Compressed-Sensing Assisted Spatial Multiplexing Aided Spatial Modulation

Xiao, Lixia; Xiao, Yao; Xu, Chao; Lei, Xia; Yang, Ping; Li, Shaoqian; Hanzo, Lajos

doi:10.1109/twc.2017.2771759

Cited by 38 publications

(53 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it can be seen from these figures, the derived analytical expressions in (24) and (33) accurately describe the decoding complexity of the proposed m-M algorithm in both perfect and imperfect CSIR especially at high SNR values for determined, under-determined, and overdetermined MIMO-SM systems.…”

Section: B Analytical Complexity Assessmentmentioning

confidence: 76%

See 1 more Smart Citation

Optimum Low-Complexity Decoder for Spatial Modulation

Al-Nahhal

Başar

Dobre

et al. 2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: i) perfect channel state information at the receiver side (CSIR), ii) imperfect CSIR of a fixed channel estimation error variance, and iii) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared to SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared to the SM-ML decoder. Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions. Index TermsMultiple-input multiple-output (MIMO) systems, spatial modulation (SM), maximum likelihood (ML) decoder, sphere decoder (SD), low-complexity algorithms, complexity analysis. ). I. INTRODUCTIONMultiple-input multiple-output (MIMO) systems, which is an integral part of modern wireless communication standards, activate all transmit antennas to increase the spectral efficiency and/or improve the bit-error-ratio (BER) performance [2]. On the other hand, activating all transmit antennas at the same time not only creates a strong inter-channel interference (ICI) but also requires multiple radio frequency chains. A promising technique called spatial modulation (SM) has been studied in recent years [3]-[5] to overcome these problems in next-generation systems. In SM [6]-[9], only one transmit antenna is activated during the transmission burst, where the active transmit antenna is chosen out of all transmit antennas according to a part of the input bit-stream. The active antenna transmits a phase shift keying (PSK) or quadrature amplitude modulation (QAM) symbol, through a wireless medium, based on the rest of the input bit-stream. At the receiver side, all receive antennas receive the delivered signal and forward it to the digital signal processor (DSP) unit for decoding. The maximum-likelihood (ML) detector is utilized to decode the received signal by attempting all possible combinations of the QAM/PSK symbols and the transmit antennas, where this process depends on the number of transmit antennas, receive antennas, and modulation order. Consequently, the ML algorithm is classified to be costly from the decoding complexity point of view, particularly for increasing number of transmit/receive antennas and constellation points. Low-latency communications and energy-ef...

show abstract

Section: B Analytical Complexity Assessmentmentioning

confidence: 76%

“…In this subsection, we evaluate the accuracy of the analytical expressions for the expected decoding complexity of the m-M algorithm given in (24) and (33). As mentioned before, the number of visited nodes (VNs) is used as a measure for the decoding complexity of all algorithms in this paper.…”

Section: B Analytical Complexity Assessmentmentioning

confidence: 99%

Optimum Low-Complexity Decoder for Spatial Modulation

Al-Nahhal

Başar

Dobre

et al. 2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…The transmit antennas are split into subgroups, and the conventional SM is applied independently in each subgroup, allowing the simultaneous transmission of multiple signal symbols. [14][15][16] As in Castillo-Soria et al, 13 the required number of RF chains is equivalent to the number of antenna subgroups. An improved QSM (IQSM) transmits two signal symbols at each channel use, instead of only one as in QSM, to achieve higher spectral efficiency.…”

Section: Introductionmentioning

confidence: 97%

“…Another technique combines the features of spatial multiplexing (SMx) and SM to increase the spectral efficiency. The transmit antennas are split into subgroups, and the conventional SM is applied independently in each subgroup, allowing the simultaneous transmission of multiple signal symbols . As in Castillo‐Soria et al, the required number of RF chains is equivalent to the number of antenna subgroups.…”

Section: Introductionmentioning

confidence: 99%

Constellation design and performance analysis of the parallel spatial modulation

Mohaisen

2019

Int J Communication

View full text Add to dashboard Cite

Summary Spatial modulation (SM) is a relatively recent multiple‐input multiple‐output (MIMO) system in which information is carried by the index of the antenna used for transmission as well as by the conventional signal symbols. Several systems that build upon SM have since been proposed including the generalized SM (GSM), a variant of GSM with multiple active antennas (MA‐SM), quadrature SM (QSM), and parallel SM (PSM), among others. The PSM system can increase the spectral efficiency by splitting the antenna set into groups and applying SM independently in each group using the same signal symbol. In this paper, we first derive the upper bound on the error probability of the PSM. The search of the optimal constellation set is then formulated as a multi‐objective optimization problem, where the obtained constellation minimizes the asymptotic error probability. We conclude that as the number of antenna groups increases, the proposed constellation converges to the conventional phase‐shift keying at relatively low number of transmit antennas. The simulation results show that the proposed constellation outperforms conventional constellations by as much as 5 dB, for high‐modulation orders. Since the multi‐objective optimization is independent of the channel matrix, it can be easily done off‐line. This implies that these gains come at no complexity or delay cost.

show abstract

“…However, since the QSM scheme is a recently developed MIMO arrangement, how to exploit the diversity gain by involving the classic STBC structure has not been investigated. On the other hand, since the SM based MIMO schemes exhibit an inherent transmit symbol sparsity, Compressive Sensing (CS) has been invoked as an efficient detection tool of detecting the SM based MIMO symbols [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Compressive Sensing Assisted Space–Time Block Coded Quadrature Spatial Modulation

Xiao

et al. 2018

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

A novel Multiple-Input and Multiple-Output (MIMO) transmission scheme termed as Space-Time Block Coded Quadrature Spatial Modulation (STBC-QSM) is proposed. It amalgamates the concept of Quadrature Spatial Modulation (QSM) and Space-Time Block Coding (STBC) to exploit the diversity benefits of STBC relying on sparse Radio Frequency (RF) chains. In the proposed STBC-QSM scheme, the conventional constellation points of the STBC structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by conventional STBC blocks. Furthermore, an efficient Bayesian Compressive Sensing (BCS) algorithm is developed for our proposed STBC-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes. Moreover, the proposed BCS detector is capable of approaching the Maximum Likelihood (ML) detector's performance despite only imposing a complexity near similar to that of the Minimum Mean Square Error (MMSE) detector in the high Signal to Noise Ratio (SNR) regions.

show abstract

Compressed-Sensing Assisted Spatial Multiplexing Aided Spatial Modulation

Cited by 38 publications

References 44 publications

Optimum Low-Complexity Decoder for Spatial Modulation

Optimum Low-Complexity Decoder for Spatial Modulation

Constellation design and performance analysis of the parallel spatial modulation

Bayesian Compressive Sensing Assisted Space–Time Block Coded Quadrature Spatial Modulation

Contact Info

Product

Resources

About