Traffic Analysis of Mobile Broadband Networks

Abstract

Detailed knowledge about the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Several traffic classification approaches co-exist in the literature, but none of them performs well for all different application traffic types present in the Internet. In this study we compare and benchmark the currently known traffic classification methods on network traces captured in an operational 3G mobile network. Utilizing the experiences about the strengths and weaknesses of the existing approaches, a novel combined method is proposed aiming at improving the completeness and accuracy of classification. The novel method is based on a complex decision mechanism, which can provide appropriate identification for each different application type. As a main contribution, with the help of the new method it is shown that applications previously used only in fixed access networks may appear in mobile broadband environment.

Sparsity Controlled Random Multiple Access With Compressed Sensing

“…Even though they enable an AP to receive more than one packet at the same time, the number of required symbol durations m for identifying active users have to be greater than or equal to the total number of users k. This assumption leads to suboptimal performance when the number of active users is much smaller than k due to the relatively large amount of resources m ≥ k reserved for identifying just a small number of active users. Furthermore, according to a report on mobile traffic [6], the number of active users does not exceed 10% of the total number of users (i.e., potential users) even in a busyhour. This implies that if there are a lot of potential users, only a small portion of users have data to send with a high probability.…”

Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

“…In addition, the distribution of active users in the network is always sparse in practice. In fact, according to the mobile traffic statistics [16], the ratio of simultaneous active users in a wireless network does not exceed to 10% even in the busy hours. Consequently, the base station needs to identify active users in the system before decoding their data.…”

Tetherway

“…Traffic analysis has been used to detect network applications [36], online activities [43], behavior profiles of client systems [42] and properties of the encrypted network, namely routing and flows [13]. Furthermore, it was shown that traffic analysis could yield results even if the transferred packets are encrypted or timing is masked [13,41,22,6].…”