Our previous work1 introduced a visual-inertial odometry (VIO) method that leverages long-tracked drifted features, which is able to perform precise localization in complex environments. However, a key limitation is its reliance on computationally intensive large sliding window filter (SWF) to maintain performance. By analyzing the observability of the original system, we shown that the consistency of the SWF is disrupted due to the discrepancy between the linearization points of the prior state in the historical SWF and the current one. In this case, the covariance matrix of the estimation errors is larger than that predicted by the SWF, leading to inaccurate state updates and loss of accuracy. The original system achieves delayed marginalization by increasing the capacity of the SWF, thereby reducing the discrepancy of the linearization points and, in turn, minimizing the disruption to the consistency of the SWF. In this paper, we adopt a more efficient strategy by aligning the null space matrix of the prior with that of the current SWF, thereby avoiding the destruction of the consistency. We evaluate our method on the EuRoC, TUM-VI, and 4Seasons datasets, comparing it with state-of-the-art methods. Experimental results show that our approach achieves high localization accuracy even with smaller SWF, making it more suitable for non-terrestrial networks (NTN)-based deployments with resource constraints. The code will be published on GitHub.2.

This paper presents a visual-inertial odometry (VIO) method using long-tracked features. Long-tracked features can constrain more visual frames, reducing localization drift. However, they may also lead to accumulated matching errors and drift in feature tracking. Current VIO methods adjust observation weights based on re-projection errors, yet this approach has flaws. Re-projection errors depend on estimated camera poses and map points, so increased errors might come from estimation inaccuracies, not actual feature tracking errors. This can mislead the optimization process and make long-tracked features ineffective for suppressing localization drift. Furthermore, long-tracked features constrain a larger number of frames, which poses a significant challenge to real-time performance of the system. To tackle these issues, we propose an active decoupling mechanism for accumulated errors in long-tracked feature utilization. We introduce a visual reference frame reset strategy to eliminate accumulated tracking errors and a depth prediction strategy to leverage the long-term constraint. To ensure real-time performance, we implement three strategies for efficient system state estimation: a parallel elimination strategy based on predefined elimination order, an inverse-depth elimination simplification strategy, and an elimination skipping strategy. Experiments on various datasets show that our method offers higher positioning accuracy with relatively short consumption time, making it more suitable for edge-enabled low-altitude IoT navigation, where high-accuracy positioning and real-time operation on edge device are required.

Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

Fluid antenna-enabled multiple-input multiple-output (FA-MIMO) systems hold significant application potential; however, they also introduce substantial costs in channel state information (CSI) acquisition. In this paper, we propose a novel FA-assisted rectangular differential index modulation (FA-RDIM) scheme for MIMO systems, aiming to achieve high spectral efficiency while addressing the problem of the increased CSI acquisition cost. The transmitted information is mapped to modulation symbols and cyclic shifts of FA pattern indices. A multi-stage detection method is introduced, which reduces computational complexity by identifying the most likely candidates for FA pattern indices. We derive a closed-form expression for the bit error rate (BER) theoretical performance considering error propagation, and present an optimization algorithm based on rank and determinant criterion (RDC), Hamming distance (HD), and gradient descent (GD) to optimize the FA pattern vector set. Simulation results demonstrate that the proposed scheme exhibits a minimal performance loss compared to conventional coherent modulation schemes under static channel conditions, while offering a performance advantage in time-varying channels with outdated CSI.

Spectral efficiency (SE) and energy efficiency (EE) are two major challenges faced by the sixth-generation wireless communication systems. In this paper, we propose a reconfigurable intelligent surfaces-assisted simultaneous wireless information and power transfer scheme based on fluid antenna port grouping index modulation (RIS-FA-PGIM). The flexible port switching capability of FA overcomes the spatial limitations of traditional antennas, significantly improving the SE. Further-more, in order to improve the SE and EE of the system, this paper jointly optimizes the beamforming matrix at the base station and RIS. Due to the coupling relationship between variables, the optimization problem is non-convex and difficult to solve. In order to solve this problem, an alternating optimization algorithm is proposed, which gradually approaches the global optimal solution through an iterative optimization process. Simulation results show that the system not only achieves outstanding SE performance but also realizes low energy consumption, which verifies the effectiveness and superiority of the scheme.

Ultrasonic intra-body communication (IBC) demands high data rates and high reliability to achieve revolutionary medical and healthcare applications. This paper proposes an ultrasonic multidimensional index modulation (U-MIM) technique for IBCs. The core idea is to utilize ultrasonic pulses to transmit modulation bits whilst jointly utilizing indices in time and code domains to transmit index bits without adding extra pulses, significantly improving the data rate. Then, a maximum likelihood (ML) receiver is investigated to reveal the optimal bit-error rate (BER) performance. To address the high computational complexity of the ML receiver, a low-complexity ML (L-ML) receiver is proposed by employing maximum-minimum selection and joint estimation of index bits and modulation bits to reduce the number of template signals. Furthermore, this paper analyzes the theoretical BER, spectral efficiency (SE), and computational complexity of the proposed L-ML receiver considering the proposed U-MIM and the existing ultrasonic index modulation (UsIM) and UsIM with spread spectrum (UsIM-SS) systems. Theoretical and simulation results demonstrate that U-MIM achieves higher SE than UsIM-SS and lower BER than UsIM. Moreover, U-MIM with the L-ML receiver can attain the same BER as UsIM-SS but with significantly reduced complexity. Compared with traditional ML receivers, the L-ML receiver can reduce the complexity by up to twelve orders of magnitude for both U-MIM and UsIM-SS, while maintaining equivalent BER performance. The joint use of U-MIM and L-ML can provide a promising solution for versatile IBC applications requiring high data rate, high reliability, and low complexity.

In this paper, an affine frequency division multiplexing aided grant-free random access (AFDM-GF-RA) system is designed for low Earth orbit (LEO) satellite Internet of Things (SIoT) networks, where users employ the GF-RA to access the satellite concurrently by configuring the AFDM modulation. In order to evaluate the system’s performance, average bit error probability (ABEP) bounds are first derived by Cholesky decomposition, which are verified by simulation results. Furthermore, a block sparse prior expectation propagation detection (BSPEP) is developed for joint active users detection (AUD) and data detection (DD). User activity probability is considered as a prior for joint detection. Concurrently, the accuracy of AUD is further improved by combining the block sparsity characteristics of the user transmission signals. Simulation results show that the proposed detector outperforms the classical oracle least squares (OLS) benchmark and approaches the theoretical ABEP bound at high signal-to-noise ratio (SNR).

This paper studies the physical-layer security (PLS) for an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) network, where transmissions from a base station (BS) to a trusted near user (TNU) and an untrusted far user (UFU) are assisted by an UAV relay in the face of multiple non-colluding external eavesdroppers (Es). The traditional strategies such as artificial noise-aided friendly jammer (TAN-FJ), artificial noise with non-friendly jammer (TAN-NFJ), and reconfigurable intelligent surface (RIS)-aided schemes fail to simultaneously guarantee the secrecy for both TNU and UFU, thus how to guarantee the PLS for NOMA users becomes an urgent issue to be solved. In this context, we propose an artificial noise (PAN) scheme aided with digital network coding (DNC). Specifically, the artificial noise signals generated by UAV are resorted to encrypt confidential signals with the assistance of DNC, which realizes one-time pad, thus the PLS of TNU and UFU can be ensured. We resort security-reliability tradeoff (SRT) as a metric to evaluate the PLS for our proposed PAN scheme. Hence, the exact and asymptotic expressions of outage probability (OP) and intercept probability (IP) are derived to quantify the reliability and security. Moreover, numerical simulations validate the correctness of our theoretical results and reveal that: 1) the PAN scheme enhances the SRT performance of near user compared to the TAN-FJ, TAN-NFJ, and RIS-aided schemes, while far user by the PAN scheme achieves almost the same SRT performance as the TAN-FJ scheme and significantly better performance than the TAN-NFJ and RIS-aided schemes, which indicates that the PAN scheme realizes the fairness of secrecy transmissions for NOMA users; 2) the SRT performance of TNU benefits from multiple antennas at BS and UAV, which has ignorable effect on the SRT performance of UFU; 3) the antenna number at UAV has more obvious impact on the SRT of TUN than the antenna number at BS.

The advancement of nanotechnology has enabled the deployment of medical nanonetworks within the human body, further accelerated by progress in terahertz communication technologies and nanonetwork routing protocols. However, nanonodes may move in dynamic environments due to environmental influences or self-propulsion mechanisms, posing significant challenges to routing protocol design. To address this, we propose a dynamic routing protocol for nanonetworks that integrates real-time velocity vectors to account for time-varying node positions. During relay node selection, key factors such as candidate nodes' velocity vectors are comprehensively evaluated to determine the optimal relay for next-hop transmission. To ensure efficient data exchange among multiple nodes, we design a time-division multiple access (TDMA)-based media access control (MAC) protocol to prevent packet collisions and losses. The protocol assigns distinct transmission and reception time slots to different node types and implements a countdown mechanism to manage channel access and eliminate conflicts. Numerical and simulation results demonstrate that the proposed protocol significantly outperforms benchmark protocols, achieving notable improvements in both time and energy efficiency.

This paper studies movable pinching-antenna (PA) assisted communications, where a single PA can be dynamically placed along a dielectric waveguide to serve multiple users via orthogonal or non-orthogonal multiple access (OMA/NOMA). In contrast to the ideal-lossless waveguide model commonly assumed, our work explicitly incorporates waveguide-power loss, thereby introducing a fundamental trade-off with free-space path loss that jointly determines system performance. By deriving the lower-bound-based suboptimal PA position for two- and three-user scenarios under both with and without waveguide-power loss, we establish a general positioning guideline applicable to multi-user scenarios. Furthermore, we perform a comparative analysis between the proposed single-PA scheme and a static multi-PA scheme. Analytical and simulation results derive the conditions determining which scheme is superior, revealing that the trade-off is fundamentally governed by the relative dominance between the waveguide-power loss and the free-space path loss. Simulation results validating our analytical derivations also confirm that the proposed PA positioning rule in NOMA-based PASS holds for both successive interference cancellation (SIC) decoding orders, demonstrating its robustness regardless of whether the near user decodes the far user’s signal first or vice versa.

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD). We provide comprehensive simulation results to analyze the performance of the proposed iterative algorithm and to demonstrate its dependence on different design parameters. Our results also confirm the superiority of the proposed resource allocation approach over conventional benchmark schemes.

The recently proposed affine frequency division multiplexing (AFDM) modulation has been considered as a promising technology for narrowband doubly-dispersive channels. However, the time-scaling effects, i.e., pulse widening and pulse shortening phenomena, in extreme wideband doubly-dispersive channels have not been considered in the literatures. In this paper, we investigate such wideband transmission and develop an efficient transmission structure with chirp-periodic prefix (CPP) and chirp-periodic suffix (CPS) for AFDM system. We derive the input-output relationship of AFDM system under time-scaled wideband doubly-dispersive channels and demonstrate the sparsity in discrete affine Fourier (DAF) domain equivalent channels. We further optimize the AFDM chirp parameters to accommodate the time-scaling characteristics in wideband doubly-dispersive channels and verify the superiority of the derived chirp parameters by pairwise error probability (PEP) analysis. We also develop an efficient cross domain distributed orthogonal approximate message passing (CD-D-OAMP) algorithm for AFDM symbol detection and analyze its corresponding state evolution. By analyzing the detection complexity of CD-D-OAMP detector and evaluating the error performance of AFDM systems based on simulations, we demonstrate that the AFDM system with our optimized chirp parameters outperforms the existing competitive modulation schemes in time-scaled wideband doubly-dispersive channels. Moreover, our proposed CD-D-OAMP detector can achieve the desirable trade-off between the complexity and performance, while supporting parallel computing to significantly reduce the computational latency.

