The Technical Committee on Communication Behavior Engineering addresses the research question “How do we construct a communication network system that includes users?”. The growth in highly functional networks and terminals has brought about greater diversity in users' lifestyles and freed people from the restrictions of time and place. Under this situation, the similarities of human behavior cause traffic aggregation and generate new problems in terms of the stabilization of network service quality. This paper summarizes previous studies relevant to communication behavior from a multidisciplinary perspective and discusses the research approach adopted by the Technical Committee on Communication Behavior Engineering.

Providing mobile services that ensure user satisfaction is one of the most crucial issues for telecommunication carriers and service providers. Traditionally, user satisfaction with the service, i.e., the Quality of Experience (QoE), has been assessed in a laboratory by using elaborate network systems and customized terminals. However, reliable QoE for a target mobile service in the context of actual use cannot be obtained by laboratory experiment, since QoE can be affected by a variety of factors such as environmental conditions and the mental state of the user. This paper proposes a novel Web script-based field evaluation method designed to ascertain user satisfaction with mobile services. The proposed method has the following practical advantages. Since experimental conditions, especially communication conditions, can be simulated with a Web script, such as FlashLite, a subjective assessment can be conducted anywhere using the mobile terminal of the participant. Thus, QoE for a target mobile service in the field can easily be obtained.

With the rapid growth of high performance ICT (Information Communication Technologies) devices such as smart phones and tablet PCs, multitasking has become one of the popular ways of using mobile devices. The reasons users have adopted multitask operation are that it reduces the level of dissatisfaction regarding waiting time and makes effective use of time by switching their attention from the waiting process to other content. This is a good solution to the problem of waiting; however, it may cause another problem, which is the increase in traffic volume due to the multiple applications being worked on simultaneously. Thus, an effective method to control throughput adapted to the multitasking situation is required. This paper proposes a transmission rate control method for web browsing that takes multitasking behavior into account and quantitatively demonstrates the effect of service by two different field experiments. The main contribution of this paper is to present a service design process for a new transmission rate control that takes into account human-network interaction based on the human-centered approach. We show that the degree of satisfaction in relation to waiting time did not degrade even when a field trial using a testbed showed that throughput of the background task was reduced by 40%.

As mobile multimedia services expand, user behavior will become more diverse and the control of service quality from the user's perspective will become more important in service design. The quality of the network is one of the critical factors determining mobile service quality. However, this has mainly been evaluated in objective physical terms, such as delay reduction and bandwidth expansion. It is less common to use a human-centered design viewpoint when improving network performance. In this paper, we discuss ways to improve the quality of web services using time-fillers that actively address the human factors to improve the subjective quality of a mobile network. A field experiment was conducted, using a prototype. The results of the field experiment show that time-fillers can significantly decrease user dissatisfaction with waiting, but that this effect is strongly influenced by user preferences concerning content. Based on these results, we discuss the design requirements for effective use of time-fillers.

This paper reports the field test performance of a base station (BS) equipped with a linear parallel multi-stage interference canceller for uplink of wideband CDMA in a suburban area. The field test was performed with one desired mobile station (MS) moving around the BS, and three fixed interfering MSs having a target SIR 11 dB higher than that of the desired MS. The field test clearly demonstrates that using interference cancellation with a proper suppression factor (or weighting factor), can reduce the transmission power of the desired MS by around 6 dB. This performance was also compared with calculated data and the interference canceller was verified to function as theoretically expected. The receive power capture ratio was estimated at around 96%. This paper therefore establishes that interference cancellation performance of CDMA systems in the field can be found by estimating the receive power capture ratio and the theoretical results of interference cancellation.

While online communities are important platforms for various social activities, many online communities fail to survive, which motivates researchers to investigate factors affecting the growth and survival of online communities. We comprehensively examine the effects of a wide variety of social network features on the growth and survival of communities in Reddit. We show that several social network features, including clique ratio, density, clustering coefficient, reciprocity and centralization, have significant effects on the survival of communities. In contrast, we also show that social network features examined in this paper only have weak effects on the growth of communities. Moreover, we conducted experiments predicting future growth and survival of online communities utilizing social network features as well as contents and activity features in the communities. The results show that prediction models utilizing social network features as well as contents and activity features achieve approximately 30% higher F1 measure, which evaluates the prediction accuracy, than the models only using contents and activity features. In contrast, it is also shown that social network features are not effective for predicting the growth of communities.