mobile advertising system.

Data Mining Based Modeling and Application of Mobile Video Service Awareness

With the popularity of 4G mobile networks, mobile video service becomes the key service in the 4G era. In order to improve users’ awareness and increase the network optimization efficiency, it is important to establish a scientific and accurate model to evaluate the video service from the user’s perception. In this paper, we focus on the video streaming traffic and propose a modelling approach to evaluate the video service performance. The essential characteristics of video traffic are taken into account. Based on the hierarchical clustering and the Pearson correlation coefficient method, key factors of video service perception are determined. Furthermore, the threshold values of key factors are obtained through extensive user surveys and simulation tests. The results of the application in the realistic network demonstrate the effectiveness of the proposed model. In addition, results show the proposed model enables the telecom operator to evaluate the video service quality of each user or user group, which helps improve the network optimization efficiency.

A Novel Architecture and Machine Learning Algorithm for Real Estate

The real estate industry is a hot topic and the factors of a house which affect the investment benefit is worth of research. This paper designs a novel machine learning assisted real estate industry investment guidance (MLRIG) architecture and a machine learning algorithm, aiming at researching the factors and their weight respectively of a house which have influence on its investment value. The MLRIG architecture is composed of 4 stages: Data collection, Data discretization, Data Mining Process and Factors weight output; the proposed machine learning algorithm, called QSFL-LR (Quantum-inspired Shuffled Frog Leaping Logistic Regression), combines Quantum-inspired Shuffled Frog algorithm with Logistic Regression to select the factors of a house which affect the investment value before data training, then output the weight of the factors respectively. Experiment shows the proposed QSFL-LR algorithm has better performance in accuracy and precision compared with traditional Logistic Regression, proving the superiority of QSFL-LR. The experiment also shows MLRIG architecture can guide both business companies and individuals to reduce investment risk in real estate industry.

Multi-index Evaluation Analysis of Region Network Development: A Cluster Empirical Study

The evaluation of region development of network has been a widespread concern in recent years. However, network development of a region, due to its specific characteristics and application scenario, should have a tailor-made evaluation system. In this study, taking into account various factors in multiple fields, a multiple-index evaluation system is established. Then, a principal component analysis-based K-means clustering approach is proposed to address the analyzing problem with an acceptable complexity. A simulation experiment is implemented to verify the algorithm. The results can be used to compare the different areas telecommunication networks, and provide rational and effective suggestions for network planning and construction.

Big Data Based Recommendation Scheme in APP Marketing Field

In the traditional marketing activities, the key to success is to identify the potential needs of target users. An excellent recommendation system can help improve the effectiveness and efficiency of the precision marketing. In this paper, an Apriori algorithm based recommendation scheme is proposed, and it’s then applied to the mobile APP marketing field. The scheme is realized through two processes, the frequent item sets generation process and the strong association rule generation process. Experimental results have shown that the scheme can achieve high advertising arrival rate, as well as superior exposure and click conversion rate. The effectiveness of the mobile APP product promotion has been improved dramatically.

Data Mining for Base Station Evaluation in LTE Cellular Systems

Effective base station (BS) evaluation can assist telecom operators to find problematic cells and optimize the system performance. This paper proposes a data mining based joint BS evaluation (JBSE) algorithm in LTE cellular systems. Initially, the JBSE algorithm considers four key factors, including the cell energy consumption, the cell revenue, the cells distribution induced interference, the high BS induced cross-boundary coverage. Then, the expert judgement matrix is employed to rank the level of each factor. Finally, the JBSE algorithm evaluates each cell comprehensively. The JBSE algorithm is used in the LTE systems evaluation of a city in China. It can find problematic cells effectively.

Big data based user clustering and influence power ranking

Telecom big data implies abundant user information. In this paper, it employs the telecom data and proposes a user clustering and influence power ranking scheme. The scheme is implemented through three stages, i.e. the user portrait analysis stage, the user clustering analysis stage and the ranking stage of user influence power. Experimental results have shown that, marketing promotion effectiveness based on this scheme has been improved significantly, while the advertising costs are also considerably reduced.