The reconfigurable intelligent surface (RIS) has been widely studied in far-field communications (FFC), and further extended to near-field communications (NFC). With the distinct electromagnetic properties in FFC and NFC, it remains debatable whether the RIS deployment strategies established for FFC are still applicable to NFC. To bridge this gap, we examine RIS-aided cooperative relaying in NFC through two representative configurations, single-RIS and multi-RIS relaying, in which a decode-and-forward relay forwards data from the source to the user with the assistance of RIS For the single RIS deployment, we show that the achievable rates with RIS deployed near the base station (BS) or near relay are completely different. It is revealed that, with a single-antenna relay, the multi-RIS is significantly preferable over the single RIS in NFC without the requirement of massive RIS reflection elements, which is completely different from that of FFC. Furthermore, we derive closed-form expressions for the achievable rates under both single-antenna and multi-antenna relay configurations to explicitly determine the distance threshold. The presented analytical results demonstrate that locating RIS near the relay yields substantial performance gains when the transmission distance exceeds a certain threshold. This conclusion is validated by numerical simulations, which systematically illustrates the distinct impact of RIS deployment strategies in near-field versus far-field regions.

Affine frequency division multiplexing (AFDM) exhibits strong robustness against time and frequency dispersion in doubly dispersive channels (DDCs), enabling reliable communication under high mobilities. However, in the multi-user uplink scenario, inter-user channel delay and Doppler differences in the discrete affine Fourier transform (DAFT) domain manifest as inevitable multi-user interference (MUI). To address this issue, building upon the DAFT and AFDM, we propose a novel uplink multiple access scheme termed as DAFT-spread affine frequency division multiple access (DAFT-s-AFDMA). In our proposed scheme, DAFT spreading is performed by each user to multiplex the transmitted symbols over the DAFT domain, which includes a pre-chirp parameter that can be flexibly adjusted to reduce the peak-to-average power ratio (PAPR) of the AFDM system. Accordingly, we derive new guidelines for setting the DAFT parameters and the asymptotically tight upper bounds on the average bit error rate, revealing the insights of PAPR reduction. Furthermore, a low-complexity cross-domain expectation propagation (CD-EP) detector is proposed, capitalizing on the sparsity of DAFT domain effective channel matrix and the corresponding symbol domain constellation constraints to enhance the error performance. Simulation results show that the proposed CD-EP detector outperforms both conventional Gaussian message passing (GMP) and minimum mean square error (MMSE) detectors with a much lower complexity, and also verify the superiority of DAFT-s-AFDMA to plain AFDMA across various scenarios of high-mobility DDCs.

Affine frequency division multiplexing (AFDM)-based orthogonal multiple access (OMA) suffers from rank deficiency of the effective channel matrix, which degrades the performance of minimum mean square error (MMSE) detection. To address this issue, in this paper, we propose a partially-overlapping AFDM-OMA scheme that inserts a cyclic prefix (CP) in the discrete affine Fourier (DAF) domain for each user and assigns different inverse discrete affine Fourier transform (IDAFT) parameters to different users. With CP insertion and a user-specific parameter strategy, the effective channel matrix is guaranteed to be full-rank. We theoretically analyze the relation-ship between the rank of the channel matrix and the lower bound of the bit error rate (BER), and demonstrate the superiority of the proposed scheme compared to the benchmarks. Simulation results show that the proposed scheme achieves improved BER performance and validate the effectiveness of assigning user-specific IDAFT parameters.

Affine frequency division multiplexing (AFDM) is a promising waveform for next generation wireless networks, effectively resisting the channel double selectivity. Conventional embedded pilot (EP) scheme for AFDM requires additional guard symbols, leading to a reduction in spectral efficiency (SE). To address this issue, superimposed pilot (SP)-based schemes have been proposed, improving the SE; however, at the cost of increased computational complexity at the receiver. In this work, we propose an iterative joint channel estimation and data detection scheme for AFDM by harnessing SPs, aiming to enhance the SE while reducing the detection complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional SP-based scheme in both channel estimation and data detection, while achieving a lower computational complexity. Notably, under low data power conditions, our approach surpasses the EP scheme in terms of both SE and data detection accuracy.

Reconfigurable intelligent surfaces (RISs) have gained considerable interest because of their inherent passive and energy-efficient design. Integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surfaces (RIS), known as UAV-RIS, can significantly improve network performance and serve as a crucial enabler for advancements in 6G mobile networks. However, ensuring security in UAV-RIS systems poses notable challenges, particularly in the presence of imperfect channel state information (CSI) and beamforming complexities. In this paper, we identify the critical security requirements for UAV-RIS beamforming in practical scenarios. To address these challenges, we introduce a novel deep deterministic policy gradient with a distributional critic (DDPG-DC)-based beamforming approach aimed at securing UAV-RIS systems while improving the overall secrecy rate. Our proposed secure beamforming solution achieves up to a 48% performance improvement compared to existing state-of-the-art algorithms.

Unmanned aerial vehicles (UAVs) in integration with reconfigurable intelligent surfaces (RIS) play a crucial role in improving wireless communication coverage and enhancing overall performance. However, optimizing the beamforming for the RIS and base station (BS) is critical in improving coverage and ensuring connectivity in densely populated areas. The primary challenge in this process arises from the diverse Quality of Service (QoS) requirements set by user equipment (UEs). To address this complexity, machine learning algorithms are employed to predict the optimal beamforming configurations for both the BS and RIS. However, the traditional supervised learning methods are becoming less effective due to the ever-changing demands of UEs, as these methods rely on fixed data patterns that struggle to adapt to the fluctuating QoS requirements of UEs. Thus, in this paper, we propose an unsupervised learning-based deep learning (DL) approach to jointly predict the optimal beamforming matrix for RIS and BS, enhancing communication coverage and maximizing QoS satisfaction of UEs. The proposed DL-based beamforming adaptively predicts the beamforming matrix, facilitates efficient data exploration during the initial learning phase, and seamlessly scales as the process advances, thereby enhancing overall performance. Numerical results demonstrate that the proposed DL-based RIS and BS beamforming outperforms by up to 89%, compared to the state-of-the-art methods.

This paper investigates affine frequency division multiplexing (AFDM) with practical digital-to-analog conversion (DAC) and analog-to-digital conversion (ADC) filters over doubly-dispersive channels. Firstly, we derive the input-output relationship for AFDM with practical DAC and ADC filters, thereby characterizing the discrete affine Fourier transform (DAFT) domain channel. Subsequently, the equivalent sampled DAFT channel matrix is visualized. Additionally, an analysis of the auto-ambiguity function of the AFDM pilot signal is conducted, focusing on the impact of various DAC and ADC filters. The selection of these filters exerts a significant influence on the sidelobe behavior in the time-delay domain of the sub-ambiguity function. Simulations demonstrate that AFDM with practical DAC/ADC outperforms OFDM and OCDM in bit error rate under identical conditions, while the root-raised cosine filter enhances spectral efficiency.

This paper proposes the orthogonal time frequency space-based code index modulation (OTFS-CIM) scheme, a novel wireless communication system that combines OTFS modulation, which enhances error performance in high-mobility Rayleigh channels, with CIM technique, which improves spectral and energy efficiency, within a single-input multiple-output (SIMO) architecture. The proposed system is evaluated through Monte Carlo simulations for various system parameters. Results show that increasing the modulation order degrades performance, while more receive antennas enhance it. Comparative analyses of error performance, throughput, spectral efficiency, and energy saving demonstrate that OTFS-CIM outperforms traditional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. Also, the proposed OTFS-CIM system outperforms benchmark systems in many performance metrics under high-mobility scenarios, making it a strong candidate for sixth generation (6G) and beyond.

This paper proposes a new orthogonal time frequency space (OTFS)-based index modulation system called OTFS-aided media-based modulation (MBM) scheme (OTFS-MBM), which is a promising technique for high-mobility wireless communication systems. The OTFS technique transforms information into the delay-Doppler domain, providing robustness against channel variations, while the MBM system utilizes controllable radio frequency (RF) mirrors to enhance spectral efficiency. The combination of these two techniques offers improved bit error rate (BER) performance compared to conventional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. The proposed system is evaluated through Monte Carlo simulations over high-mobility Rayleigh channels for various system parameters. Comparative throughput, spectral efficiency, and energy efficiency analyses are presented, and it is shown that OTFS-MBM outperforms traditional OTFS and OTFS-SM techniques. The proposed OTFS-MBM scheme stands out as a viable solution for sixth generation (6G) and next-generation wireless networks, enabling reliable communication in dynamic wireless environments.

This paper presents a novel and robust target-to-user (T2U) association framework to support reliable vehicle-to-infrastructure (V2I) networks that potentially operate within the hybrid field (near-field and far-field). To address the challenges posed by complex vehicle maneuvers and user association ambiguity, an interacting multiple-model filtering scheme is developed, which combines coordinated turn and constant velocity models for predictive beamforming. Building upon this foundation, a lightweight association scheme leverages user-specific integrated sensing and communication (ISAC) signaling while employing probabilistic data association to manage clutter measurements in dense traffic. Numerical results validate that the proposed framework significantly outperforms conventional methods in terms of both tracking accuracy and association reliability.

With the flourishing development of sixth-generation wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high-quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this article, multiple-input-single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in detail, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations.

Orthogonal time sequency multiplexing (OTSM) has arisen as a promising single-carrier waveform, providing reliable performance akin to orthogonal time frequency space (OTFS) while surpassing orthogonal frequency division multiplexing (OFDM) in high-mobility doubly spread channels (DSCs), yet with significantly lower complexity. To alleviate both time and frequency dispersions in DSCs, delay-Doppler alignment modulation (DDAM) has been recently proposed for the systems operating at millimeter-wave (mmWave) and higher frequency bands. Building on the promising combination of OTSM with DDAM, this paper presents for the first time, waveform optimization in mmWave massive multiple-input multiple-output (mMIMO) integrated sensing, communication, and wireless power transfer (ISCAP) systems. We propose an OTSM-DDAM-based ISCAP system and derive the system’s input-output relations in time, delay-time, and delay-sequency (DS) domains based on delay-Doppler (DD) bin alignment in the presence of fractional DD shifts and by incorporating transceiver hardware impairments. This facilitates the derivation of key performance metrics including bit error rate (BER), spectral efficiency (SE), Cramér-Rao bound (CRB) for sensing, and energy harvested via wireless power transfer (WPT). By harnessing the advantage actor-critic method combined with a greedy strategy, an on-policy deep reinforcement learning algorithm is introduced to solve a newly defined optimization problem. This approach optimizes the ISCAP waveform while simultaneously recognizing the environment, providing the base station with the path state information needed for practical DDAM implementation. The optimization problem jointly considers precoding, power loading, time and power splitting ratios, beam alignment, and receive combining with the goal of minimizing the transmitted power coupled with CRB while maintaining constraints on signal-to-interference-plus-noise ratio, BER, SE, CRB, harvested energy, and total power budget to ensure high-quality ISCAP services. Simulation results, based on both real-world and deep learning datasets, validate the effectiveness of the proposed scheme, demonstrating improvements in peak-to-average power ratio, SE, detection complexity, CRB, and BER in high-mobility DSCs.

International audience

Molecular communication (MC) is a prominent technique within the Internet of Bio-Nano Things (IoBNT), which aims to interconnect artificial and biological devices for pioneering healthcare applications. Despite its rapid advancements in theoretical study, the experimental development of MC is still in the infancy stage, and most existing testbeds require intruding hardware into the channel during the signal detection process, potentially harming the organism. Against this background, the noninvasive interface across the internal and external information exchange of organisms is of great necessity. Specifically, this work adopts a capacitive sensing mechanism to recover information from the sensed chemical signals. Our testbed demonstrates a notably higher transmission rate than previously reported noninvasive MC approaches, underscoring its potential to advance IoBNT research.

This paper investigates the physical layer security (PLS) issue of unmanned aerial vehicle (UAV) aided multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) networks with randomly distributed passive eavesdroppers (Eves). Considering Nakagami-m fading, we propose a novel secure communication protocol that integrates transmit antenna selection (TAS) and maximum ratio combining (MRC) diversity technology. Specifically, to tackle the challenges of low spectrum efficiency and PLS performance, we propose two TAS solutions: TAS-max UN and TAS-max UF, the first one aims to enhance the performance of UN and the other one focuses on UF. To mitigate the impact of passive eavesdropping, a secure protected zone is established around the UAV to limit the Eve’s ability. Accordingly, we derive the closed-form expressions for the ergodic secrecy rate to evaluate the impact of spatial randomness. Then, the accuracy of the derived expressions is verified through Monte-Carlo simulations.To further support the theoretical analysis, asymptotic expressions under the high-SNR regime are derived, which offer valuable insights into secrecy rate trends and model convergence. Moreover, we propose a three-dimensional UAV deployment optimization framework that adopts a hybrid approach combining grid-based evaluation and Genetic Algorithm refinement, which improves ESR performance while significantly reducing computational complexity. In addition, a Simulated Annealing based power allocation scheme is introduced to optimize the power coefficient aF, achieving enhanced secrecy rate with improved search efficiency and adaptability. Extensive simulation results confirm that the proposed TAS/MRC framework, together with the secure protected zone, consistently outperforms conventional OMA and MRT schemes in terms of secrecy rate and robustness. The impact of key system parameters, including power allocation, UAV altitude, antenna configuration, and Eve density, is also thoroughly analyzed.

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.

This paper proposes a blockchain-integrated federated learning (FL) framework tailored for secure, efficient, and energy-aware model training in edge computing environments. The framework follows an offload-train-aggregate paradigm where edge devices transmit local datasets to proximate servers for localized model updates. A reputation-driven RAFT consensus protocol is incorporated to achieve reliable, low-latency, and lightweight blockchain coordination while preserving privacy and accountability. To overcome the inherent mixed-integer nonlinear programming (MINLP) complexity, we develop a two-stage cross-layer optimization strategy. In the first stage, an alternating direction method of multipliers (ADMM)-based feedback control scheme jointly allocates bandwidth and computation resources under energy and delay constraints. In the second stage, server selection and sub-band assignment are modeled as a bipartite matching problem and solved via the Hungarian algorithm, guided by a convergence-aware performance bound. Extensive simulations demonstrate that our framework significantly improves learning accuracy, uplink throughput, and energy efficiency over state-of-the-art FL baselines. It also exhibits strong robustness to network fragmentation and resource heterogeneity, making it well suited for practical edge environments.

This paper establishes a molecule harvesting transmitter (TX) model in molecular communication (MC). In particular, we consider that molecules are encapsulated in vesicles generated within the TX and released from the TX through membrane fusion process. We also consider that the TX membrane is covered by heterogeneous receptors of varying sizes and at arbitrary locations, where the receptors can absorb the released molecules once the molecules hitting any of the receptor. Assuming that the vesicle generation follows a jump process, with each vesicle generated at distinct time instants, and assuming a transparent receiver (RX), we calculate the molecule release rate, the expected fraction of absorbed molecules at the TX, and the received signal at the RX. All obtained analytical expressions are functions of all receptors’ locations and sizes, and are validated by particle-based simulations. Our numerical results indicate that evenly distributed receptors on the TX membrane absorb more molecules than randomly distributed receptors or a single receptor. Furthermore, inspired by the biological phenomenon that cells can regulate their release of new molecules by interacting with the molecules that are already present in the environment, we incorporate a negative feedback mechanism (NFM) at the TX. This mechanism utilizes the number of molecules absorbed by the TX as a criterion to determine if the TX should stop releasing additional molecules. We then derive the closed-form expression for the expected fraction of recyclable molecules for a single emission. Here, the pool of recyclable molecules comprises both the molecules that remain unreleased by the TX due to NFM and those that are absorbed back by the TX. Our numerical results demonstrate that incorporating NFM can reduce inter-symbol interference (ISI) while maintaining the same peak received signal as without NFM. Additionally, our results show that TXs incorporating both molecule harvesting and NFM can achieve a higher energy efficiency and lower error probability than TXs employing only molecule harvesting or neither functionality.

As a new paradigm focusing on transmitting the meaning of information, semantic communications (SCs) have been revealed significant potential in alleviating network congestion and improving energy efficiency. By extracting a small-size semantic feature from the large-size raw-data, the communication loads can be reduced at a price of more computing power, i.e., the computational resource used in semantic extraction. In this paper, we explore the computation dimension to improve the communication performance in an uplink SC system. The SC-oriented user first compresses its original data via local computing during other users’ transmission time and then transmits it to the base station within the assigned time. To achieve a balanced tradeoff between communications and computing, we formulate an optimization problem to minimize the energy consumption of all users by jointly considering the compression ratio and time allocation. We first propose an efficient general algorithm that can be applied to different SC models. To gain more insights, we then derive the closed-form solutions for two special cases: equal-time allocation and two-user transmission. The obtained analytical results reveal a pronounced energy saving of adopting SC, especially for large transmitted data size, high transmission energy coefficient, scarce time resources, and poor channel conditions. Simulation results reveal that our proposed SC-based scheme achieves excellent performance in terms of saving energy consumption compared to the conventional communication.

With the further investigation and utilization of the extremely large-scale antenna array (ELAA) and Terahertz (THz) band, as well as the increasingly stringent standards of regulatory agencies, the near-field communications are evolving into the norm of intelligent transportation systems. In this work, we propose a novel beamfocusing scheme to improve the system performance for the reconfigurable intelligent surface (RIS)-aided hybrid near- and far-field communications with multi-unmanned aerial vehicle (UAV). Compared to conventional schemes without considering the near-field beam pattern with a finite depth, in our proposed scheme, the RIS only serves one UAV, while other UAVs are within the radiation range of the concentrated signal energy by beamfocusing. Specifically, we study the polar radius and angular deviations between UAVs with a given beamfocusing gain, aiming to reveal the impact of RIS structure on beamfocusing gain. We also make a fair comparison between the proposed scheme and allocated RIS scheme, deriving the feasible reference distance. In addition, we study the feasibility for different RIS structures focusing on the beamfocusing gain, which has the potential to improve the system performance. Simulation results demonstrate that the proposed scheme is always superior than that of the allocated RIS scheme within the feasible reference distance.

In the rapidly evolving field of wireless communications, the emergence of 5G and the progression towards 6G technologies highlight the demands for innovative frameworks capable of enhancing network performance under complex environmental factors. According to this trend, this work introduces a novel system model for aerial reconfigurable intelligent surface (ARIS)-assisted wireless communications, engineered to adeptly manage dynamic environmental influences such as fluctuating wind patterns and variable weather conditions. By treating these influences as random variables, we further introduce unpredictable variations in ARIS orientation (roll, yaw, and pitch), affecting network efficiency and reliability. To mitigate these environmental perturbations and uncertainties in the channel state information (CSI), we propose a robust beamforming strategy to reshape the original optimization problem into a form that is easier to analyze and solve, by employing stochastic optimization, successive convex approximation (SCA), and block coordinate descent (BCD) techniques to optimize active beamforming vectors and ARIS phase shifts. Comprehensive simulations rigorously evaluate the performance of our proposed algorithms across diverse conditions, including aerial disturbances, varying channel states, and different RIS element configurations. The results underscore the efficacy of our beamforming design in boosting the resilience and dependability of ARIS-enhanced wireless networks.

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks.

In this paper, we focus on computation-aware offloading for executing deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we resort to semantic communications (SCs), through which the users can offload the compressed task data to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We first consider a special case with the preconfigured time scheduling and derive closed-form solutions to computation allocation and offloading time, which yield a threshold-based structure determined by users’ channel conditions and local computation consumption. Inspired by the characteristics of these optimal solutions, a general low-complexity iterative algorithm is then designed to solve the original non-convex problem. Simulation results demonstrate that our proposed SC-based computation -offloading scheme can substantially reduce users’ energy consumption compared to the conventional offloading and full offloading, especially with scarce wireless 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.

Edge intelligence is expected to revolutionize the industrial Internet of Things (IoT) by providing proximal intelligent services to massive low-cost IoT devices. However, integration of the two paradigms needs to simultaneously maximize the edge quality of training (QoT) and IoT quality of service (QoS) under time-varying co-channel interference, for which the existing edge or IoT resource allocation algorithms become ineffective, as they ignore the contradiction between learning performance and communication requirements. This paper proposes an edge intelligence industrial IoT (EI3) framework, which jointly maximizes QoT and QoS through a newly derived learning-communication balancing power allocation (LCBPA) formulation. An efficient algorithm is proposed to solve the non-convex and non-smooth LCBPA problem. Simulation results demonstrate that the proposed LCBPA scheme achieves superior performance compared to several benchmarks in terms of the desired learning accuracy and qualified transmission rate. It is also shown that EI3can adapt to new scenarios by flexibly adjusting the importance factor between learning and communication.

Millimeter wave (mmWave) communications are highly affected by blockage, whereas the emerging reconfigurable intelligent surface (RIS) has the potential to overcome this issue. This paper proposes a Neyman-Pearson (N-P) criterion-based blockage-aware algorithm to improve resilience to blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for RIS-assisted mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. Specifically, we propose an accelerated projected gradient descent (PGD) algorithm to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we formulate a new Nesterov momentum acceleration scheme to speed up the convergence rate. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate performance.

Involving proper boundaries is an important aspect of molecular communication, especially when considering it as a potential candidate for intra-body communication. Inspired by the massive cellular environments, this paper proposes a novel approach to model the channel for a diffusive molecular communication (DMC) system in a bounded spherical environment. Specifically, this spherical boundary is made up of a reflecting and absorbing portion, whose property depends on whether it is covered by absorbing receptors (ARs). To obtain an accurate channel impulse response (CIR), we employ the homogenization technique to convert the mixed Dirichlet-Neumann boundary into a Robin boundary. Assuming these ARs with the same size and uniform distribution, we derive a valid CIR expression for the bounded DMC system with arbitrary sizes and numbers of ARs. Numerical results based on the particle-based simulation validate our analysis.

Today's wireless communication networks have many requirements such as high data rate, high reliability, low latency, low error data transmission, and high energy efficiency. High-performance index modulation (IM) techniques and reconfigurable intelligent surface (RIS) technology, which has recently attracted the attention of researchers, are strong candidates to meet these requirements. This paper introduces a novel RIS-supported code IM-based receive spatial modulation (RIS-CIM-RSM) system. The proposed RIS-CIM-RSM system uses quadrature amplitude modulation (QAM) symbols, receive antenna indices, and spreading code indices for wireless data transmission. In the proposed system, an RIS applies a phase rotation that maximizes signal-to-noise ratio (SNR) to the signals coming to the reflecting elements and directs them to the selected receive antenna. Performance analyses of the proposed RIS-CIM-RSM system such as data rate, throughput, and energy saving are obtained. The results obtained show that the proposed RIS-CIM-RSM system is superior to the counterpart RIS-based IM systems in the literature in terms of data rate, throughput, energy saving, and error performance.

Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates the arrival time and carrier frequency offset by comparing the correlations of samples. Moreover, we propose the other so-called stepwise ML estimator to reduce the complexity. Both proposed estimators exploit the redundant information contained within the chirp-periodic prefix inherent in AFDM symbols, thus dispensing with any additional pilots. To further mitigate the intercarrier interference resulting from the residual frequency offset, we design a mirror-mapping-based scheme for AFDM systems. Numerical results verify the effectiveness of the proposed time and carrier frequency offset estimation criteria and the mirror-mapping-based modulation for AFDM systems.

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.

Cybertwin technology introduces a novel paradigm employing digital twins to model complex physical systems within a cyber environment, thus enhancing communication, collaboration, and decision-making capabilities. By harnessing advanced technologies, such as reconfigurable intelligent surfaces (RISs) and multiaccess edge computing (MEC), seamless interaction between physical and virtual entities is facilitated. In this article, we propose a cybertwin-driven edge computing framework that leverages RIS technology, complemented by an efficient computing offloading strategy to support large-scale Internet of Everything (IoE) applications. Specifically, the proposed strategy focuses on a multicell system where numerous randomly distributed end users have the option to offload delay-sensitive and computing-intensive tasks to edge computing nodes. The offloading channels are enhanced by RISs through passive beamforming, while cybertwin technology directs resource cooperation among multicells and allocates computing and communication resources. Our main objective is to optimize the system’s utility with respect to task completion latency and energy consumption reduction. To achieve this goal, we conduct the joint optimization of task offloading and resource allocation. Furthermore, we develop a joint task offloading and resource allocation (JTORA) algorithm to derive optimal solutions for passive beamforming design, computing offloading decisions, communication resource scheduling, and computing capacity allocation. The simulation results demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of edge computing efficiency. Furthermore, the system utility can be further enhanced by increasing the number of embedded RIS elements.

