Composite Multiple-Mode Orthogonal Frequency Division Multiplexing With Index Modulation

Li, Jun; Dang, Shuping; Huang, Yu; Chen, Pengxu; Qi, Xiaomin; Wen, Miaowen; Arslan, Hüseyin

doi:10.1109/twc.2022.3220752

Cited by 50 publications

(20 citation statements)

References 45 publications

(53 reference statements)

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“…If W 0 i is the same as the composite pseudo-code W, the despread data b 0 i are calculated by Equation (17) and are equivalent to the real index data d 0 i .…”

Section: Index Data Bitsmentioning

confidence: 99%

“…The proposed scheme uses the corresponding activation pseudo-code to modulate and decode data according to the pseudo-code activation principle and the correlation peak judgement mechanism and converts the antenna index in SM into the index of the spreading code. So far, many studies [15][16][17] have shown that the OFDM-IM technique and its improved technique have a better bit error rate (BER) performance than traditional OFDM under the same spectral efficiency.…”

Section: Introductionmentioning

confidence: 99%

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High performance bit‐activation code index modulation method

Liu

Zheng

Feng

2023

IET Signal Processing

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With the increasing demand of applications for the spread spectrum technique, especially the demand for data transmission rates and spectral efficiency, the advantages of the traditional direct sequence spread spectrum (DSSS) system are limited. Therefore, multi‐ary spread spectrum (M‐ary) technology, parallel combinatory spread spectrum (PCSS) technology, and code index modulation (CIM) technology have been proposed. Although these three new technologies can improve the data rate, they all face the problem of the large consumption of pseudo‐code resources. In order to solve the problem of pseudo‐code resources, a bit‐activation code index modulation (BA‐CIM) method is proposed. At the transmitter, considering the good correlation among multiple pseudo‐codes, the corresponding pseudo‐code activation principle is established, and the corresponding spreading pseudo‐code is activated by using the status of each bit of the index data according to the pseudo‐code activation principle. Then, multicode superposition processing is carried out to spread the modulation data. At the receiver, the corresponding activation pseudo‐code is obtained using the maximum peak‐to‐average ratio (MPAR) and secondary peak‐to‐average ratio (SPAR) judgement mechanisms to decode the multibit index data. Compared with existing methods, the proposed BA‐CIM method can not only achieve a better bit error rate performance but also use the least pseudo‐code resources. Moreover, BA‐CIM has the best comprehensive performance improvement and is far superior to other methods. This research can provide technical support for the application of efficient spread spectrum communication.

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“…If W 0 i is the same as the composite pseudo-code W, the despread data b 0 i are calculated by Equation (17) and are equivalent to the real index data d 0 i .…”

Section: Index Data Bitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High performance bit‐activation code index modulation method

Liu

Zheng

Feng

2023

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…To accomplish this objective, superposition coding (SC) is performed at the transmitter and successive interference cancellation (SIC) at the receiver [ 13 ]. Moreover, combining NOMA with high spectral efficient multiple-input multiple-output (MIMO) techniques, such as quadrature spatial modulation (QSM) [ 14 , 15 ], can further enhance the spectral efficiency and increase the capacity of non-terrestrial wireless networks [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

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The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

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“…Millimeter wave (mmWave) communication is a promising technology that plays a major role in fifth-generation (5G) wireless communications owing to its wide bandwidth [ 1 ], sufficient spectral-efficiency [ 2 , 3 , 4 , 5 ] and high quality of service (QoS) [ 6 , 7 , 8 , 9 , 10 ]. In highly directive wireless communications, beamforming is a signal processing tool used to steer, shape, and control multiple antenna directions efficiently, which minimizes transmission losses [ 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

RIS-Assisted Hybrid Beamforming and Connected User Vehicle Localization for Millimeter Wave MIMO Systems

Sarker

Son

Han

2023

Sensors

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A reconfigurable intelligent surface (RIS) is a type of metasurface that can dynamically control the reflection and transmission of electromagnetic waves, such as radio waves, by changing its physical properties. Recently, RISs have played an important role in intelligently reshaping wireless propagation environments to improve the received signal gain as well as spectral efficiency performance. In this paper, we consider a millimeter wave (mmWave) vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) system in which, an RIS is deployed to aid downlink V2V data transmission. In particular, the line-of-sight path of the mmWave system is affected by blockages, resulting in higher signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles, and pedestrians. In this paper, we propose an RIS-assisted hybrid beamforming scheme for blockage-aware mmWave V2V MIMO systems to increase communication service coverage. First, we propose a conjugate gradient and location-based hybrid beamforming (CG-LHB) algorithm to solve the user sub-rate maximization problem. We then propose a double-step iterative algorithm that utilizes an error covariance matrix splitting method to minimize the effect of location error on the passive beamforming. The proposed algorithms perform quite well when the channel uncertainty is smaller than 10%. Finally, the simulation results validate the proposed CG-LHB algorithm in terms of the RIS-assisted equivalent channel for mmWave V2V MIMO communications.

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Composite Multiple-Mode Orthogonal Frequency Division Multiplexing With Index Modulation

Cited by 50 publications

References 45 publications

High performance bit‐activation code index modulation method

High performance bit‐activation code index modulation method

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

RIS-Assisted Hybrid Beamforming and Connected User Vehicle Localization for Millimeter Wave MIMO Systems

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