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Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of M -QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes. [ABSTRACT FROM AUTHOR]

Summary: Fifth‐generation (5G) networks are expected to offer unprecedented amounts of spectral and energy efficiency. Spatial modulation (SM) techniques have the capability to achieve an appealing trade‐off between these conflicting design parameters. Fully generalized SM (FGSM) and fully quadrature SM (FQSM) are the recent high‐rate SM variants, where the spectral efficiency is linearly proportional to the number of transmit antennas. The transmit antenna selection schemes can efficiently improve the average bit error rate (ABER) performance of SM techniques. The main objective of this work is to investigate the employment of transmit antenna selection schemes to FGSM and FQSM. Initially, Euclidean distance optimized antenna selection (EDAS) scheme is employed to FGSM and FQSM. It offers superior performance than conventional FGSM/FQSM without transmit antenna selection at the cost of higher computational complexity. In order to reduce the complexity cost, four suboptimal schemes based on channel capacity, correlation and combination of them are proposed and employed to FGSM and FQSM. The suboptimal transmit antenna selection based on capacity and correlation (TAS‐A‐C) offers a minimum gain of ∼6 dB over conventional FGSM/FQSM with significantly lower complexity. In addition, the performance of all schemes is investigated for increased number of available transmit antennas. As the proposed schemes address the spectral and energy efficiency trade‐off effectively, while corroborating better ABER performance, they have the potential to become a competing candidate for next‐generation networks. [ABSTRACT FROM AUTHOR]

Summary: Spatial modulation techniques (SMTs) have emerged as promising multiple‐input and multiple‐output (MIMO) technology for fifth generation (5G) networks, which can achieve an appealing trade‐off between conflicting design objectives such as reliability, hardware cost, complexity, spectral efficiency, and energy efficiency. Most of the SMTs suffer from significant performance deterioration under correlated fading channels. In this paper, a novel spectral efficient SMT referred as enhanced redesigned spatial modulation (EReSM) is proposed, which is robust against adverse channel correlation effects. At any time instant, EReSM activates either one or two transmit antennas and employs a robust bits to antenna index mapping that ensures the selection of antenna subsets with maximum spatial separation to mitigate the effect of spatial correlation. EReSM also exploits phase rotation of transmitted symbols as an additional dimension to convey an extra information bit. The rotation angles used for bit mapping are optimized for various modulation schemes to maximize the minimum euclidean distance between the symbols. To analyze the performance, analytical upper bound expression for average bit error probability (ABEP) is derived for both uncorrelated and spatially correlated channel conditions. Monte Carlo simulation results substantiate the accuracy of the analytical results and also demonstrate that the proposed EReSM outperform conventional redesigned spatial modulation (ReSM) by at least 4 dB. [ABSTRACT FROM AUTHOR]

Spatial Modulation techniques (SMTs) offer an exceptional trade-off between spectral and energy efficiencies. In this work, we propose a new SMT termed as permutation index-quadrature spatial modulation (PI-QSM), aiming for enhanced spectral efficiency and better average bit error rate (ABER) performance compared to rival SMTs. To enhance spectral efficiency, PI-QSM exploits permutations of active transmit antennas by conveying additional information bits innovatively through indices of active antenna permutations. PI-QSM also alleviates the major constraint in some of the SMTs, where the total number of transmit antennas need to be a power of two. At any time instance, PI-QSM transmits real and imaginary parts of two complex modulated symbols separately using four different active antennas. To avoid any ambiguities at the receiver and to improve the error performance of the overall system, a rotation angle is introduced between the two modulated symbols before transmission. The rotation angle is optimized for different M -quadrature amplitude modulation (QAM) constellations using extensive Monte Carlo simulations. Performance of PI-QSM scheme is analyzed by deriving closed-form expressions for upper bound and asymptotic average bit error probability (ABEP) over Rayleigh fading channels. The Simulation results substantiate the accuracy of analytical analysis and also indicate that the proposed PI-QSM scheme offers better ABER performance than rival SMTs such as QSM and generalized QSM (GQSM). [ABSTRACT FROM AUTHOR]

Spatial modulation (SM) is a spectrum and energy-efficient simple digital modulation scheme, which uses the transmitter antenna indices to convey the additional bits. The conventional SM variants have high complex receivers and offer poor performance under spatially correlated and line-of-sight (LOS) channel conditions. In this work, a new variant, modified spatial modulation (MSM) for 8 × 8 multiple input multiple output (MIMO) configuration is proposed, which uses one or two transmitter antennas based on the incoming bit patterns. The adaptive mapping used at the transmitter side for antenna selection and modified maximum likelihood (ML) detection used at the receiver side reduces the computational complexity. Every possible bit per channel use (bpcu) is mapped to a unique transmit symbol vector, which minimizes the receiver complexity, while combating the effect of spatial correlation. Through simulation results, it is observed that the average bit error rate (ABER) performance of the MSM-ML scheme is much better than that of the conventional SM-ML, generalized spatial modulation (GSM)-ML and redesigned spatial modulation (ReSM)-ML schemes under spatially correlated and LOS channel conditions. The promising results prove that the proposed scheme can be a better alternative to spatial multiplexing (SMX) in battery-driven compact handheld devices, which can be used for international mobile telecommunication-2020 (IMT-2020). [ABSTRACT FROM AUTHOR]