As a new candidate waveform for the next generation of mobile communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its high spectrum efficiency and robustness to narrow-band interference or impulsive noise. Under vehicular communication channels with multiple lags and multiple Doppler shifts (MLMD), the signal suffers doubly selective (DS) fadings in the time and frequency domain, and data symbols modulated on orthogonal chirps interfere with each other. To address the problem of symbol detection of OCDM over MLMD channels, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in the Fresnel domain and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message-passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communications and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analyses are provided to validate its advantages as a promising detector for OCDM.

In this article, we study a previously overlooked work of designing the constellation for concentration shift keying (CSK) in diffusion-based molecular communication (MC) systems, considering the adverse effects of signal-dependent noise and intersymbol interference (ISI). Against this background, the conventionally used uniform constellation strategy is no longer the optimal choice for CSK. Considering the discrete nature of messenger molecules, the constellation design of CSK is essentially a nonlinear integer programming problem. However, due to the nonconvexity of the goal function, general convex optimization methods cannot be easily employed. To solve the aforementioned problem, a novel CSK strategy with a brute-force search algorithm is proposed to obtain the optimal constellation numerically. Moreover, a low-complexity search algorithm that provides new insight into reducing the complexity of MC systems is further presented. Specifically, we theoretically derive the optimal threshold and employ a maximum likelihood detector that effectively minimizes the average symbol error rate (SER) by leveraging the knowledge of ISI at the receiver. Finally, we can obtain a fitted equation for the nearly optimal constellation points under specific channel parameters, attaining the SER performance similar to the optimal one, while the search complexity is much lowered.

This article investigates physical-layer security (PLS) for an energy-harvesting cognitive network with nonorthogonal multiple access (EHCN-NOMA), where a cognitive base station (CBS) communicates with two cognitive users named cognitive near user (CNU) and cognitive far user (CFU) by adopting the NOMA principle under an active or passive eavesdropper (Eve) attack. By means of energy harvesters, the CBS collects the radio frequency energy from the primary source (PS) by a time-switching protocol; meanwhile, the cognitive transmit power is limited by the interference threshold of primary destination (PD) to guarantee the Quality of Service (QoS) of primary transmissions. To evaluate the impact of active and/or passive eavesdroppings on system performance, we derive closed-form expressions in terms of outage probabilities (OPs), intercept probabilities (IPs), as well as effective secrecy throughputs (ESTs). The numerical results verify the correctness of our theoretical derivations and confirm that there exists a tradeoff between security and reliability for the EHCN-NOMA transmissions. Moreover, the EST improvement depends on the competition between security and reliability. Furthermore, the maximum EST performance can be achieved for both eavesdropping scenarios by adjusting the time allocation between that of the energy transfer phase and the information transmission phase.

To provide sustainable energy support while meeting the demands for serving a rapidly increasing number of devices, in this paper, we propose a new Reconfigurable intelligent surface (RIS) Assisted simultaneous wireless information and Power transfer (SWIPT) and over-the-aIr computation (AirComp) feDerated learning system which is termed RAPID. Specifically, at each training iteration, an access point (AP) first simultaneously broadcasts global model and transfers wireless energy to the selected devices via the RIS-assisted SWIPT. These devices then use the harvested power to compute and upload their local gradients to the AP for aggregation via the RIS-assisted AirComp. To identify the performance improvement to federated learning, we first analyze and derive the expected convergence rate for the proposed RAPID system taking into account factors of device selection and wireless communication. We then formulate a joint learning-communication optimization problem in terms of device selection, transmit beamforming, power splitting, receive beamforming, and RIS coefficients design. To solve the formulated non-convex problem, we propose a new two-stage algorithm by successively solving the downlink SWIPT and the uplink AirComp sub-problems based on alternating optimization and successive convex approximation techniques. Simulation results are presented to demonstrate that our proposed RAIPD system can significantly improve the convergence and accuracy of federated learning compared with benchmark schemes.

In this paper, a novel active fully-connected reconfigurable intelligent surface assisted space shift keying and code index modulation (AFRIS-SCIM) scheme is proposed. On one hand, by introducing joint space-code index modulation while maintaining low power consumption and complexity, the proposed scheme achieves higher data rates compared to existing one-dimensional index modulation. On the other hand, the proposed active fully-connected architecture achieves a desirable trade-off between reliability and power consumption compared to conventional passive RIS and active RIS architectures. Additionally, to reduce detection complexity at the receiver, a low-complexity detection algorithm is proposed and the upper bound for the bit error rate (BER) of the system is derived. Mathematical models characterizing the system complexity and power consumption are also established to analyze the overall performance. Both theoretical analyses and simulation results demonstrate that the AFRIS-SCIM scheme outperforms existing RIS-IM schemes as well as multidimensional index modulation systems in terms of BER performance.

The reconfigurable intelligent surface (RIS) aided index modulation (IM) is a promising technology for next-generation wireless communications. However, acquiring channel state information (CSI) for RIS-based IM requires expensive pilot overhead, especially for the IM in multiple domains. In this paper, a novel rectangular differential reflecting spatial modulation (RDRSM) system is proposed for the RIS-aided multiple-input multiple-output (MIMO) system. Specifically, the proposed multi-antenna-activated RDRSM (M-RDRSM) scheme utilizes a rectangular dispersion matrix (DM) to jointly map a digital beamforming (DBF) weight vector and a RIS reflection pattern to perform the rectangular differential modulation. A thorough analysis presents that the proposed M-RDRSM scheme can achieve high-spectral efficiency, low complexity of the system, and noncoherent decoding without prior knowledge of CSI in the space-reflection dual-domain IM. Further, the proposed M-RDRSM scheme is simplified to a single-antenna-activated RDRSM (S-RDRSM) scheme, which can reduce the hardware cost and avoid the inter-antenna synchronization problem more effectively. Simulation results demonstrate that the proposed RDRSM scheme performs better in terms of bit error rate performance and achieve a lower decoding complexity than the existing correlated differential IM schemes with the same achievable spectral efficiency.

In this paper, we propose a secure unmanned aerial vehicle (UAV) communication system with the assistance of a reconfigurable intelligent surface (RIS), where UAV trajectory design and artificial noise are incorporated to prevent eavesdropping from multiple colluding curious users. To maximize the secrecy rate of the proposed system, we undertake a joint optimization process that encompasses the trajectory of the UAV, the RIS phase shifts, and the beamforming vectors for both information and artificial noise signals, considering the constraints of the UAV transmit power, UAV flying speed and the phase shifts. To address the non-convex nature of the joint problem and handle the coupling effects of multiple parameters, we conduct the problem decomposition by using the block coordinate descent (BCD) method, combined with an alternating algorithm to optimize the decomposed sub-problems. To further tackle the non-convexity in sub-problems, we apply the successive convex approximation (SCA) method to circumvent the trajectory optimization problem and to optimize the beamformers of information and artificial noise signals, while a majorization-minimization (MM) based scheme is adopted for the RIS phase shifts optimization. Numerical simulation results substantiate the convergence and effectiveness of the proposed algorithm through the comparison with benchmark methods, and our proposed scheme is proven to achieve a significant improvement in average secrecy rate across various conditions.

In this paper, we present a novel method by using a sliding window filter (SWF) for real-time kinematic (RTK)-visual-inertial navigation. Unlike other recent works that retain only the states of visual keyframes to reduce computational complexity, we additionally retain the GNSS states (i.e., position, orientation, and velocity of the body and inertial biases at the time of capturing GNSS measurements) in the SWF to construct more appropriate constraints between measurements and states. In order to make the method run as a real-time system, especially when the SWF contains numerous GNSS states, we propose a parallel elimination strategy in a predefined elimination ordering, which can solve the Gauss-Newton problem and simultaneously obtain the covariance for ambiguity resolution (AR). We reveal when and how the system improves the AR performance. Moreover, we analyze the observability of the system under different conditions. We also conduct experiments in the real world and compare the results with the state-of-the-arts. Experimental results show that the proposed method is able to achieve a higher and stabler fixed rate in GNSS challenging environments, and has better positioning performance with or without measurements of a base station. We have decided to publish the code of our work for the community1.

Full duplex (FD) radios have attracted extensive attention due to the co-time and co-frequency transceiving capability. However, the potential gain brought by FD radios is closely related to the management of self-interference (SI), which imposes high or even stringent requirements on SI cancellation (SIC) techniques. When the FD deployment evolves into next-generation mobile networking, the SI problem becomes more complicated, significantly limiting its potential gains. In this paper, we conceive a multi-cell FD networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. To achieve the full potential of the system, we aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and users, as well as the phase shift matrix at the RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between the SR and minimum mean square error (MMSE). The optimal solutions of TPC matrices are obtained in closed form, while both complex circle manifold (CCM) and successive convex approximation (SCA) based algorithms are developed to resolve the phase shift matrix suboptimally. Our simulation results show that introducing an RIS into an FD networking system not only improves the overall SR significantly but also enhances the cell edge performance prominently. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for SIC and the number of BS antennas, which further reduces the hardware cost and power consumption, especially with a sufficient number of reflecting elements. As a result, the utilization of an RIS enables the originally cumbersome FD networking system to become efficient and practical.

To fully exploit the potential of reconfigurable intelligent surface (RIS), the controllable channel state information (CSI) should be accurate for its future applications. Unfortunately, in vehicular communications, obtaining exact instantaneous CSI presents substantial challenges. Moreover, even with an instantaneous CSI acquisition, a processing latency for RIS phase shift adaption might occur before the vehicular system reacts to the instantaneous CSI information. To effectively introduce RIS into Internet of vehicle (IoV) networks, we employ a more realistic statistical CSI approach in designing RIS-assisted vehicular communication systems that are robust to the general characteristics of the channel, rather than its instantaneous fluctuations. We present a practical system framework, where a roadside unit employs an RIS to facilitate indirect wireless communications for vehicle-to-vehicle (V2V) communications. Particularly, the direct links between vehicles are susceptible to blockages caused by surrounding obstacles/vehicles. The deployment of RIS is to establish supplementary communication links between a multi-antenna vehicle source (VS) and multiple vehicular users (VUs) as they traverse areas with a poor service coverage. With the objective to maximize the time-averaged sum-rate of VUs, instead of instantaneous CSI, we rely on the delayed statistical CSI feedback to design active beamforming at the VS and passive beamforming at RIS. Moreover, we develop an efficient algorithm, named JAPBNB, which leverages the fractional programming technique to find a stationary solution for the formulated sum-of-logarithms-of-ratio problem. Specifically, a non-convex block coordinate descent (BCD) approach, collaborating with the alternating direction method of multipliers (ADMM), is applied for the joint optimization of active and passive beamforming. Finally, the complexity and convergence of the proposed JAPBNB algorithm are thoroughly discussed and validated. Simulation results demonstrate that the time-averaged sum-rate obtained by the proposed JAPBNB algorithm approaches that obtained by the instantaneous CSI scheme, when the delayed statistical CSI feedback interval is adequately small.

Underwater acoustic (UWA) channels present diverse sparsity, which is challenging for UWA communications and underwater networked applications. Conventional methods fail to adapt to the uncertain sparsity due to inflexible adjustment rules or improper assumptions. This paper investigates the estimation of channels with uncertain sparsity and model-driven deep learning (DL) based estimation networks are proposed. We construct the channel estimation as sparse signal recovery model and exploit the channel sparsity based on the approximate message passing (AMP) method. Then the learned AMP network with soft-thresholding shrinkage function (ST-LAMP) is designed as a generalized estimator and suitable to random sparse UWA channels. Moreover, to achieve the Bayesian optimal solution, Gaussian mixture (GM) prior is imposed for UWA channels and the GM-LAMP network is formulated with the derived shrinkage function based on the minimum mean squared error criterion. The proposed networks incorporate the learning ability of DL methods by exploiting marine data and the domain knowledge from the classical model to realize parameter configuration and adaptive optimization. Experimental results verify that the proposed networks outperform benchmarks over UWA channels with diverse sparsity in terms of adaptability and effectiveness. Especially, real tests present the feasibility of proposed networks for on-line deployments.

