No abstract

Multi-band and multi-tier network densification is being considered as the most promising solution to overcome the capacity crunch problem of cellular networks. In this direction, small cells (SCs) are being deployed within the macro cell (MC) coverage, to off-load some of the users associated with the MCs. This deployment scenario raises several problems. Among others, signalling overhead and mobility management will become critical considerations. Frequent handovers (HOs) in ultra dense SC deployments could lead to a dramatic increase in signalling overhead. This suggests a paradigm shift towards a signalling conscious cellular architecture with smart mobility management. In this regards, the control/data separation architecture (CDSA) with dual connectivity is being considered for the future radio access. Considering the CDSA as the radio access network (RAN) architecture, we quantify the reduction in HO signalling w.r.t. the conventional approach. We develop analytical models which compare the signalling generated during various HO scenarios in the CDSA and conventionally deployed networks. New parameters are introduced which can with optimum value significantly reduce the HO signalling load. The derived model includes HO success and HO failure scenarios along with specific derivations for continuous and non-continuous mobility users. Numerical results show promising CDSA gains in terms of saving in HO signalling overhead.Index Terms-Cellular networks; control data separation architecture; dual connectivity handover; signalling load; radio access networks.

In this paper the authors present an analytical model that -compared to previously published work -more accurately captures the delay of IEEE 802.11 protocol under low, medium, and near-saturation load conditions. A Markov chain is used to keep track of the instantaneous number of (active) nodes that have a frame to transmit. One advantage of the proposed analytical model is its ability to estimate the IEEE 802.11 protocol latency and delivery ratio in the presence of quality of service (QoS) classes, each class being defined by a specific maximum retransmission count. Such QoS classes can be adopted to support real time applications for which both latency and delivery ratio must be closely monitored for satisfactory operation.

No abstract

In order to meet the challenges of ambitious capacity, user experience, and resource efficiency gains, the next‐generation cellular networks need to leverage end‐to‐end user and network behavior intelligence. This intelligence can be gathered from the mobile network big data which includes the massive telemetric data about network health and status as well as data about user whereabouts, preferences, context, and mobility patterns. As a result, exploitation of big data on wireless cellular network is emerging as an indispensable approach for harnessing intelligence in future wireless communication networks. In this article, we first identify and classify the big data that can be gathered from different layers and ends of a wireless cellular network. We then discuss several new utilities of the big data that can bridge the existing gaps to meet 5G requirements. After that we summarize the existing literature on data analytics for cellular network performance. We present different platforms and two different frameworks to implement big data analytic‐based solutions in 5G and beyond and compare their pros and cons. We then discuss how key performance indicators (KPIs)‐based data collection may not suffice in 5G. Through an exemplary study, we show how to unleash the full potential hidden within the big data, granularity of low‐level performance indicators, and how context is essential. Finally, we highlight the opportunities that can be availed from big data in cellular network and the challenges therein.