Improving Spectrum Efficiency in Heterogeneous Networks Using Granular Identification

  • Rohit SinghEmail author
  • Douglas Sicker
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 261)


Given the ever-increasing demand for wireless services and the pending explosion of the Internet of Things (IoT), demand for radio spectrum will only become more acute. Setting aside (but not ignoring) the need for additional allocations of spectrum, the existing spectrum needs to be used more efficiently so that it can meet the demand. Other than providing more spectrum there are other factors (like, transmit power, antenna angles, QoS, bandwidth, and others) that can be adjusted to cater to the demand and at the same time increase the spectrum efficiency. With heterogeneity and densification these factors are so varied it becomes necessary that we have some tool to monitor these factors so as to optimize our outcome. Here we propose a PHY layer granular identification that monitors the physical and logical parameters associated with a device/antenna. Through a simple optimization problem, we show how the proposed identification mechanism can further the cause of spectrum efficiency and ease coordination among devices in a heterogeneous network (HetNet) to assign resources more optimally. Compared to received signal strength (RSS) way of assigning resources the proposed approach shows a \(138\%\) to \(220\%\) increase (depending on the requested QoS) in spectrum efficiency. Ultimately, this research is aimed at assisting the regulators in addressing future spectrum related efficiency and enforcement issues.


Spectrum efficiency Identification Heterogeneous networks Spectrum sharing Optimization Radio resource management 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Engineering and Public PolicyCarnegie Mellon UniversityPittsburghUSA
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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