In this paper, a novel amplitude phase shift keying (APSK) modulation scheme for cooperative backscatter communications aided by a reconfigurable intelligent surface (RIS-CBC) is presented, according to which a passive or an active RIS is configured to modulate backscatter information onto unmodulated or PSK-modulated signals impinging on its panel via APSK. In passive RIS-CBC-APSK, the backscatter information is conveyed through the number of RIS reflecting elements being in the ON state and their phase shift values, whereas, in active RIS-CBC-APSK, this information is embedded through the number of RIS elements being in the active mode as well as the phase shift values of all elements. By using the optimal APSK constellation to ensure that reflected signals from the RIS undergo APSK modulation, a bit-mapping mechanism is developed. Assuming maximum-likelihood detection, we also present closed-form upper bounds for the symbol error rate (SER) performance for both proposed passive and active RIS-CBC-APSK schemes over Rician fading channels. In addition, we devise a low-complexity detector that can achieve flexible trade-offs between performance and complexity. Finally, we extend RIS-CBC-APSK to multiple-input single-output scenarios and present an alternating optimization approach for the joint design of transmit beamforming and RIS reflection. Our extensive simulation results on the SER performance of the proposed RIS-CBC-APSK framework corroborate our conducted performance analysis and showcase the superiority of both designed modulation schemes over the state-of-the-art RIS-CBC benchmarks.

Recently, intelligent reflecting surface (IRS) emerges as an effective technique for saving power consumption by customizing the wireless propagation environment. On the other hand, the symbol level precoding (SLP) technique provides a clever solution to interference exploitation by converting the multiuser interference (MUI) into a beneficial part of the desired signal. In this paper, we propose to jointly exploit IRS and SLP to cope with the power control and interference management issues in a heterogeneous network (HetNet). Considering the possible coordination between the macro base station (MBS) and the pico base station (PBS), we propose two corresponding schemes to manage the inter-cell and intra-cell interference. For both proposed schemes, the power minimization problems are studied by jointly optimizing the precoding matrices at the MBS and PBS as well as reflecting coefficients at the IRS. Due to the non-convexity of these problems, the precoding matrices and reflecting coefficients are optimized alternately. We propose two Lagrangian based algorithms to obtain the optimal solutions of the precoding matrices, where the precoding matrix of the MBS always yields a closed-form. A multiple-gradient descent algorithm based on the Riemannian manifold (MGD-RM) is proposed as well to enhance the received signal quality of each MUE and PUE for the reflecting design. Simulation results manifest a significant performance gain achieved by our proposed HetNet over benchmarks.

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to multiple macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. Furthermore, to explore the potential advantage of common streams in RSMA systems, we develop a “Hybrid RSMA” scheme in which the MBS only employs RSMA to encode the backhaul messages while each MCU’s message is directly encoded without rate-splitting. We formulate an optimization problem to maximize the system’s spectral efficiency (SE) by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we first propose an iterative centralized algorithm based on successive convex approximation (SCA). Then, we further develop an efficient decentralized algorithm that can be executed in parallel at the MBS and each SBS based on the local channel state information with fewer signaling exchanges. Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. Particularly, “Hybrid RSMA” has a greater performance improvement than “RSMA” in the underloaded system.

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.

In this paper, we propose a multi-cell full-duplex (FD) networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. We aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and the phase shift matrix at RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between SR and minimum mean square error. The optimal solutions of TPC matrices are obtained in closed form, while a successive convex approximation-based algorithm is developed to resolve the phase shift matrix suboptimally. Simulation results show that introducing an RIS into the FD networking system can improve the overall SR significantly. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for self-interference cancellation (SIC) and the number of BS antennas effectively, especially with enough reflecting elements. As a result, the utilization of RIS enables the originally cumbersome FD networking system to become efficient and practical.

Sensing, computation, and communication (SC2) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC2resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC2resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC2resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

In this paper, we redesign the concentration shift keying (CSK) constellation design for diffusion-based molecular communication systems, where both signal-dependent noise and inter-symbol interference (ISI) are considered. The conventionally used uniform constellation strategy is no longer the optimal choice when signal-dependent noise exists. Against this background, a novel CSK constellation strategy with the maximum likelihood detector is presented by minimizing the symbol error rate (SER) with statistical ISI knowledge. The well-matched analytical and simulated results in terms of SER demonstrate the effectiveness of our proposed CSK constellation design.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. In order to improve the system spectral efficiency (SE) while guaranteeing the quality-of-service (QoS) of each user, we formulate an optimization problem to maximize the sum SE by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we propose an iterative algorithm based on successive convex approximation (SCA). Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. In addition, RSMA has better flexibility than other benchmark schemes in meeting the increasing QoS requirements of users.

In wireless powered communication networks (WPCNs), the wireless energy acquisition is weak due to the fading characteristic of wireless link, which is hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This paper proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by the RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. So we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that the throughput of the multi-user system can be significantly improved.

Reconfigurable intelligent surface (RIS)-aided symbiotic active/passive transmission is a promising communication paradigm, which is able to improve the propagation environment while transmitting additional information. In this article, a novel scheme, termed RIS-aided number modulation (RIS-NM), is proposed for symbiotic active/passive communications. In RIS-NM, the RIS elements are divided into in-phase (I-) and quadrature (Q-) subsets depending on their phase shift configurations, and the number of elements in the I-subset (or Q-subset, equivalently) is used to convey the RIS’s private information. A low-complexity yet near-optimal detector is designed for RIS-NM by shrinking the search space of constellation points. We then investigate a special case of RIS-NM, termed RIS-aided number shift keying (RIS-NSK), in which the radio-frequency source transmits unmodulated carrier signals. Statistic channel state information (CSI)-based maximum-likelihood (ML) detection is developed for RIS-NSK. We analyze the bit error rate (BER) performance of RIS-NM/NSK over Rician fading channels. BER upper bounds are derived in closed form for RIS-NM by assuming instantaneous CSI-based ML detection, while an approximate BER expression is obtained for RIS-NSK by assuming statistic CSI-based ML detection. Furthermore, we extend RIS-NM to multiple-input multiple-output scenarios. Our simulation results in terms of BER corroborate the performance analysis and the superiority of RIS-NM over the state-of-the-art RIS-aided symbiotic active/passive transmission scheme.

Industrial Internet of Things (IIoT) improving by leaps and bounds has brought new possibilities for industrial manufacturing. Meanwhile, it does bring some serious challenges with the increasing number of devices. In which massive connectivity and multiservice are two major challenges for IIoT, in order to address the two main issues, in this article, we jointly consider nonorthogonal multiple access (NOMA) and wireless network slicing scenario, where multiservice devices share the same communication resources. To connect devices as many as possible, we formulate the connectivity maximization problem with joint subcarrier association and power allocation as a mixed-integer nonlinear programming (MINLP) problem, under the constraints of limited communication resources. To solve the problem effectively, we first split the MINLP problem into two subproblems by introducing a power allocation weight. Then, we analyze the theoretical approach for a special case and propose the layered access (LA) algorithm for general cases. Furthermore, a bisection search (Bisearch) algorithm is devised to find out the optimal power allocation weight. Simulation results show that the proposed LA algorithm has better performance compared to other benchmark schemes.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ$\epsilon $-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proven to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI).

Molecular communication (MC) is an emerging field aiming at realizing information exchange via chemical signals between nanomachines in nanonetworks. Information transmission that is energy-efficient and relies on relatively low-complexity transceiver techniques is of practical importance for MC systems. Based on the fact that constellation shaping can improve energy efficiency, in this paper, we propose a molecular shell mapping (MSM) scheme to implement the probabilistic constellation shaping for MC. The MSM method is designed to exploit the concentration sequences with the lowest sum sequence weight, which results in an energy-efficient signal constellation. Furthermore, we propose an algorithm for selecting and sorting the concentration sequences to mitigate inter-symbol interference. For information detection, we design a genie-aided maximum-likelihood (ML) detector and a realistic ML detector to leverage the constructive effect of intra-sequence interference, as well as derive their bit error rates and achievable rates. Additionally, for the applications using large blocklength sequences, a low-complexity ML detection method is proposed. Numerical simulation results confirm that the shaped signaling using the MSM method is more energy-efficient than the conventional equiprobable signaling, achieving shaping gains of up to 1.5 dB at an ultra-short blocklength of 4.

Due to the inherent broadcast characteristics of wireless communications, the data transmission is difficult to be shielded from unintended recipients. Secure communications over wireless channel is regarded as an effective way to overcome this issue in the design of wireless networks. In this paper, a new cooperative relaying system based on partial non-orthogonal multiple access (P-NOMA) is proposed, where two relays help the communications between the source and destination nodes in the presence of an eavesdropper (Eve). Specifically, after receiving the P-NOMA signals transmitted from the base station, the relay nodes decode and forward the receptions under the attack of a passive Eve. To improve the physical layer security (PLS) of the proposed system, we design four cooperative schemes for signals decoded at the relays and destination, where maximum ratio combination (MRC) technique is adopted to both destination and Eve to construe the worst case for achievable secrecy rate. Without lose of generality, the channel conditions (weak or strong), new power allocations, decoding principles and Eve locations are considered in designing forwarding schemes. In particular, to further improve the achievable secrecy rate, we also propose that, if the Eve locating near to the weak relay node, the weak relay only forwards the signal with lower power allocation factor at the base station. The closed-form expressions of the achievable secrecy rates are derived for the proposed schemes over Rayleigh fading and Nakagami-$m$fading channels, which well match the simulation results. By means of the numerical result, it corroborates the superiority of the proposed P-NOMA schemes over the conventional NOMA schemes. It is also revealed that, with an increasing overlap ratio, the advantage in terms of the secrecy rate becomes more remarkable.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed$\epsilon$-Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

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, a cyclic-prefixed single-carrier (CPSC) transmission scheme with phase shift keying (PSK) signaling is presented for broadband wireless communications systems empowered by a reconfigurable intelligent surface (RIS). In the proposed CPSC-RIS, the RIS is configured according to the transmitted PSK symbols such that different cyclically delayed versions of the incident signal are created by the RIS to achieve multipath diversity. A practical and efficient channel estimator is developed for CPSC-RIS and the mean square error of the channel estimation is expressed in closed-form. We analyze the bit error rate (BER) performance of CPSC-RIS over frequency-selective Nakagami-$m$fading channels. An upper bound on the BER is derived by assuming maximum-likelihood detection. Furthermore, by applying the concept of index modulation (IM), we propose an extension of CPSC-RIS, termed CPSC-RIS-IM, which enhances the spectral efficiency. In addition to conventional constellation information of PSK symbols, CPSC-RIS-IM uses the full permutations of cyclic delays caused by the RIS to carry information. A sub-optimal receiver is designed for CPSC-RIS-IM to aim at low computational complexity. Our simulation results in terms of BER corroborate the performance analysis and the superiority of CPSC-RIS(-IM) over the conventional CPSC without an RIS and orthogonal frequency division multiplexing with an RIS.

Reconfigurable intelligent surface (RIS)-empowered communication has emerged as a novel concept for customizing future wireless environments in a cost- and energy-efficient way. However, due to double path loss, existing fully passive RIS systems that purely reflect the incident signals into preferred directions attain an unsatisfactory performance improvement over the traditional wireless networks in certain conditions. To overcome this bottleneck, we propose a novel transmission scheme, named hybrid reflection modulation (HRM), exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains. In the HRM scheme, the active reflecting elements using additional power amplifiers are able to amplify and reflect the incoming signal, while the remaining passive elements can simply reflect the signals with appropriate phase shifts. Based on this novel transmission model, we obtain an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach. Moreover, comprehensive computer simulations are performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems.

