This paper provides a review of various inter carrier interference cancellation methods used in Orthogonal Frequency Division Multiplexing (OFDM) in the downlink of Long Term Evolution (LTE) and in many other applications. It contains the analysis of different methods, their explanations, areas of implementation, advantages, drawbacks and finally a comparison of the methods has been made. The paper is based on the fact that during OFDM reception, the overlapping frequencies interfere due to various reasons and different methods have to be applied to avoid interference among the frequencies for a proper reception at the receiver.

With drastic increase in internet traffic over last few years due to increase in number of internet users, IP traffic classification has gained significant importance for research community as well as various internet service providers for optimization of their network performance and for governmental intelligence organizations. Today, traditional IP traffic classification techniques such as port number and payload based direct packet inspection techniques are rarely used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for IP traffic classification. In this research paper, a real time internet traffic dataset has been developed using packet capturing tool for 2 second packet capturing duration and other datasets have been developed by reducing number of features of 2 second duration dataset using Correlation and Consistency based Feature Selection (FS) Algorithms. Then, five ML algorithms MLP, RBF, C4.5, Bayes Net and Naïve Bayes are employed for IP traffic classification with these datasets. This experimental analysis shows that Bayes Net is an effective ML technique for near real time and online IP traffic classification with reduction in packet capture duration and reduction in number of features characterizing each application sample with Correlation based FS Algorithm.