Low-Complexity Signal Detection for Large-Scale Quadrature Spatial Modulation Systems

Xiao, Lixia; Yang, Ping; Fan, Shangchun; Li, Shaoqian; Song, Lijun

doi:10.1109/lcomm.2016.2602210

Cited by 27 publications

(22 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To remove the dependency of (20) on the instantaneous value of R m-M , an expectation over the pdf of R m-M should be calculated. (15) can be written in terms of its real and imaginary components as…”

Section: A Perfect Channel State Information At the Receivermentioning

confidence: 99%

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: A Perfect Channel State Information At the Receivermentioning

confidence: 99%

“…This algorithm requires an exhaustive pre-processing step to calculate the pseudo-inverse of the channel matrix columns. This step is mitigated in [15] by considering a sparse channel of a large-scale MIMO system. However, the problem of noise variance dependency still exists in [15].…”

mentioning

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

“…In recent works, some low complexity algorithms have been presented for MIMO, SM, and QSM signal detection [10,13,19]. In the works presented by [14,16], optimization algorithms and trigonometric functions were required in the receiver to detect the most likely antenna combinations.…”

Section: Low Complexity Detection Algorithmmentioning

confidence: 99%

“…Some previous works have analyzed this problem for basic QSM transmission schemes. For example, low complexity QSM detectors that consider compressive sensing [13,14], sphere decoding [15], minimum mean squared error (MMSE) [16], equivalent maximum likelihood [17], and zero forcing precoding [18] based low complexity detectors have been recently proposed. However, to the best of our knowledge, practical implementations of hardware architectures for the detection process in MIMO-QSM systems have not been discussed.…”

Section: Introductionmentioning

confidence: 99%

Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

et al. 2019

View full text Add to dashboard Cite

This paper presents a proposal for an architecture in FPGA for the implementation of a low complexity near maximum likelihood (Near-ML) detection algorithm for a multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. Our proposal was verified in the context of a MIMO receiver. The effects of the finite length arithmetic and limited precision were evaluated in terms of their impact on the receiver bit error rate (BER). We defined the minimum fixed point word size required not to impact performance adversely for n T transmit antennas and n R receive antennas. The results showed that the proposal performed very near to optimal with the advantage of a meaningful reduction in the complexity of the receiver. The performance analysis of the proposed detector of the MIMO receiver under these conditions showed a strong robustness on the numerical precision, which allowed having a receiver performance very close to that obtained with floating point arithmetic in terms of BER; therefore, we believe this architecture can be an attractive candidate for its implementation in current communications standards.

show abstract

“…Recently, the QSM design efforts have been mainly focused on the performance analysis [6]- [7], on its coherent vs non-coherent detection [8]- [9], as well as on its extension to multi-carrier transmission [10]. In order to exploit the diversity gain of the QSM and SM system, the diversity oriented QSM and SM designs were investigated in [11]- [14].…”

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

Low-Complexity Signal Detection for Large-Scale Quadrature Spatial Modulation Systems

Cited by 27 publications

References 18 publications

Optimum Low-Complexity Decoder for Spatial Modulation

Optimum Low-Complexity Decoder for Spatial Modulation

Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

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

Contact Info

Product

Resources

About