Mingqiang Yuan

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.

A survey of security and privacy in big data

Big data has been arising a growing interest in both scientific and industrial fields for its potential value. However, before employing big data technology into massive applications, a basic but also principle topic should be investigated: security and privacy. In this paper, the recent research and development on security and privacy in big data is surveyed. First, the effects of characteristics of big data on information security and privacy are described. Then, topics and issues on security are discussed and reviewed. Further, privacy-preserving trajectory data publishing is studied due to its future utilization, especially in telecom operation.

Big data assisted customer analysis and advertising architecture for real estate

Advertising delivery is the key in the real estate industry. This paper proposes a big data assisted customer analysis and advertising (BDCAA) architecture. Its aim is to precisely seek potential users and improve the efficiency of advertisement delivery. The proposed BDCAA architecture consists of three stages, including user 360-degree portrait and users segmentation, potential customer mining, precise advertising delivery. Experiment shows the BDCAA architecture can reach high advertising arrival rate, as well as superior advertising exposure/click conversion rate.

A novel cluster algorithm for telecom customer segmentation

With the rapid development of the telecom market, telecom customer gradually shows the characteristics of differentiation and diversification. Telecom customer clustering is an effective method for marketing and retention. In this paper, we propose a cluster algorithm based on k-means and Multivariable Quantum Shuffled Frog Leaping Algorithm (MQSFLA), called MQSFLA-k, which can be used as a customer segmentation method in telecom customers marketing. Simulation results show that the proposed MQSFLA has advantages of both convergence rate and convergence accurate value compared with other intelligent algorithms. In addition, the proposed MQSFLA based MQSFLA-k has the advantage of convergence rate compared with k-means. Furthermore, MQSFLA-k can solve the problem of telecom customer segmentation effectively, which provides target customers for retention.

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.

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.

Crowdfunding Assisted Cellular System Analysis and Application

In this paper, a novel crowdfunding assisted cellular system analytical (CCSA) scheme is designed. Its basic idea is that mobile terminals collect cellular system related data from different telecom operators continuously, including signal strength, signal quality, data rate, delay etc. Mobile terminals automatically report the collected data to the analytical system periodically. Then, the analytical system analyses the performance and competitiveness level among telecom operators, as well as seeking the problem area for each telecom operator. Compared to driving test (DT) and call quality test (CQT), the CCSA scheme can save the capital expenditure (CAPEX) and effectively analyse the user experience in the cellular system.

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.