Polarization-spatial modulation (PSM) has been recently proposed to improve the system performance and energy efficiency of the conventional polarization modulation (PM) by activating only a single dual-polarized (DP) antenna for signal transmission. However, the spectral efficiency (SE) of PSM is significantly degraded due to the single DP antenna transmission. To tackle this problem, we propose a generalized PSM (GPSM) scheme to enhance the SE of PSM through transmission using multiple DP antennas. In the GPSM scheme, the information bits are mapped to antenna activation patterns (AAPs), polarization matrix activation patterns (PAPs), and modulated symbols. To further enhance the SE of GPSM, we propose a more practical scheme termed multi-mode GPSM (MM-GPSM), which transmits information bits not only through the AAPs, PAPs, and modulated symbols but also via the constellation activation patterns (CAPs). A low-complexity maximum likelihood (ML) detector and a log-likelihood ratio detector for both GPSM and MM-GPSM are proposed to relieve the high computational complexity of the optimal ML detector at the cost of a negligible performance loss. An upper bound on the bit error rate (BER) is derived in closed-form to evaluate the performance of both GPSM and MM-GPSM. Simulation results show that GPSM and MM-GPSM outperform the conventional PM and PSM schemes, particularly in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis of the upper-bounded BER.

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER).

Unmanned aerial vehicles (UAVs) serve as flexible aerial platforms, enriching air-ground communication networks in various ways. To support massive connectivity within limited time-frequency blocks, non-orthogonal multiple access (NOMA) is proposed to be integrated into UAV networks. However, a common issue associated with almost all NOMA schemes is the susceptibility to inter-user interference (IUI). Therefore, in this paper, we propose a multi-UAV cooperative system aided by NOMA with index modulation (IM), termed MCU-NOMA-IM, to improve the performance of air-ground networks by mitigating IUI and also avoiding the successive interference cancellation (SIC) decoding method that is prone to error floors. With MCU-NOMA-IM, the information bits pertaining to multiple UAVs are mapped into multiple dimensions, including the modulated symbols, subcarrier indices, and energy allocation patterns. To fully investigate the performance of MCU-NOMA-IM on air-ground networks, we consider scenarios in the presence of three and four UAVs and derive upper-bounds for the bit error rates (BERs). In addition, we propose a multi-clustered-UAV cooperative system aided by NOMA with IM (MCCU-NOMA-IM), which groups closely located UAVs into several clusters to reduce the requirement for time resources. Simulation results demonstrate that both MCU-NOMA-IM and MCCU-NOMA-IM greatly outperform cooperative NOMA and non-cooperative NOMA-IM schemes, especially for distant UAVs when the signal-to-noise ratio is sufficiently high. Also, we show that the derived BER upper bounds are asymptotically tight.

Affine frequency division multiplexing (AFDM) is a novel modulation technique based on chirp signals that has been recently proposed as an effective solution for highly reliable communications in high-mobility scenarios. In this paper, we focus on the design of robust index modulation (IM) schemes under the multiple-antenna AFDM transmission framework. To this end, the cyclic delay diversity (CDD) technique is employed to harvest the transmit diversity gain. As a result, we propose two novel AFDM-IM schemes with transmit diversity, termed as CDD-AFDM-IM-I and CDD-AFDM-IM-II. We analyze the full diversity conditions and parameter settings of the proposed CDD-AFDM-IM schemes for both integer and fractional Doppler cases over linear time-varying (LTV) channels. Moreover, we prove that IM enables AFDM to have stronger diversity protection when the full diversity condition is not satisfied. Asymptotically tight upper bounds on the average bit error rates (BERs) of the proposed schemes with maximum-likelihood (ML) detection are derived in closed-form. Furthermore, we propose a low-complexity double-layer message passing (DLMP) algorithm for practical large-dimensional signal detection in the proposed CDD-AFDM-IM systems. Comparison with existing detections shows that the proposed DLMP algorithm achieves a better tradeoff between the BER performance and the computational complexity. Finally, BER simulation results confirm that our proposed CDD-AFDM-IM schemes with both the ML and DLMP detections outperform the benchmark schemes over the LTV channels.

