Compressed-Sensing-Aided Space-Time Frequency Index Modulation

In space-time shift keying (STSK), the information is conveyed by both the spatial and time dimensions, which can be used to strike a tradeoff between the diversity and multiplexing gains. On the other hand, orthogonal frequency-division multiplexing (OFDM) relying on index modulation (IM) conveys i...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2018-07, Vol.67 (7), p.6259-6271
Main Authors: Siyao Lu, Hemadeh, Ibrahim A., El-Hajjar, Mohammed, Hanzo, Lajos
Format: Article
Language:English
Subjects:
channel coding
Codes
Complexity
compressed sensing (CS)
Constellations
coordinate interleave (CI)
Decoding
Detectors
Dispersion
Diversity methods
Frequency division multiplexing
frequency index modulation
Frequency modulation
frequency-selective fading
Indexes
Keying
maximum likelihood detection
MIMO
Modulation
OFDM
Orthogonal Frequency Division Multiplexing
reduced-complexity detector
soft detection
space-time shift keying (STSK)
Spacetime
Subcarriers
Time compression
Tradeoffs
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Summary:In space-time shift keying (STSK), the information is conveyed by both the spatial and time dimensions, which can be used to strike a tradeoff between the diversity and multiplexing gains. On the other hand, orthogonal frequency-division multiplexing (OFDM) relying on index modulation (IM) conveys information not only by the conventional signal constellations as in classical OFDM, but also by the indices of the subcarriers. In this paper, we combine the benefits of STSK and IM in order to strike a flexible tradeoff between the throughput and bit-error performance by transmitting extra information bits in each subcarrier block, while increasing the diversity order. In order to further improve this tradeoff, as well as to decrease the complexity of the detector, compressed sensing is combined with the proposed STSK-aided IM system. We first present the maximum likelihood (ML) detection, which forms the best-case bound on the proposed system's performance. Then, we propose a pair of reduced-complexity detection algorithms capable of approaching the ML detector's performance. Furthermore, in order to attain a near capacity performance, we propose a soft-input soft-output decoder that is capable of exchanging soft information with a channel decoder through iterative decoding.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2018.2818298