Publication: Quadrature permutation matrix modulation
| dc.contributor.department | Department of Electrical and Electronics Engineering | |
| dc.contributor.kuauthor | Özpoyraz, Burak | |
| dc.contributor.kuauthor | Başar, Ertuğrul | |
| dc.contributor.kuauthor | Aydın, Atalay | |
| dc.contributor.schoolcollegeinstitute | College of Engineering | |
| dc.date.accessioned | 2026-02-26T07:12:57Z | |
| dc.date.available | 2026-02-25 | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This paper introduces the in-phase and quadrature (IQ) extension to the permutation matrix modulation (PMM) technique and proposes the quadrature PMM (QPMM) scheme for higher spectral efficiency without any expense in RF chains. Assuming a single-user MIMO (SU-MIMO) system model with all antennas activated, the proposed QPMM scheme utilizes the permutation matrices as the spatial indexing unit for both the IQ components of the complex symbol vector. Furthermore, a low-complex detector, conditional maximum likelihood detector (C-MLD), that achieves the same bit error rate (BER) performance as the optimal joint MLD is presented. In addition to the low-complex C-MLD detector, a deep learning (DL) based detector, called FusionDet, is also proposed for the QPMM scheme. This DL-based detector provides a trade-off between complexity and BER performance. The BER performance of the QPMM scheme and the complexities of the detectors are examined. The provided computer simulations reveal that the proposed QPMM scheme outperforms the conventional PMM method for different MIMO setups and modulation levels. In addition, the complexity analysis shows that C-MLD attains the same BER performance as the optimal joint MLD with significantly lower complexity. Finally, FusionDet is observed to further decrease detector complexity in exchange for sub-optimal detection performance. | |
| dc.description.fulltext | Yes | |
| dc.description.harvestedfrom | Manual | |
| dc.description.indexedby | WOS | |
| dc.description.indexedby | Scopus | |
| dc.description.openaccess | Hybrid OA | |
| dc.description.peerreviewstatus | N/A | |
| dc.description.publisherscope | International | |
| dc.description.readpublish | N/A | |
| dc.description.sponsoredbyTubitakEu | N/A | |
| dc.description.version | N/A | |
| dc.identifier.doi | 10.1109/TWC.2025.3581483 | |
| dc.identifier.eissn | 1558-2248 | |
| dc.identifier.embargo | No | |
| dc.identifier.endpage | 118 | |
| dc.identifier.issn | 1536-1276 | |
| dc.identifier.quartile | Q1 | |
| dc.identifier.scopus | 2-s2.0-105009432320 | |
| dc.identifier.startpage | 107 | |
| dc.identifier.uri | https://doi.org/10.1109/TWC.2025.3581483 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14288/32477 | |
| dc.identifier.volume | 25 | |
| dc.identifier.wos | 001659564100048 | |
| dc.keywords | Receiving antennas | |
| dc.keywords | Transmitting antennas | |
| dc.keywords | Symbols | |
| dc.keywords | Detectors | |
| dc.keywords | Complexity theory | |
| dc.keywords | Multiple-input multiple-output (MIMO) | |
| dc.keywords | Modulation | |
| dc.keywords | Bit error rate (BER) | |
| dc.keywords | Vectors | |
| dc.keywords | Precoding | |
| dc.keywords | Index modulation (IM) | |
| dc.keywords | Deep learning (DL) | |
| dc.keywords | Neural network (NN) | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.affiliation | Koç University | |
| dc.relation.collection | Koç University Institutional Repository | |
| dc.relation.ispartof | IEEE Transactions on Wireless Communications | |
| dc.relation.openaccess | Yes | |
| dc.rights | CC BY-NC-ND (Attribution-NonCommercial-NoDerivs) | |
| dc.rights.uri | Attribution, Non-commercial, No Derivative Works (CC-BY-NC-ND) | |
| dc.subject | Engineering | |
| dc.subject | Telecommunications | |
| dc.title | Quadrature permutation matrix modulation | |
| dc.type | Journal Article | |
| dspace.entity.type | Publication | |
| relation.isOrgUnitOfPublication | 21598063-a7c5-420d-91ba-0cc9b2db0ea0 | |
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