As a promising technology, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed to improve the transmission quality and coverage, which is considered to be widely applied to intelligent automated transportation (IAT) systems. However, due to the open environment and 360° omnidirectional transmission, security issues have always been a major difficulty hindering the implementation of STAR-RIS aided IAT systems. To tackle this challenge, we propose an enhanced security index modulation (ESIM) scheme in this paper, which combines well-known IM and higher-order linear decoding quasi-orthogonal space-time block coding (LD-QO-STBC) techniques to improve the system security performance. Specifically, the information bits and transmit antennas are organized into four distinct sets, with each subset of antennas activated by individual IM. Subsequently, the STAR-RIS is employed to meticulously craft the LD-QO-STBC scheme, which involves determining the phase of the unmodulated carrier. In a strategic move to thwart eavesdropping attempts, the eavesdroppers are subjected to continuously varying and disruptive continuous artificial noise (CAN) and is thus unable to reliably decode the transmitted information, which collectively achieves secure communications amidst potential eavesdropping threats. In addition, the theoretical analysis of both the bit error rate (BER) and secrecy capacity are derived to explore the potential of ESIM-LD-QO-STBC. Numerical results reveal that our proposed method excels in achieving a substantial diversity gain while maintaining low computational complexity. Furthermore, our proposed scheme enhances secrecy capacity by at least 50%, offering a marked improvement over conventional physical layer security (PLS) schemes.

Virtual spatial modulation (VSM) has shown potential for enhancing the energy efficiency of multiple-input multiple-output (MIMO) systems. However, the use of a limited set of virtual parallel channels in VSM results in lower spectral efficiency compared to traditional MIMO systems. To address this, we propose a composite multiple-mode VSM (C-MM-VSM) scheme, along with its optimized variants, to increase SE. The C-MMVSM extends indexing to multiple domains, mapping input bits to channel activation patterns, energy allocation patterns, and mode activation patterns. As the constellation sets are practically higher than active antennas, we propose a variant scheme, which introduces a new mapping rule between input bits and additional mode activation patterns, increasing the mode bits of the system. We also develop diversity-enhanced schemes using coordinate interleaving for improved average bit error probability (ABEP). Additionally, we propose generalized schemes that optimize all the indexes and the in-phase/quadrature extension which expands indexing to in-phase and quadrature domains, achieving double spectral efficiency. Analytical ABEP results, along with simulations, confirm that the proposed schemes outperform benchmark schemes, providing significant spectral efficiency and ABEP improvements without needing extra resources.

Affine frequency division multiplexing (AFDM) and orthogonal AFDM access (O-AFDMA) are promising techniques based on chirp signals, which are able to suppress the performance deterioration caused by Doppler shifts in high-mobility scenarios. However, the high peak-to-average power ratio (PAPR) in AFDM or O-AFDMA is still a crucial problem, which severely limits their practical applications. In this paper, we propose a discrete affine Fourier transform (DAFT)-spread AFDMA scheme based on the properties of the AFDM systems, named DAFT-s-AFDMA to significantly reduce the PAPR by resorting to the DAFT. We formulate the transmitted time-domain signals of the proposed DAFT-s-AFDMA schemes with localized and interleaved chirp subcarrier allocation strategies. Accordingly, we derive the guidelines for setting the DAFT parameters, revealing the insights of PAPR reduction. Finally, simulation results of PAPR comparison in terms of the complementary cumulative distribution function (CCDF) show that the proposed DAFT-s-AFDMA schemes with localized and interleaved strategies can both attain better PAPR performances than the conventional O-AFDMA scheme.

Affine frequency division multiplexing (AFDM) is a new multicarrier technique based on chirp signals tailored for high-mobility communications, which can achieve full diversity. In this paper, we propose an index modulation (IM) scheme based on the framework of AFDM systems, named AFDM-IM. In the proposed AFDM-IM scheme, the information bits are carried by the activation state of the subsymbols in the discrete affine Fourier (DAF) domain in addition to the conventional constellation symbols. To efficiently perform IM, we divide the subsymbols in the DAF domain into several groups and consider both the localized and distributed strategies. An asymptotically tight upper bound on the average bit error rate (BER) of the maximum-likelihood detection in the existence of channel estimation errors is derived in closed-form. Computer simulations are carried out to evaluate the performance of the proposed AFDM-IM scheme, whose results corroborate its superiority over the benchmark schemes in the linear time-varying channels. We also evaluate the BER performance of the index and modulated bits for the AFDM-IM scheme with and without satisfying the full diversity condition of AFDM. The results show that the index bits have a stronger diversity protection than the modulated bits even when the full diversity condition of AFDM is not satisfied.

