Modeling and performance analysis of unlicensed bands MAC strategy in multi-channel LTE-A networks with M2M/H2H coexistence

With the growing use of the machine-to-machine (M2M) communication and the unlicensed band by advanced long term evolution (LTE-A) networks, known as LTE unlicensed (LTE-U), demand for resource access strategy is rapidly increasing and has recently been attracting considerable attention of mobile operators. The requirement set by 3rd generation partnership project in the release 11 about LTE standards will allow LTE-U and other unlicensed band access technology to peacefully coexist and operate in the same unlicensed band. LTE-U supports not only the human-to-human (H2H) communication but also the M2M communication. In this paper, a new MAC protocol for LTE-U that allow friendly co-existence of H2H with M2M communications working in unlicensed bands is presented. The proposed MAC mechanisms is designed to ensure an efficient and fair channel access as well as enabling better H2H/M2M coexistence. The throughput performance of both H2H and M2M systems is evaluated analytically and by simulation. The impact of H2H/M2M transmissions periods and spectrum sensing time on the throughput performance of H2H and M2M systems are also studied.     

Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days. CTR prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client: The more clicks, the more successful the ad is. In this study we develop a machine learning based click through rate prediction model. The proposed study defines a model that generates accurate results with low computational power consumption. We used four classification techniques, namely K Nearest Neighbor (KNN), Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The study was performed on the Click-Through Rate Prediction Competition Dataset. It is a click-through data that is ordered chronologically and was collected over 10 days. Experimental results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features.