Sense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks

Seunghwan Kim, Han Cha, Jeemin Kim, Seung Woo Ko, Seong Lyun Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is important to explore whether spectrum access opportunities are available, but conventional CR based on transmitter (TX) sensing cannot be used to this end because the paired receiver (RX) may experience different levels of interference, according to the extent of their separation, blockages, and beam directions. To address this problem, this paper proposes a novel form of medium access control (MAC) termed sense-and-predict (SaP), whereby each secondary TX predicts the interference level at the RX based on the sensed interference at the TX; this can be quantified in terms of a spatial interference correlation between the two locations. Using stochastic geometry, the spatial interference correlation can be expressed in the form of a conditional coverage probability, such that the signal-to-interference ratio at the RX is no less than a predetermined threshold given the sensed interference at the TX, defined as an opportunistic probability (OP). The secondary TX randomly accesses the spectrum depending on OP. We optimize the SaP framework to maximize the area spectral efficiencies (ASEs) of secondary networks while guaranteeing the service quality of the primary networks. The testbed experiments using universal software radio peripheral (USRP) and MATLAB simulations show that SaP affords higher ASEs compared with CR without prediction.

Original languageEnglish
Article number8681732
Pages (from-to)2777-2793
Number of pages17
JournalIEEE Transactions on Wireless Communications
Volume18
Issue number5
DOIs
Publication statusPublished - 2019 May

Bibliographical note

Funding Information:
Manuscript received October 3, 2018; revised January 28, 2019; accepted March 18, 2019. Date of publication April 4, 2019; date of current version May 8, 2019. This work was supported in part by the Institute for Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government (MSIT) under Grant 2018-0-00923 (Scalable Spectrum Sensing for Beyond 5G Communication) and in part by IITP Grant funded by the Korea Government (MSIT) under Grant 2018-0-00170 (Virtual Presence in Moving Objects through 5G). This paper was presented in part at the IEEE DySPAN 2017 [2]. The associate editor coordinating the review of this paper and approving it for publication was J. M. Romero-Jerez. (Corresponding author: Seung-Woo Ko.) S. Kim, H. Cha, and S.-L. Kim are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: shkim@ramo.yonsei.ac.kr; chan@ramo.yonsei.ac.kr; slkim@yonsei.ac.kr).

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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