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Dynamic spatial modulation for next generation networks
Gudla V.V.,
Published in Elsevier B.V.
2019
Volume: 34
   
Pages: 90 - 104
Abstract
A major challenge for fifth generation (5G) wireless communication, which is expected to be deployed around 2020 is to provide 10 times higher spectral efficiency and energy efficiency than fourth generation (4G). Spatial modulation (SM) is a novel multiple input and multiple output (MIMO) technique, which provides an exceptional trade-off between spectral efficiency and energy efficiency. All the conventional SM variants do not perform well under spatially correlated channel conditions. Hence, they cannot be employed for indoor wireless local area network (WLAN) and compact wireless mobile applications, where the effect of spatial correlation is imminent. In this paper, a new SM variant, dynamic spatial modulation (DSM) is introduced for 4×4 and 8×8 MIMO configurations, which activates either one or two transmit antennas based on input bit pattern. DSM employs dynamic mapping, which maximizes the Euclidean distance between active spatial bits by choosing antenna subsets with maximum spatial separation to minimize the effect of spatial correlation. The average bit error rate (ABER) performance of DSM under uncorrelated and spatially correlated channel conditions is quantified in comparison with conventional redesigned spatial modulation (ReSM). Later, a low complexity, suboptimal, ordered weight minimum mean square error based conditional maximum likelihood search (OWMMSE-CML) detection scheme is proposed for the DSM system. Through extensive simulations under various channel conditions and complexity analysis, it is shown that proposed OWMMSE-CML detection scheme offers performance nearer to optimal ML and also outperforms suboptimal signal vector based minimum mean square error (SVMMSE) detection scheme with a significant reduction in computational complexity. © 2019 Elsevier B.V.
About the journal
JournalData powered by TypesetPhysical Communication
PublisherData powered by TypesetElsevier B.V.
ISSN18744907
Open AccessNo