The reconfigurable intelligent surface (RIS)-aided vehicle road cooperation (VRC) scheme is a promising technique. However, the security issue of the RIS-VRC systems is still a crucial problem due to the high connectivity of multiple vehicles under the broadcasting environment. Within this study, based on the potential of the RIS-VRC system, a novel low-complexity consecutive artificial noise (CAN) with legal channel state information (CSI) and a low-cost and spectral-efficient secure index modulation (IM) design physical layer security (PLS) scheme is detailed. Specifically, an information symbol consisting of a modulation symbol and AN devised based on the CSI of the legitimate link across two consecutive time intervals is transmitted. At the receiver, the decoding scheme relies on two consecutive time intervals which can enable the ideal elimination of AN solely at the authorized vehicle is proposed. Nevertheless, given the independence of the CSI for the legitimate links and the illegitimate links, the unauthorized vehicle remains vulnerable to AN interference. Moreover, the antenna selection method based on minimum Euclidean distance (MED-TAS) is proposed to guarantee the requisite number of transmitting antennas for IM. Finally, the expressions of the secrecy rate are deduced by considering the disparity in the attainable mutual information between the authorized vehicle and the eavesdropping vehicle. Compared with traditional PLS schemes, the numerical outcomes affirm the capability of the proposed scheme to attain enhanced security performance with much lower complexity.

In this paper, we employ a reconfigurable intelligent surface (RIS) to boost the single-carrier frequency-domain equalization (SC-FDE) transmission for both single- and multi-antenna broadband wireless communications. Different from the existing RIS-SC-FDE schemes that use an RIS to harvest cyclic delay diversity with a limited beamforming gain, our advanced schemes unlock the potential of the RIS in passive beamforming to maximize the receive signal-to-noise ratio. Concerning the high complexity issue, a low-complexity minorization-maximization (MM) method, which leads to a closed-form solution in each iteration, is developed for the RIS beamforming optimization problem. Finally, simulation results show that the proposed RIS-SC-FDE with the MM-based beamforming significantly outperforms the existing counterparts in terms of bit error rate.

3-D object detection from mobile phones in Device-to-Device (D2D) system provides a new smart payment tool for the next generation of fintech, which is more flexible and efficient than the traditional barcode. In this article, we propose a monocular 3-D object detection method based on depth-guided local convolution. The method combines the information of RGB image mode and depth mode by using a convolution kernel through depth image and works on a single RGB image locally. According to the multiscale input information, the convolution kernel is adaptively adjusted to capture the target objects of different scales, so as to improve the performance of 3-D object detection. In addition, we use the soft-non-maximum suppression algorithm instead of traditional non-maximum suppression to select the best prediction box. In order to further improve the accuracy of 3-D object detection, the depth estimation network and 3-D object detection network are jointly trained in this method to make the two networks constrain each other and achieve the best performance.

6G network enables the rapid connection of autonomous vehicles, the generated internet of vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis to build an intelligent transportation system in terms of the 3D object detection and segmentation. Recently, a great variety of deep convolution networks have been proposed for 3D data analysis, making significant progress in the application of deep learning in 3D computer vision. Inspired by the application of transformer network in 2D computer visual tasks, and in order to increase the expression ability of local fine-grained features, we propose an effective local feature transformer network to learn local feature information and correlations between point clouds. Our network is adaptive to the arrangement of set elements through transformer module, so it is suitable for the feature extraction of local point clouds. In addition, experimental results demonstrate that our LFT-network outperforms the state-of-the-art in 3D model classification tasks on ModelNet40 dataset and segmentation tasks on S3DIS dataset.

The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and analysis of the driving environment, while it involves massive computing resources for deep learning, which can’t be provided by vehicle-mounted devices. This requires the use of seamless, reliable, and efficient massive connections provided by the 6G network for computing in the cloud. In this paper, we propose a novel deep learning framework with 6G enabled transport system for joint optimization of depth and ego-motion estimation, which is an important task in 3D perception for autonomous driving. A novel loss based on feature map and quadtree is proposed, which uses feature value loss with quadtree coding instead of photometric loss to merge the feature information at the texture-less region. Besides, we also propose a novel multi-level V-shaped residual network to estimate the depths of the image, which combines the advantages of V-shaped network and residual network, and solves the problem of poor feature extraction results that may be caused by the simple fusion of low-level and high-level features. Lastly, to alleviate the influence of image noise on pose estimation, we propose a number of parallel sub-networks that use RGB image and its feature map as the input of the network. Experimental results show that our method significantly improves the quality of the depth map and the localization accuracy and achieves the state-of-the-art performance.

Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.

3D perception is an essential capability of autonomous vehicles. Most state-of-the-art stereo matching networks pursue higher prediction accuracy at the cost of the inference speed. However, high demand on computational resource pushes the cost of hardware, hindering practical applications of stereo matching. In this paper, we propose RMCNet, a novel re-parameterized coarse-to-fine network for stereo matching. RMCNet achieves real time on edge devices while outputs accurate disparity maps. To reduce the computing complexity of 3D convolution in cost aggregation, we propose Mixed Cost Volumes that consist of 4D and 3D cost volumes, enabling efficient optimization of the initial disparity map for real-time inference. An Efficient Feature Matching module is proposed to provide absolute disparity candidates for 3D cost volume. To increase the reliability of 4D and 3D cost volumes, we propose a simple but efficient 3D cost aggregation module and a pyramid 2D cost aggregation module for the aggregation of Mixed Cost Volumes. Moreover, we propose a Re-parameterized Channel Sensing module and a Re-parameterized Disparity-Aware module to replace 2D and 3D convolutional blocks with residual connections. Being evaluated on KITTI 2012 and KITTI 2015, the comprehensive performance of the proposed network is improved by the re-parameterization approach. Outstanding trade-off between accuracy and speed on edge devices over existing SOTA works is achieved. With fast average inference speed on NVIDIA Jetson Nano of 33.74 FPS, the accuracy in terms of 3px-all on KITTI 2012 test set and D1-all on KITTI 2015 test set are 3.01% and 3.41%, respectively.

In this paper, we propose a composite multiple-mode orthogonal frequency division multiplexing with index modulation (C-MM-OFDM-IM) scheme to increase the spectral efficiency (SE) of OFDM-IM systems by extending the indexing to the energy and constellation domains. In C-MM-OFDM-IM, the information bits are mapped to not only the subcarrier activation patterns (SAPs) and modulation symbols, but also the energy allocation patterns (EAPs) and constellation activation patterns (CAPs). To cope with the practical situations, we propose a variant IM scheme named C-MM-OFDM-IM-II to build a new mapping rule between information bits and the increased CAPs, capable of further increasing the SE of C-MM-OFDM-IM. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of C-MM-OFDM-IM(-II). Moreover, we further propose two enhanced schemes, named generalized C-MM-OFDM-IM(-II) and C-MM-OFDM with in-phase/quadrature IM(-II), where the former jointly considers all SAPs, EAPs, CAPs and modulated symbols, while the latter expands the index implementation to the in-phase and quadrature constellation domains. Simulation results show that C-MM-OFDM-IM(-II) outperforms the conventional OFDM-IM related schemes, especially in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis for the upper-bounded BER and achievable rate.

In this paper, we propose a hybrid OFDM-SNM scheme, named joint-mapping OFDM-SNM (JM-OFDM-SNM), to avoid transmitting variable lengths of information bits. In JM-OFDM-SNM, the signal vectors are generated by jointly considering subcarrier activation patterns and constellation symbols. To relieve the high computational complexity of the optimal maximum-likelihood (ML) detection, we design a low-complexity detection method via resorting to the log-likelihood ratio criterion. We also analyze the upper bound on the bit error rate of JM-OFDM-SNM. To further enhance the utilization of frequency resource, we propose a more general scheme, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM), to accommodate the constellation orders for different numbers of activated subcarriers. Simulation results show that AJM-OFDM-SNM achieves better performance than both JM-OFDM-SNM and OFDM-SNM at the same spectral efficiency. The low-complexity detection method of JM-OFDM-SNM achieves very close performance to the optimal ML detection, and the theoretical curves well match the simulation curves in the high signal-to-noise ratio region.

