Space-Frequency Shift Keying in Rapidly Time-Varying MIMO OFDM Channels

Li, Zhenzhou; Zheng, Jianping

doi:10.1109/vtcspring.2018.8417812

Cited by 3 publications

(3 citation statements)

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“…Inspired by the aforementioned advantages, the space shift keying (SSK) concept was introduced in [12], [13] as a low-complexity implementation of SM, where only the TA indices convey information. As a further advance, the principle of SM has also been extended to the time domain, resulting in the space-time shift keying (STSK) [14]- [17], to the frequency domain yielding the space-frequency shift keying (SFSK) [18] and to the time-frequency domain creating the space-time-frequency shift keying (STFSK) [19], [20] to exploit all three degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Improved-Throughput Generalised Spatial Modulation: Bit-to-Symbol Mapping, Detection and Performance Analysis

An¹,

Liu

et al. 2021

Preprint

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Low-complexity improved-throughput generalised spatial modulation (LCIT-GSM) is proposed.More explicitly, in GSM, extra information bits are conveyed implicitly by activating a fixed number out of transmit antennas (TAs) at a time. As a result, GSM has the advantage of a reduced number of radio-frequency (RF) chains and reduced inter-antenna interference (IAI) at the cost of a lower throughput than its multiplexing-oriented full-RF based counterparts. Variable-GSM mitigates this throughput reduction by incorporating all possible TA activation patterns associated with a variable value ranging from 1 to during a single channel-use, which maximises the throughput of GSM but suffers a high complexity of the mapping book design and demodulation. In order to mitigate the complexity, first of all, we propose two efficient schemes for mapping the information bits to the TA activation patterns, which can be readily scaled to massive MIMO setups. Secondly, in the absence of IAI, we derive a pair of low-complexity near-optimal detectors, one of them has a reduced search scope, while the other benefits from a decoupled single-stream based signal detection algorithm. Finally, the performance of the proposed LCIT-GSM system is characterised by the error probability upper bound (UB). Our Monte Carlo based simulation results confirm the improved error performance of our proposed scheme, despite its reduced signal detection complexity.

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Section: Introductionmentioning

confidence: 99%

Low-Complexity Improved-Throughput Generalised Spatial Modulation: Bit-to-Symbol Mapping, Detection and Performance Analysis

An¹,

Liu

et al. 2021

Preprint

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“…Furthermore, several researchers considered the design of Multi-Dimensional Index Modulation (MIM) relying on both the Spatial Domain (SpD) and FD. For example, Space-Frequency Shift Keying (SFSK) [17] relies on an SFSK Dispersion Matrix (DM), which achieves beneficial transmit diversity in rapidly time-varying channels. Space-Time Shift Keying (STSK) constitutes another multi-functional MIMO technique in the family of MIM.…”

mentioning

confidence: 99%

“…However, this leads to a substantial increase in detection complexity for conventional methods, such as the ML detector. c) Coded Joint Index Mapping: Another way of further increasing the transmit diversity is to employ coded index mapping, where we use a circular shift based design of the DMs, which was proposed for SFSK in [17]. In this method, the number of active subcarriers in each column is n q , with N q − n q inactive subcarriers, where N q is the column length of D q .…”

mentioning

confidence: 99%

Near-Instantaneously Adaptive Learning-Assisted and Compressed Sensing-Aided Joint Multi-Dimensional Index Modulation

Feng,

El-Hajjar,

et al. 2023

IEEE Open J. Veh. Technol.

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Index Modulation (IM) is capable of striking an at-1 tractive performance, throughput and complexity trade-off. The 2 concept of Multi-dimensional IM (MIM) combines the benefits 3 of IM in multiple dimensions, including the space and frequency 4 dimensions. On the other hand, IM has also been combined with 5 compressed sensing (CS) for attaining an improved throughput. 6 In this paper, we propose Joint MIM (JMIM) that can utilize the 7 time-, space-and frequency-dimensions in order to increase the 8 IM mapping design flexibility. Explicitly, this is the first paper 9 developing a jointly designed MIM architecture combined with 10 CS. Three different JMIM mapping methods are proposed for 11 a space-and frequency-domain aided JMIM system, which can 12 attain different throughput and diversity gains. Then, we extend 13 the proposed JMIM design to three dimensions by combining 14 it with the time domain. Additionally, to circumvent the high 15 detection complexity of the proposed CS-aided JMIM design, we 16 propose Deep Learning (DL) based detection. Both Hard-Decision 17 (HD) as well as Soft-Decision (SD) detection are conceived. 18 Additionally, we investigate the adaptive design of the proposed 19 CS-aided JMIM system, where a learning-based adaptive mod-20 ulation configuration method is applied. Our simulation results 21 demonstrate that the proposed CS-aided JMIM (CS-JMIM) is 22 capable of outperforming its CS-aided separate-domain MIM 23 counterpart. Furthermore, the learning-aided adaptive scheme 24 is capable of increasing the throughput while maintaining the 25 required error probability target.

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