Abstract
This paper focuses on proposing a new access barring scheme for internet of things (IoT) devices in long term evolution advanced (LTE/LTE-A) and 5G networks. Massive number of IoT devices communicating simultaneously is one of the hallmarks of the future communication networks such as 5G and beyond. The problem of congestion also comes with this massive communication for which access barring is one of the solutions. So, it is required that sophisticated access barring techniques are designed such that the congestion is avoided and these devices get served in less time. Legacy access barring schemes like access class barring (ACB) and extended access barring (EAB) suffer from high energy consumption and high access delay respectively. However, our proposed scheme provides less energy consumption than ACB while giving less access delay than EAB. The proposed scheme maximizes the success probability while reducing the number of collisions at the same time. The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles. Extensive Matlab simulations are performed to validate our claims and analysis.
Highlights
Internet of things (IoT) is one of the technologies to shape the future in communications [1].Huge number of devices are expected to communicate autonomously to each other, or to the server.It is shown in Figure 1 that according to [2], the count of devices that desire to connect to the network will increase to in excess of 75 billion by the end of 2025
The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles
In this paper we discuss combined access barring (CAB) scheme to deal with the inherent issues of access class barring (ACB) and extended access barring (EAB)
Summary
Internet of things (IoT) is one of the technologies to shape the future in communications [1]. In [8], the authors propose the methodology to obtain the optimal ACB factor that rely on the number of active devices in the network. In [10], the authors used fixed ACB factor, i.e., they assume fixed number of devices in the network. In [12], the authors use the information on collided preambles for the last three slots, and use heuristics in devising algorithm to find the number of devices, and in turn the optimal transmission probability. In [13] the authors use information on success and idle preambles and employ pseudo Bayesian algorithm to find the number of devices in the network. CAB presents less collision probability than ACB and less access delay than.
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