The secure exchange of messages between different communication devices is a major issue of Internet-of-Things (IoT) applications in future smart cities. Current security mechanisms focus on multiple antennas technology, such as spatial modulation (SM), but in space shift keying (SSK), there is still a space to explore. In this article, we propose a secrecy-enhancing SSK scheme for IoT applications by applying security technologies in the physical layer wherein the number of transmit antennas is arbitrary rather than the value of power of two. In this scheme, the security performance of the communication system is improved by using two technologies, namely, artificial noise (AN) and antenna selection. We assume that the application scenario of the SSK system is under the classic eavesdropping model. First, we design ANs according to the channel state information (CSI) to interrupt the eavesdropper and benefit the legitimate receiver via the appropriate cancellation technology. Second, the antenna selection method is designed based on the signal to leakage noise ratio (SLNR) to further boost the secrecy performance by expanding the mutual information difference between the main channel and the eavesdropping channel. Results from our simulations indicate that by the use of the proposed scheme, significant secrecy enhancing can be achieved in terms of bit error ratio (BER) and secrecy rate (SR) when compared with existing schemes. This achieved secrecy enhancing can benefit the suitable IoT communication applications in the smart city environment to avoid the leakage of data transmission.

Orthogonal frequency division multiplexing with subcarrier number modulation (OFDM-SNM) has been recently proposed to improve the spectral efficiency (SE) of the traditional OFDM system. In this paper, we propose a joint-mapping OFDM-SNM (JM-OFDM-SNM) scheme to transmit the signal vector with a constant length of information bits by jointly considering the subcarrier activation patterns and constellation symbols. A low-complexity detection scheme based on log-likelihood ratio criterion is proposed to relieve the high computational complexity of the maximum-likelihood detection at the cost of a negligible performance loss. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of JM-OFDM-SNM. To suit different application scenarios, we further propose two enhanced schemes, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM) and JM-OFDM with in-phase/quadrature SNM (JM-OFDM-IQ-SNM), where the former adjusts the constellation orders for different numbers of active subcarriers, and the latter extends the indexing to in-phase and quadrature domains. Simulation results corroborate the tightness of the derived BER expression in the high signal-to-noise ratio region and show that (A)JM-OFDM-SNM improves the performance of OFDM-SNM, while both AJM-OFDM-SNM and JM-OFDM-IQ-SNM schemes perform better than JM-OFDM-SNM at the same SE.

Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks.

Motivated by multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) and multiple-input multiple-output (MIMO) technologies, a new modulation technique named multiple-mode MIMO with IM (MM-MIMO-IM) is proposed, which is implemented over the virtual parallel channels resulting from singular value decomposition (SVD) of the MIMO channel. Moreover, coordinate interleaved technique is employed to improve its diversity gain. Compared with traditional MIMO systems, the spectral efficiency (SE) of MM-MIMO-IM systems is significantly improved due to the introduction of the mode permutation. We further extend MM-MIMO-IM to in-phase/quadrature (I/Q) dimension, yielding MM-MIMO-IM-IQ, which achieves a double SE with respect to MM-MIMO-IM. Asymptotically tight upper bounds on bit error rates (BERs) of the proposed schemes are derived to evaluate their performance. Monte Carlo simulation results demonstrate the advantages of the proposed schemes.

Recent advances in cooperative multiple-input-multiple-output (CMIMO) techniques have encouraged interest in the development of intelligent transportation systems (ITS). They have the potential for use in the infrastructure to vehicle (I2V) and infrastructure to infrastructure (I2I) communications in ITS networks where the energy consumption of wireless sensor nodes embedded on the road infrastructure is constraint. Therefore, how to reduce the energy consumption becomes a hot research topic . In this paper, applications of cooperative communications in ITS networks are proposed for reducing the total energy consumption . At first, the ITS model is established based on the cooperative multiple-input-multiple-output spatial modulation (CMIMO-SM). A detailed energy consumption analysis of the proposed scheme compared with the traditional single-input-single-output (SISO) based scheme is then presented. The comparison conducted between these communication schemes helps us select the optimal one for energy reduction in energy constrained ITS networks. Additionally, under the guidance of the proposed scheme we consider the multi-hop transmission scenario where the energy efficiency improvement is achieved by finding the optimal hop number with the equal hop-length scheme. As a result, we analyze the energy consumption in different situations, and discuss the requirements on the hop-length and hop number in ITS networks. It shows that the optimal results are dependent on the ITS scenarios and choosing the appropriate transmission scheme will provide a good energy consumption performance in ITS.