Industrial Internet of Things (IIoT) is a technological revolution that is profoundly reshaping the visage of industry. Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable. The advent of the fifth-generation (5G) wireless networks brings unprecedented possibilities for deploying the anticipated IIoT. To address the two main issues, i.e., connection density and multi-service requirements, in 5G empowered IIoT, we consider the non-orthogonal network slicing in this work. In particular, we jointly utilize network slicing to incorporate different types of services and exploit non-orthogonal multiple access (NOMA) to enhance the connection density. We formulate the connectivity maximization problem with joint sub-carrier association and power allocation as a mixed-integer nonlinear programming (MINLP). To tackle the intractable MINLP, we first transform it into a mixed-integer linear programming (MILP) and then simplify the MILP into an integer linear programming (ILP) by developing a simple yet effective pairing guideline. In order to further reduce the computational complexity, we then propose the alternating selection best-effort pairing (AS-BEP) algorithm with low complexity to solve the ILP effectively. Our analyses are supplemented by comprehensive simulation results that illustrate the performance superiority of the proposed algorithms to the benchmark schemes.

Reconfigurable intelligent surface (RIS) has the revolutionary ability to customize the radio propagation environment for enhancing the secure transmission performance. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the channel state information (CSI) is imperfectly obtained at the base station, leading to uncertain CSI. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper investigates the energy efficient secure transmission while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling the variables, the secure energy-efficiency maximization problem can be solved via alternatively executing the concave-convex procedure and penalty-based method. Simulation results show that the proposed RIS-aided secure transmission scheme significantly improves the energy-efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Overcoming inter-symbol interference (ISI) is one of the key challenges in the design of molecular communication systems. In this paper, we propose a scheme for optimizing the detection interval to minimize the impact of ISI while ensuring the acquisition of effective information. Our detection interval optimization applies to both the absorbing and passive receivers. For analysis, we consider as the performance metrics signal-to-interference difference (SID) and signal-to-interference and noise amplitude ratio (SINAR) proposed in the literature rather than the intractable bit error rate (BER). Accordingly, we derive the optimal detection interval in closedform. Finally, simulation results in terms of BER verify the theoretical analysis and also show the promising advantages of the proposed scheme in signal detection.

To achieve huge passive/reflect beamforming gain by intelligent reflecting surface (IRS), channel state information (CSI) on IRS is essential but challenging to obtain in IRS-aided systems due to the lack of signal transmitting/receiving/processing capabilities of IRS passive elements as well as the massive channel coefficients to be estimated. In this paper, we propose a new channel estimation scheme using the full-duplex technique for estimating the CSI of all users’ cascaded channels in an IRS-aided multiuser communication system. Specifically, one arbitrarily selected reference user is equipped with the full-duplex technique in the channel estimation phase to estimate the element-wise squared channel between it and the IRS via its pilot transmission together with the IRS’s time-varying reflection. Meanwhile, the base station (BS) and non-reference users estimate the CSI of their respective IRS-reflected channels, based on which the users-IRS-BS cascaded channels of the non-reference users are recovered by utilizing the fact that the IRS-BS channel is common to all users. As such, the proposed channel estimation scheme significantly reduces the required minimum training overhead for estimating all users-IRS-BS cascaded channels, which is determined by the number of IRS reflecting elements only, and is irrelevant to that of IRS-served users. Numerical results demonstrate the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) are widely applied for service provisioning in many domains, such as topographic mapping and traffic monitoring. These applications are complicated with huge computational resources and extremely low-latency requirements. However, the moderate computational capability and limited energy restrict the local data processing for the UAV. Fortunately, this impediment may be mitigated by utilizing wireless power transfer (WPT) and employing the multiaccess edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV via wireless communications. Particularly, the offloaded information may become compromising by the eavesdropper (Eve) when UAVs offload the computational tasks to MEC servers. To address this issue, a UAV-MEC (UMEC) system with energy harvesting (EH) is studied, where the full-duplex protocol is considered to realize simultaneously receiving confidential data from the UAV and broadcasting the control instructions. It is worth noting that in our proposed scheme, these control instructions also serve as the artificial interference to confuse the Eve. To improve the energy efficiency for offloading, the computational communication resource allocation is optimized to minimize the energy consumption for UAV with the consumed and harvested energy. Specially, the worst case secrecy offloading rate and computation-latency constraint are considered, to further enhance the reliability and security of the proposed system. Since the objective optimization problem is nonconvex, we convert it into a convex one by analytical means. The semiclosed form expressions of the offloading time, offloading data size, and transmit power are, respectively, derived. Moreover, the conditions of nonoffloading, partial, and full offloading are also discussed from a physical perspective. With the specific conditions of activating the above-mentioned three offloading options, numerical results verify the performance of our proposed offloading strategy in various scenarios and show the superiority of our offloading strategy with the existing works in terms of the offloading capacity and energy efficiency.

In this paper, we propose a two-layer heterogeneous network to realize the remote monitoring and advanced warning for infectious diseases spread by airborne pathogens, whose detection can be considered as a binary detection problem. To intuitively study the detection process, we abstract it as a molecular communication via diffusion (MCvD) model and uncover that one of the key factors to impact the detection performance is inter-symbol interference (ISI), i.e., the viral aerosol from other biological entities. Therefore, overcoming ISI is imperative to ensure reliable detection. In the abstracted MCvD model, the detection performance can be described by the bit error rate (BER) performance. Following this assumption, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for both the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we design a modified-SINAR (mSINAR) to measure BER performance for the MCvD system with a variable detection interval. Besides, we derive the optimal detection interval in closed-form. Using simulation results, we show that in terms of BER, our proposed mSINAR scheme is superior to the competitive schemes, and performs similarly to the scheme with optimal intervals determined by the exhaustive search.

To cope with the explosive traffic growth expected in next-generation wireless networks, it is necessary to design next-generation multiple access techniques that can provide higher spectral efficiency as well as larger-scale connectivity. As a promising candidate, power-domain non-orthogonal multiple access (NOMA) has been widely studied. In conventional power-domain NOMA, multiple users are multiplexed in the same time and frequency band with differentpresetpower levels, which, however, may limit the spectral efficiency under practical finite alphabet inputs. Inspired by the concept of spatial modulation, we propose to solve this problem by encoding extra information bits into the power levels, and exploiting different signal constellations to help the receiver distinguish between them. To convey this idea, termed power selection (PS)-NOMA, clearly, we consider a simple downlink two-user NOMA system with finite input constellations. Assuming maximum-likelihood detection, we derive closed-form approximate bit error rate (BER) expressions for both users. Moreover, the two-user achievable rate region is also characterized. Simulation results verify the analysis and show that the proposed PS-NOMA can outperform conventional NOMA in terms of BER and achievable rate.

In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performances of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC to assist edge vehicles as full-duplex relays. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of the secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion for edge vehicles. Then, we design a power allocation and task scheduling algorithm based on the genetic algorithm to solve the mixed-integer non-linear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay.

To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, existing research studies have only considered the applications of either of these approaches, which neglect their necessary interactions in real-world AV motion planning systems. To address this problem, we design an AV motion planning strategy based on motion prediction and V2V communication. Specifically, we propose the perception system and V2V communication module to provide real-time traffic and vehicular information to the participated AVs. Then, we formulate the AV lane-change motion planning problem through the scope of model predictive control based problem, as well as proposing the method on learning optimal motion planning by means of a novel deep learning technique. We conduct extensive case studies to evaluate the performance of the proposed system model. Our experimental results demonstrate the effectiveness of the proposed system model under various traffic conditions. In addition, the robustness of the perception system is guaranteed by utilizing the Car Learning to Act (CARLA) system with available V2V communication.

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism, which does not require any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which may result in some power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for better utilizing the reflection power in RIS-aided downlink multiple-input single-output (MISO) wireless systems. To this end, RIS-QRM partitions the RIS elements into two subsets in order to reflected the incident signals towards two orthogonal directions by using passive beamforming and encodes its local data onto the two partitions. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming maximum-likelihood detection at the receiver. Moreover, a low-complexity detection method that decouples the joint search of the constellation symbol and the RIS element partition is proposed. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM improves the error performance of the additional bits delivered by the RIS without deteriorating the error performance of the bits modulated on the constellation symbol, as compared to ON/OFF-based RIS-aided schemes.

In this paper, we investigate a multi-vehicle multi-task non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) system with passive eavesdropping vehicles. To enhance the performance of edge vehicles, we propose a vehicle grouping and pairing method (GPM) to employ the vehicle near to MEC acting as a full-duplex (FD) relay to assist edge vehicles. In order to improve the transmission security, artificial noise (AN) is used to interfere with eavesdropping vehicles. The approximate expression of secrecy outage probability of the system (SOPS) is derived in closed form. This paper aims to minimize the total delay of task completion of edge vehicles by jointly optimizing vehicle task division, power allocation and transmit beamforming. We design a power allocation and task scheduling algorithm relying on genetic algorithm (GA-PATS) to solve our formulated mixed-integer non-linear programming (MINP) problem. Numerical results demonstrate the superiority of our proposed scheme in the aspect of system security and transmission delay.

The emergence of 5G brings unprecedented possibilities for deploying the anticipated Industrial Internet of Things (IIoT). To achieve high density connectivity with multiple services in 5G empowered IIoT, we consider the non-orthogonal network slicing in this work. In particular, we jointly utilize network slicing to incorporate two different types of services and exploit non-orthogonal multiple access (NOMA) to maximize the number of total devices that can be accessed to the system. We formulate the connectivity maximization problem as a mixed-integer nonlinear programming (MINLP) by jointly optimizing the transmit power and device-subcarrier association. To tackle the intractable MINLP, we first transform it into a mixed-integer linear programming (MILP) and then reduce the MILP by devising a simple but effective transmit power allocation scheme. Thereafter, we propose a low-complexity best-effort pairing (BEP) algorithm to solve the reduced MILP. By comprehensive simulations, we find that our proposed BEP significantly outperforms the benchmark schemes.

Molecular communication (MC) is an emerging communication paradigm, where the information is carried via the patterns of molecules that are mainly governed by the diffusion process. Current MC literature concentrates on the time-domain analysis, while the signal analysis in other domains may facilitate the MC research. To this end, this paper performs the frequency-domain analysis by deriving the frequency response of the diffusion-based MC channels, manifesting an explicitly low-compass characteristic. The energy of the channel impulse response in the diffusion-based MC is also derived, and the corresponding bandwidth definition is proposed, which determines the sampling frequency for the one-shot diffusive channel impulse response in MC. The results in this work lay the foundation for the frequency-domain signal processing in diffusion-based MC channels.

Wireless communications empowered by Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable research attention due to the increased system design flexibility offered by RISs for diverse functionalities. In this paper, we consider a Multiple Input Multiple Output (MIMO) physical layer security system with multiple data streams including one legitimate and one eavesdropping passive RISs, with the former being transparent to the eavesdropper and the latter’s presence being unknown at the legitimate link. We first focus on the eavesdropping subsystem and present a joint design framework for the eavesdropper’s combining matrix and the reflection coefficients of the eavesdropping RIS. Then, focusing on the secrecy rate maximization, we propose a physical layer security scheme that jointly designs the legitimate precoding matrix and the Artificial Noise (AN) covariance matrix, as well as the legitimate combining matrix and the reflection coefficients of the legitimate RIS. Our simulation results reveal that, in the absence of a legitimate RIS, transceiver spatial filtering and AN are incapable of offering nonzero secrecy rates, even for eavesdropping RISs with small numbers of elements. However, when an L-element legitimate RIS is deployed, confidential communication can be safeguarded against cases with even more than a 5L-element eavesdropping RIS.

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it provides rich computation resources to train AI models, as well as low-latency access to the data generated by mobile and Internet of Things devices. In this paper, we present an infrastructure to perform machine learning tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criteria are to maximize the throughput, we aim at optimizing the learning performance. Specifically, we minimize the maximum learning error of all users by jointly optimizing the beamforming vectors of the base station and the phase-shift matrix of the RIS. An alternating optimization-based framework is proposed to optimize the two terms iteratively, where closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching framework to effectively solve the nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks.

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.

