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Radio Resource Management Algorithms for D2D Communications With Limited Channel State Information
KTH, School of Information and Communication Technology (ICT).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Network assisted Device-to-Device (D2D) communication has the potential benefits of increasing system capacity, energy efficiency and achievable peak rates while reducing the end-to-end latency. To realize these gains, recent works have proposed power control (PC) and resource allocation (RA) schemes that show near optimal performance in terms of spectral or energy efficiency. Unfortunately, these schemes assume perfect and instantaneous access to either large scale or small scale channel state information (CSI) at some central entity. Obviously, this assumption does not hold in practical implementations and we therefore investigate the performance of D2D communications with limited CSI.

First, we analyze existing power control (PC), mode selection (MS) and resource allocation (RA) approaches in terms of the required input parameters, focusing on large scale fading. Then we build up a model in a system simulator to capture the impact of unavailability or CSI errors on the performance of PC, MS and RA algorithms.

Through simulations, we find that with proper algorithms, the system gains continuously from having more CSI knowledge. Specially, with additional CSI, the newly implemented Binary Power Control and Matching Allocation increases the throughput impressively with low complexity and proper fairness between D2D layer and cellular layer.

Furthermore, we investigate the impact of errors in the channel gains. Simulation results demonstrate that a certain user may suffer or benefit from the errors, however, the system performance is insensitive to the small scale errors. Numerical results also show errors of asymmetric range cause relatively more notable impact than the symmetric errors.

Place, publisher, year, edition, pages
2015. , 78 p.
Series
TRITA-ICT-EX, 2015:107
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-177494OAI: oai:DiVA.org:kth-177494DiVA: diva2:873054
Examiners
Available from: 2015-11-25 Created: 2015-11-23 Last updated: 2018-01-10Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf