uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • 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
Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0001-5219-8248
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0003-0762-5743
Analyt & AI Grp, S-17062 Stockholm, Sweden.
2019 (English)In: IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, ISSN 2332-7731, Vol. 5, no 2, p. 382-399Article in journal (Refereed) Published
Abstract [en]

This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 5, no 2, p. 382-399
Keywords [en]
Energy harvesting, cognitive radio, multiple access channel, spectrum sensing, fading channel
National Category
Communication Systems Telecommunications Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-389809DOI: 10.1109/TCCN.2019.2908860ISI: 000471115000016OAI: oai:DiVA.org:uu-389809DiVA, id: diva2:1339698
Funder
Swedish Research Council, 2017-04053Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Biswas, SinchanDey, Subhrakanti

Search in DiVA

By author/editor
Biswas, SinchanDey, Subhrakanti
By organisation
Signals and Systems Group
Communication SystemsTelecommunicationsSignal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 12 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • 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