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The demand for high-speed data communication and the use of real-time applications in mobile networks are becoming increasingly high, and communication systems that specialize in downlink packet transfer are being developed and deployed. In these high-speed downlink packet networks, scheduling at the base station is one of the key technologies to accomplish such high-speed. A scheduling algorithm that is based on the proportional fair criterion is widely used in commercial networks, but the problem with such algorithm is that it does not schedule real-time application's traffic efficiently. This paper proposes a new scheduling algorithm for real-time applications that does not need substantial change in the existing system and coexists efficiently with the proportional fair criterion. We evaluate the characteristics of the proposed algorithm through computer simulation and show that the efficiency of the proposed algorithm is better than that of existing algorithms.

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This paper proposes a novel approach to separating background (BG) and foreground (FG) traffic based on periodicity analysis. As BG traffic is commonly periodically generated by applications, this trait is leveraged to effectively detect BG traffic. Concretely, the Period Candidate Array (PCA) approach is proposed to extract only necessary information from long and sparse traffic flows, hence quickly detects the flows' periodicity with low computational cost. The PCA works directly with "on-site" traffic without depending on historical data as in machine learning methods. As a result, the proposed approach can be immediately applied to the real world traffic management systems. In addition, the PCA properly works with latencyincluded traffic affected by network delays. Experimental results reveal the effectiveness and efficiency of the PCA compared to the conventional methods in terms of computational cost, memory usage, and independence to historical data. 978-1-4799-5952-5/15/$31.00 ©2015 IEEE II. RELATED WORK BG traffic detection is an emerging topic in network traffic analysis and management [6], [7], [8] aiming at improving energy efficiency [1], [3] and communication quality [4], [6]. BG traffic is traffic generated by devices to maintain their network connectivity such as network management packets (ARP, DHCP, IMCP,. . . ), network service handshake (Net-BIOS, DNS,. . . ), or applications heartbeats (Windows update, Yahoo News,. . . ) [1], [3], [4]. In contrast, FG traffic is generated by users on real communications such as web-surfing. As BG traffic is not immediately useful for users, it can be delayed to some extend to save the device (battery, memory, computational capability,. . . ) or network resources (bandwidth, radio channel,. . . ), specifically at critical times.User activity analysis is a suitable approach to improve QoS and QoE of mobile phone based network services. Net-Sense [9] and LiveLabs [10] are two interesting live projects that study user activities on smartphones including social networking, location-based services, screen state, etc. User activity involvement inferred from the screen state (on/off) is associated with traffic generated/received by the device's network interface to detect BG traffic [3], [4]. The essential issue of this method is that a specific application must be installed in the UE leading to user stressfulness, requiring more resources and computational cost. Moreover, BG traffic detected on UEs would be only useful for optimizations on individual devices (e.g., battery saving). It would be far to utilize this detection to manage the network traffic since it needs further information about the network load and the amount of BG traffic on the whole network. This work resolves this issue by a novel solution at the network edges to quickly and effectively capture, analyze and optimize a large amount of network traffic, without imposing any policy on the UE.

This paper proposes autonomous data transmission using off-peak hours for cellular-based machine-to-machine (M2M) communications. In the near future, there will be an enormous number of machine type terminals (MTs) capable of connecting to cellular networks. Thus, an efficient scheme for accommodating M2M traffic in existing cellular networks is required. To tackle this challenge, we focus on the fact that there are a lot of delay-insensitive services in M2M communications. The goal of the proposed method is to shift such traffic to off-peak hours in a scalable, flexible and simple manner. We evaluate the proposed method through a field experiment and a computer simulation using traffic data obtained from a commercial cellular network.

With the widespread of terminals, such as mobile phones, equipped with both Digital TV reception capability and Internet connectivity, interactive TV services are becoming more popular and useful. This paper analyses the control accuracy of the access control method proposed for interactive TV services. The proposed method performs access control by utilizing random sampling and estimation. Therefore, error during control is somewhat inevitable and the accuracy of control becomes a problem. This paper analyses the cause of the various types of error expected while adopting the proposed method. The control accuracy and the volume of error of the proposed method are evaluated through numerical calculation. The obtained results quantitatively demonstrate the maximum values and the range of the control error when the proposed method is applied. Based on the obtained insights and results, adequate settings of control parameters and capacity planning of the system providing interactive TV services are illustrated.