Orthogonal frequency division multiplexing (OFDM) is widely considered as a promising technique for vehicular communications. In current OFDM-based vehicular communications systems, a portion of tones are dedicated for pilot symbols and transmitted along with the data-bearing tones to estimate residual frequency error and/or channel state information. However, these pilot symbols themselves do not carry any information, thus inevitably degrading the system spectral efficiency (SE). Motivated by the concept of index modulation, in this paper, we propose a novel pilot insertion technique to enhance the SE, in which the pilot positions are selected according to extra information bits. Specifically, three different types of pilot position selection schemes, which result in equal-spaced, unequal-spaced, and hybrid pilot placements, are studied, and the corresponding pilot position detection methods are designed, where the pilots can be used for either carrier phase tracking or channel estimation purpose. Computer simulation results corroborate the advantages of the proposed technique in terms of bit error rate.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has become a high-profile modulation scheme for the fifth-generation (5G) wireless communications and has thus been extended to multi-hop scenarios in order to improve the network coverage and energy efficiency. However, the extension of OFDM-IM to multi-relay cooperative networks is not trivial, since it is required that a complete OFDM block should be received and decoded as an entity in one node. This requirement prevents the employment of multiple relays to forward fragmented OFDM blocks on individual subcarriers. In this regard, we propose a distributed processing scheme for multi-relay assisted OFDM-IM, by which multiple relays are selected to forward signals in a per-subcarrier manner to provide optimal error performance for two-hop decode-and-forward (DF) OFDM-IM systems. Specifically, a single selected relay only needs to decode partial information carried on certain active subcarriers and forward just as for traditional OFDM systems without IM. After receiving all signals on active subcarriers forwarded by different relays, the destination can reconstruct the complete OFDM block and retrieve the full information. We analyze the average block error rate and modulation capacity of the two-hop OFDM-IM system employing the proposed distributed DF protocol and verify the analysis by numerical simulations.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has recently attracted many researchers' attention due to its superior spectrum efficiency and reliability compared to the traditional OFDM. Cooperative decode-and-forward relaying has been incorporated with OFDM-IM, which provides a higher energy efficiency and better network coverage. However, it might not be feasible in realistic applications, owing to the high system complexity and transmission delay rendered by complex decoding and channel estimation procedures. Therefore, in this paper, we propose a fixed-gain (FG) amplify-and-forward (AF) relay-assisted OFDM-IM system, which does not need to perform complex decoding and channel estimation at the relay, but only requires a pre-processing capability at the relay, e.g., cyclic prefix removal and re-insertion. Therefore, the system complexity can be reduced and the forwarding delay as well as power consumption caused by processing at the relay also decline. We analyze the average outage probability, block error rate, and achievable rate of the proposed system and verify all analysis by numerical results. The proposed FG AF relay-assisted OFDM-IM provides a simple solution to the implementation of OFDM-IM in new network paradigms, where nodes are simple, power limited, and/or complexity limited.

We first generalize the extended orthogonal space time block codes (EO-STBCs). Then, based on the generalized EO-STBCs (GEO-STBCs) and channel state information (CSI), the feedback bits are generated at the receiver. By using the feedback bits from the legitimate receiver, we propose a new advantage distillation scheme for multiple-input multiple-output (MIMO) wiretap channels.

This paper proposes a novel generalised pre-coding aided spatial modulation (GPSM) scheme, called generalised virtual spatial modulation (GVSM), which performs index modulation on the virtual parallel channels obtained by the singular value decomposition (SVD) of multiple-input multiple-output (MIMO) channels. To improve the diversity performance, the coordinate interleaving technique is further introduced into GVSM, resulting in so-called coordinate interleaved GVSM (CI-GVSM), in which the real and imaginary parts of two complex symbols are interleaved and transmitted over strong and weak eigen-modes. A closed-form upper bound on the average bit error probability (ABEP) applicable to both GVSM and CI-GVSM is derived, followed by the asymptotic performance analysis that reveals the error floor and diversity order under imperfect and perfect channel estimation, respectively. Numerical and simulation results corroborate our analysis and show that the proposed GVSM and CI-GVSM outperform the existing GPSM schemes at the same spectral efficiency.