CTN Issue: February 2013

1. Radio Resource Allocation in LTE-Advanced Cellular Networks with M2M Communications

The paper entitled “Radio Resource Allocation in LTE-Advanced Cellular Networks with M2M Communications” presents the Machine-to-Machine (M2M) communications as a means for providing ubiquitous connectivity between machines without the need of human intervention. To support a significantly large number of autonomous devices, the M2M system architecture needs to be extremely power and spectrum efficient, which may be addressed with the aid of the relevant radio resource allocation schemes. In this respect, the currently existing features of M2M services are outlined primarily for LTE-Advanced, including the relevant architectural enhancements proposed by the authors. In particular, when opposed to the traditional human-to- human (H2H) services, such as voice or web streaming, M2M services are shown to have very different requirements on a communication system due to their characteristics and given the large increase in the number of Machine-Type Communication (MTC) devices. Following, various radio resource allocation schemes are described and quantified in terms of their applicability to LTE-Advanced cellular networks with the aim of minimising co-channel interference and maximising network efficiency, and then supported with system-level simulation results demonstrating that the proposed schemes can improve the network performance in terms of user utility. The paper concludes that M2M communications are to be an emerging technology facilitating the deployment of the Internet of Things (IoT) concept by means of, among others, the aforementioned cellular technology.

Title and author(s) of the original paper in IEEE Xplore:
Title: Radio Resource Allocation in LTE-Advanced Cellular Networks with M2M Communications
Author: Kan Zheng, Fanglong Hu, Wenbo Wang, Wei Xiang, and Mischa Dohler
This paper appears in: IEEE Communications Magazine
Issue Date: July 2012

2. Cognitive Network Interference

With the emergence of new wireless applications and devices in the last ten years, there has been a drastic increase in the demand for radio spectrum. Cognitive radio techniques for opportunistic spectrum access are a promising solution to efficiently share the spectrum. Radio devices with cognitive capabilities can learn from the environment spatial and temporal utilization status of the radio spectrum and opportunistically exploit underutilized resources if doing so does not cause interference for other systems. Several off-the-shelf products like ZigBee nodes or WiFi access points use rudimentary cognitive radio techniques by implementing the carrier sensing multiple access (CSMA) protocol. More recently, new cognitive radio techniques have been proposed to enable the deployment of small-cell base stations to provide access to the mobile radio network where the primary (macro-cell) network can't provide the service. However, spectrum sharing is challenging since it creates interference from an unknown number of nodes randomly scattered in the network. This article introduces a new statistical model for cognitive network interference (CNI) based on the theory of truncated-stable distributions. The model accounts for sensing procedures, spectrum reuse protocols, and environment-dependent conditions such as path loss, shadowing, and channel fading. This provides an accurate characterization of CNI in realistic environments, making the model very attractive for operators deploying efficient mobile networks (e.g., heterogeneous networks and small cells), industry developing new wireless applications (e.g., internet of things and smart grids), and regulators planning modern spectrum utilization (e.g., cognitive radio and white space technology). This paper is the winner of The IEEE Communications Society William R. Bennett Prize in the Field of Communications Networking, 2012.

Title and author(s) of the original paper in IEEE Xplore:
Title: Cognitive Network Interference
Author: Alberto Rabbachin, Tony Q.S. Quek, Hyundong Shin, and Moe Z. Win
This paper appears in: IEEE Journal on Selected Areas in Communications
Issue Date: February 2011

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