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of the eavesdropper and the cascaded channel brought by the IRS, the eavesdropper’s channel state information is imperfectly obtained at the base station. Under the channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper formulates a secrecy rate maximization problem while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling variables, the secrecy rate maximization problem can be solved via alternatively executing difference-of-convex programming and semidefinite relaxation method. The simulation results validate the strength of this newly established transmission scheme when compared to baseline schemes of random phase-shift, fixed phase-shift, and IRS ignoring CSI uncertainty.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism that conveys information by reconfiguring incident electromagnetic waves without using radio frequency chains. Existing reflection modulation schemes propose to convey additional information by manipulating the ON/OFF states of reflecting elements, which underuse the potential of the RIS. In this paper, we first propose a new reflection modulation scheme, called RIS-based quadrature reflection modulation (RIS-QRM), to achieve joint passive beamforming and information transfer for RIS-aided single-input single-output wireless communications by assuming that the direct link between the transceivers is blocked. We then extend the RIS-QRM scheme to the multiple-input single-output setting with the direct link between transceivers being taken into account. In this case, an optimization problem is formulated to jointly optimize the active beamforming and the phase shifts of the RIS in order to improve the received signal power at the user. Computer simulation results corroborate the effectiveness of the RIS-QRM scheme. It is shown that in contrast to ON/OFF-based schemes, the RIS-QRM scheme is able to improve the error performance of the additional bits delivered by the RIS without deteriorating that of the bits carried by the constellation symbols.

Due to the serious frequency selective fading and time selective fading in underwater acoustic (UWA) channel, the design of receiver becomes more difficult. We present a convolutional neural network (CNN) based orthogonal frequency division multiplexing (OFDM) receiver that fully considers the banded channel matrix features of doubly-selective channels in the UWA communication to achieve the integrated design of channel estimation and equalization. We propose a novel architecture using decoder and encoder convolutional neural network, named DECCN, which uses twenty-one convolution layers to compose an encoder and a decoder based on the dilation convolution and the feature reuse. DECCN focuses on reducing the complexity without considering the full connection (FC) layer. DECCN performs well for various length signals without changing the structure of system, such as 1024 bits and 2048 bits. Simulation results show that the proposed scheme reduces the bit error rate compared with the traditional algorithms and other DL-based schemes, especially with the pilot only 1/8 length of the signal or without cyclic prefix.

In molecular communication via diffusion (MCvD), information is conveyed by diffusing molecules. MCvD enjoys high energy efficiency but suffers from low date rates due to the long tail of the channel impulse response. To this end, the multiple-input multiple-output (MIMO) technique has been introduced to MCvD. However, the inter-symbol interference (ISI) and inter-link interference (ILI) deteriorate the bit error rate (BER) performance of MIMO MCvD systems. In this paper, a novel molecular modulation scheme, called molecular type permutation shift keying in the spatio-temporal domain, is proposed for MIMO MCvD systems to improve the BER performance by combating ILI and ISI. A low-complexity detector without requiring channel impulse response information is proposed. Furthermore, a complementary coding scheme that can effectively reduce ILI is designed. Additionally, the BER upper bound is analyzed. Numerical simulations on BER corroborate the analysis and show that the proposed scheme is a promising multi-molecule modulation alternative, which outperforms the existing MIMO MCvD modulation schemes.

Abstract Here, an enhanced spatial modulation with generalized antenna selection (ESM‐GAS) for multiple‐input single‐output systems is proposed. Unlike the conventional SM systems, in ESM‐GAS, the number of active antennas is not fixed, but varies from only one to all. In ESM‐GAS, a concept called equivalent antennas is introduced to describe the status of the antennas. Out of all the available equivalent antennas, a subset is selected as the candidate antennas for SM, in which additional information bits are used to determine the indices of the activated equivalent antennas. Two criteria are used to select the candidate equivalent antennas. One is ESM‐GAS, where a part of candidate equivalent antennas with larger channel power gains are selected with low computational complexity. To further improve the bit error ratio (BER) performance of ESM‐GAS, the other one is a combination scheme of Euclidean distance antenna selection (EDAS) and ESM‐GAS (ESM‐GAS‐EDAS). Simulation results show that ESM‐GAS has better BER performance than conventional SM, and ESM‐GAS‐EDAS outperforms conventional SM, EDAS‐based SM, and ESM‐GAS, which verifies the effectiveness of the proposed schemes.

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

The massive connectivity and information exchange in the Internet of Things (IoT) pose great challenges to the bandwidth efficiency. To overcome this issue, this article investigates a cooperative full-duplex device-to-device system with nonorthogonal multiple access (NOMA) and partial decode-and-forward (P-DF). In the proposed system, the base station simultaneously transmits signals to all receiving nodes. Two kinds of scenes are considered depending on the locations of the users, where the cases with and without an eavesdropper are respectively studied. According to the P-DF protocol, three kinds of relay forwarding strategies are studied, where only the correctly decoded symbol will be forwarded to the user equipments to avoid performance loss. For the full-duplex relay, it is allowed to forward its own signal by superposing the partial correctly decoded symbol from the previous phase to other users, to further improve the spectral efficiency. Closed-form expressions of the ergodic sum rate, outage probability and secrecy rate are derived. Simulation results verify the analytical results especially in the high signal-to-noise ratio region, and show the superiority of our proposed P-DF-NOMA scheme in terms of outage probability performance.

Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Things (IoT). However, due to the limited transmit power at IoT devices, collecting the sensing data in EL systems is a challenging task. To address this challenge, this article proposes to integrate unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs? This article further proposes a graph-based path planning model, a network energy consumption model, and a sample size planning model that characterizes F-measure as a function of the minority class sample size. With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. To this end, it is proved that each IoT device should be served only once along the path, thus the problem dimension is significantly reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)-based algorithm is derived, which converges in expectation to the optimal solution to the JPESP problem. Simulation results under different task scenarios show that our optimization schemes outperform the fixed EL and the full path EL schemes.

As a typical usage scenario for the next-generation mobile communication network, massive Internet of Things (mIoT) is requested to provide high machine-type communication device (MTCD) density service. Nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) can further enhance the performance of mIoT. Furthermore, to cope with the energy consumption constraint of MTCD, energy harvesting (EH) can be leveraged. In this article, considering the social trusts of MTCDs, we propose an MEC offloading scheme for cellular Internet of Things networks with massive NOMA-aided EH MTCD and several road side units with edge servers randomly distributed in a macrocell. We aim to maximize the total sum rate of the network by jointly considering the processing mode selection, device clustering, subchannel allocation, and power allocation while satisfying the power, energy, latency, and quality of service requirements. To this end, we prove the NP-hardness of the considered optimization problem and decompose it into three subproblems, which can be solved by an iterative algorithm. Numerical results demonstrate the superior performance of the proposed scheme.

Molecular communication (MC) is a bio-inspired communication paradigm, which lays the foundation for the Internet of Bio-NanoThings (IoBNT) in the medical field. As a high energy-efficient information transfer method, MC via diffusion (MCvD) is envisioned as a promising candidate for IoBNT but suffers from low date rates due to the long tail of the channel impulse response (CIR). To this end, the multiple-input–multiple-output (MIMO) technique has been introduced to MCvD. However, the intersymbol interference (ISI) and interlink interference (ILI) deteriorate the bit error rate (BER) performance of MIMO MCvD systems. In this article, molecular type permutation shift keying in the space domain (MTPSK-SD) and time-interleaved MTPSK-SD are proposed for MIMO MCvD systems to improve the BER performance by reducing ILI. The principle of MTPSK-SD is further generalized to the spatiotemporal domain, yielding three spatiotemporal modulation schemes, which can provide desirable BER performance without requiring any CIR information in the communication scenarios affected by different levels of ISI and ILI. Two low-complexity detectors are proposed to obtain different tradeoffs between anti-ILI and anti-ISI performance. Furthermore, a complementary coding scheme, which can effectively reduce the ILI under the considered symmetrical system topology, is designed and applied to all the proposed modulation schemes. Additionally, the BER upper bound is analyzed. Numerical simulations on BER corroborate the analysis and show that the proposed schemes are promising multimolecule modulation alternatives, which outperform the existing MIMO MCvD modulation schemes.

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the$(M,M,N,N)$multiple-input–multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled Vehicles-to-Vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for$M \le N/2$,$N=M$, and$2N \le M$antenna configurations, and reveal that for a fixed$N$, the optimal$M$to maximize the sum-SDoF is not less than$2N$. Through simulations, we examine the secure sum-rate performance of proposed transmission schemes and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter (CSIT).

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

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.

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

This paper considers a cooperative device-to-device (D2D) system with non-orthogonal multiple access (NOMA). We assume that the base station (BS) can simultaneously communicate with all users to satisfy the full information transmission requirement. In order to characterize the impact of the weak channel and different decoding schemes, two novel decoding strategies are introduced: single signal decoding scheme and maximum ratio combining (MRC) decoding scheme, respectively. With the single signal decoding scheme, the users decode the received signals immediately after the receptions from the BS. On the other hand, the MRC decoding scheme jointly decodes the received signals via MRC until the corresponding phase comes and the users jointly decode the received signals by employing MRC. Considering Rayleigh fading channels, the ergodic sum-rate (SR), outage probability and outage capacity of the proposed D2D-NOMA system are analyzed. Moreover, approximate expressions for the ergodic SR are also provided with a negligible performance loss. Numerical results demonstrate that the ergodic SR and outage probability of the proposed D2D-NOMA scheme overwhelm that of the conventional NOMA schemes. Furthermore, it is also revealed that the system performance including the ergodic SR and outage probability are limited by the weak channel for both the single signal decoding scheme and conventional NOMA schemes, but not for the MRC decoding scheme.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

Edge caching can effectively reduce backhaul burden at core network and increase quality-of-service at wireless edge nodes. However, the beneficial role of edge caching cannot be fully realized when the offloading link is in deep fade. Fortunately, the impairments induced by wireless propagation environments could be renovated by a reconfigurable intelligent surface (RIS). In this paper, a new RIS-aided edge caching system is proposed, where a network cost minimization problem is formulated to optimize content placement at cache units, active beamforming at base station and passive phase shifting at RIS. After decoupling the content placement subproblem with the hybrid beamforming design, we propose an alternating optimization algorithm to tackle the active beamforming and passive phase shifting. For active beamforming, we transfer the problem into a semidefinite programming (SDP) and prove that the optimal solution of SDP is always rank-one. For passive phase shifting, we introduce the block coordinate descent method to alternately optimize the auxiliary variables and the RIS phase shifts. Further, a conjugate gradient algorithm based on manifold optimization is proposed to deal with the non-convex unit-modulus constraints. Numerical results show that our RIS-aided edge caching design can effectively decrease the network cost by improving the quality of offloading links.

Recent research on reconfigurable intelligent surfaces (RISs) suggests that RISs can perform passive beamforming and information transfer (PBIT) simultaneously via smart reflections. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation (RPM) to achieve PBIT, where the joint active and passive beamforming is carefully designed by taking into account the communication outage probability. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS under the assumption that the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. Moreover, a closed-form expression for the asymptotic outage probability of the proposed scheme in Rician fading is derived. The achievable rate of the proposed scheme is also investigated under the assumption that the transmitted symbols are drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate. It is shown that the proposed scheme outperforms, in terms of achievable rate, the conventional RIS-assisted system without information transfer.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has recently received increased attention, due to the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). In this paper, we investigate the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems, where a hybrid analogy-digital (HAD) beamforming architecture is employed. Taking advantage of the Pareto-optimal beam design, we propose a feasible solution to approximately achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of the multi-objective optimization problem (MOP) can be solved efficiently. Correspondingly, the MOP of SE-EE trade-off can be converted into a feasible solution for energy-efficient resource usage, by finding the Pareto-optimal set (POS) towards the Pareto front. This combinatorial-oriented resource allocation approach on the SE-EE relation considers the optimal beam design and power control strategies for downlink multi-user mmWave transmission. To ease the system performance evaluation, we adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations demonstrate that the proposed optimization approach is vastly superior to existing algorithms.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). This paper investigates the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems. Taking advantage of the properties of Pareto optimality, we propose a feasible solution to achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of multi-objective optimization problem (MOP) can be solved efficiently. The MOP of SE-EE trade-off can then be converted into a Pareto-optimal set (POS) solution problem. This combinatorial-oriented resource allocation approach on SE-EE relation considers the optimal beam design and power reallocation for downlink multi-user mmWave transmission. We adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations reveal that the proposed optimization approach is vastly superior to existing algorithms.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.

Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beam-forming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's information is statistically known by the AP. Since the formulated problem is non-convex and thus difficult to solve optimally, we propose an efficient algorithm based on the alternating optimization technique to find a high-quality solution. Simulation results validate the effectiveness of the proposed reflection pattern modulation and beamforming design. It is shown that the proposed scheme outperforms the conventional RIS-assisted system with full RIS reflection in terms of achievable rate performance.

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a promising IM technique for OFDM systems to improve the spectral efficiency. In this paper, a secure index and data symbol modulation (DSM) scheme based on MM-OFDM-IM systems is proposed by employing the notion of the channel reciprocity assuming time division duplexing mode. The channel state information of the legitimate link is adopted as a secret key for protecting data transmission. We investigate randomized mapping rules for both IM and DSM, based on which even though an eavesdropper has estimated the permutations of modes and their carrying symbols, he cannot decode the message correctly. Particularly, the randomized mapping rules for IM are designed by exploiting the redundancy of mode permutations. Both numerical simulations and theoretical analysis for achievable secure rate and bit error rate are processed, whose results demonstrate that the proposed scheme can enhance the physical-layer security of the considered communication system.

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.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle's signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance.

A joint data and interference transmission scheme based on a new distributed asynchronous cyclic delay diversity (dACDD) technique is proposed for cooperative communication systems. Without any perfect channel state information from a legitimate user (LU) and an eavesdropping user (EU), joint remote radio head (RRH) selection for the data and jamming signal transmissions is proposed for dACDD to achieve the maximum diversity gain at the LU, while degrading the receive signal-to-interference-plus-noise ratio at the EU. The proposed dACDD is the extension of distributed cyclic delay diversity, which requires a tight synchronization among the central control unit and RRHs. Thus, processing at each RRH causing no intersymbol interference at the LU is developed. Then, the selection scheme for a data RRH is proposed, which selects a single RRH connected with the channel having the greatest channel magnitude as the data RRH to transmit a desired confidential message and controls the remaining RRHs to transmit an artificial interference sequence to the LU and EU. For the proposed distributed system, the marginal secrecy outage probability and marginal probability of non-zero achievable secrecy rate are analyzed by deriving closed-form expressions, whose correctness is verified via link-level simulations over non-identically distributed frequency selective fading channels.

In this article, we propose a downlink non-orthogonal multiple access (NOMA) based coordinated direct and relay system with one cell-center user and multiple cell-edge users, where a decode-and-forward (DF) relay bridges the connection between the base station and the cell-edge users. Both full-duplex (FD) and half-duplex (HD) protocols are considered for the relay. We assume that the performance of the cell-edge users is subjected to the relay, and the cancellation of the mutual interference between the relay and cell-center user is imperfect. Both the exact analytical expression of outage probability and an approximate expression of the ergodic sum rate at high signal-to-noise ratio (SNR) are derived. Numerical results demonstrate that: 1) the FD relaying NOMA system outperforms the HD relaying NOMA system at low SNR, but the situation is exactly the opposite at high SNR; 2) the mutual interference can cause a larger performance gap than the self-interference at the relay; 3) the power allocation coefficients for the cell-center user and relay can affect the performance more significantly than those for cell-edge users.11This article was presented in part at the IEEE International Workshop on Signal Processing Advances in Wireless Communications 2019 [1].

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

In this paper, a novel opportunistic spectrum sharing scheme, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM), is proposed for cognitive radio (CR) networks. In the considered OFDM-IM based CR (OFDM-IM-CR) model, the primary transmitter (PT) communicates with the primary receiver with the aid of an amplify-and-forward (AF) relay by transmitting OFDM-IM signals. Meanwhile, the secondary transmitter (ST) passively senses the spectrum and transmits its own information over those inactive subcarriers of the primary network to the secondary receiver if the signal-to-noise ratio of the PT$\to $ST link is above a predefined threshold; otherwise, the ST stays in silent mode. Two different types of maximum-likelihood (ML) detectors are designed for the primary network, based on the knowledge of either the estimated channel state information or the statistical channel information of the secondary network. A complexity-reducing method, which is applicable to both types and achieves near optimal performance, is further proposed. To evaluate the performance, a tight upper bound on the bit error rate (BER) is derived, assuming the first type of ML detection. Simulation results corroborate the analysis and show that OFDM-IM-CR has the potential of outperforming OFDM-CR and OFDM-IM-AF in terms of BER with higher spectral efficiency.

In this paper, a novel dual-hop spatial modulation (SM) relay network is proposed, in which the relay is enabled to transmit its own information while forwarding the SM signal from the source over the same frequency band. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are studied. For DF relaying, the relay embeds its own information into one out of two spatial dimensions of quadrature SM. For AF relaying, the relay activates only one transmit antenna to forward the received signal, and encodes its own information by the index of the active antenna. Under both relaying protocols, the throughput of the network is increased without consuming extra power. The bit error rate (BER) performance is analyzed, and tight upper bounds on the BERs are derived in closed form for the source and relay over Rayleigh fading channels for both DF and AF relaying. The performance analysis is verified by Monte Carlo simulations, showing that the proposed scheme provides a simple yet effective solution to the implementation of SM relay networks in which the relay has its own information to be transmitted.

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.

In this paper, we present the space shift keying based molecular communication (SSK-MC), where the space symbol is of interest. For micro-scale SSK-MC, the information is embedded into the index of a single activated transmitter nanomachine, while the index of the sensor is considered for the macro-scale SSK-MC. The cancellation of the strongest inter-link interference in multiple-input multiple-output (MIMO) based MC is available with the implementation of SSK-MC, in which only one transmitter is activated during each symbol duration. We derive the symbol error rate (SER) of SSK-MC, which shows a perfect match with its numerical simulations. Apart from the theoretical analysis, we further design an underwater prototype under the 4×4 MIMO-MC configuration for SSK-MC, demonstrating its SER performance with different detection algorithms.

A joint data and jamming transmission scheme based on a new distributed asynchronous cyclic delay diversity (dACDD) scheme is proposed for cooperative communication systems. Without any exact knowledge of channel state information (CSI) at the transmitting side, a joint remote radio head (RRH) selection scheme for the data and jamming signal transmissions is proposed for dACDD to achieve the maximum diversity gain at a legitimate user (LU), while degrading the receive signal-to-interference-plus-noise ratio at an eavesdropping user (EU). A single RRH connected with the channel having the greatest channel gain is selected as a data RRH that transmits a desired confidential signal, whereas the remaining RRHs are controlled by the central control unit to transmit an artificial noise sequence (ANS) to the LU and EU. Without assuming exact knowledge of CSI of the whole system, the secrecy outage probability of the distributed communication system is analyzed by deriving a closed-form expression, and through link-level simulations over non-identically distributed frequency selective fading channels over the entire system.

In this paper, we propose a coordinated direct and relay transmission scheme based on non-orthogonal multiple access (NOMA), where the relay and cell- center user operate in full-duplex mode to help enhance the quality of service of the cell-edge user as well as ensure the secure transmission. In the proposed system, the base station directly serves the cell-center user and relay while communicating with the cell-edge user through the relay and cell-center user. Taking advantage of NOMA, the cell-center user and relay are able to cancel the mutual interference between themselves. Moreover, they can also cooperatively apply the artificial noise scheme to interfere any potential eavesdropper without impairing the legitimate cell-edge user. Exact and closed-form expressions for the outage probability, achievable rate, and achievable secure rate are derived. Numerical results prove that the proposed system outperforms the existing counterpart in the low signal-to-noise ratio region, and ensures secure communications with appropriate power allocation.

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resource-constraint nature of IoT devices.

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.

This IEEE Journal on Selected Areas in Communications (JSAC) special issue (SI) aims to provide a comprehensive overview of the state-of-the-art advances and a view of emerging research challenges and opportunities forSpatial Modulation in Emerging Wireless Systems. This SI solicits high-quality original research papers regarding theoretical studies, and application-oriented contributions dealing with architectures, platforms, and multiple access schemes.

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

In this paper, a distributed cognitive underlay single carrier system is investigated. A secondary users' network consists of a control unit (CU) and a group of secondary user remote radio heads (S-RRHs). To effectively access the radio spectrum licensed to the primary users, a distributed cyclic delay diversity (dCDD) scheme is employed between the CU and S-RRHs as the transmit diversity scheme. Multiple primary user transmitters (PTXs) are assumed to be located isotropically within the secondary users' network, so that a mixture of line-of-sight (LoS) and non-line-of-sight (nLoS) paths from the PTXs to the secondary user receiver is considered. In addition, a mixture of LoS and nLoS paths is considered in the secondary users' network. For a new transmit diversity scheme and channel model, the performance of the secondary users' network achieved by the dCDD in the presence of isotropically distributed multiple PTXs is investigated. The dCDD enables the CU to use multiple S-RRHs at the same time, so that determining the effects of a different number of S-RRHs in the presence of a new channel model is an open research issue. To this end, a new closed-form expression for the outage probability is derived, and then its accuracy is verified by link-level simulations.

There is a recent surge of research interest in the study of performance-enhancing techniques for orthogonal frequency division multiplexing (OFDM)-based relay systems. Among those, subcarrier mapping has been verified to be an effective one for boosting the system capacity and improving the error performance. However, it has to be performed at the relay, which subsequently conveys the subcarrier permutation information to the destination. The existing signaling scheme occupies a portion of subcarriers to this end, leading to a loss of spectral efficiency. In this paper, we propose a novel signaling scheme to eliminate this overhead by transferring the subcarrier permutation to the mode permutation that can be implicitly conveyed without consuming additional spectrum resources. We adopt phase rotation for mode design considering both non-adaptive and adaptive modulation, and illustrate the proposed scheme by taking the dual-hop OFDM relaying with semi-blind amplify-and-forward protocol as an example. An asymptotically tight upper bound on the bit error rate (BER) of the proposed scheme is derived in closed-form over Rayleigh fading channels. BER simulation results validate the analysis and show that the proposed scheme asymptotically approaches the ideal case that assumes perfect knowledge of subcarrier permutation information at the destination and significantly outperforms the existing scheme in the asymptotic signal-to-noise ratio region at the same spectral efficiency.

In this paper, a new channel model is presented for molecular communications (MC), where a point source emitted by the transmitter undergoes three phases, with effect of convection dominating in the first two phases, whereas diffusion prevailing in the final phase. The point source obtains its initial velocity and passes through the nozzle of the nanomachine transmitter in the first phase, followed by a deceleration process in the second phase. The free diffusion model is considered in the third phase. Based on this channel model, the energy transfer issue for two-way MC system is also taken into account, in which one of the transceivers is assumed to have abundant information molecules from its ambient environment, whereas the other one obtains the information molecules by implementing the simultaneous molecular information and energy transfer (SMIET). Finally, analytical bit error rate (BER) expressions are validated by computer simulations. Our results suggest that the symbol duration and the SMIET order significantly influence the BER performance in our two-way MC system.

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.

In this paper, we develop a secure non-orthogonal multiple access (NOMA)-based two-way relay network, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of an eavesdropper. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to confuse any potential eavesdropper. Specifically, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. Different decoding schemes based on the successive interference cancellation (SIC) are proposed for the legitimate users, relay, and eavesdropper. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. We propose different decoding schemes based on the successive interference cancellation for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multiple-eavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

As an emerging multi-carrier transmission scheme, multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) conveys information through multiple distinguishable constellations (or modes, alternatively) and their full permutations, increasing the spectral efficiency of OFDM-IM and classical OFDM. However, both MM-OFDM-IM and its extension, that is, MM-OFDM with in-phase/quadrature index modulation (MM-OFDM-IM-IQ), cannot provide any transmit diversity gain, which may be critical for ultra-reliable communications. In this paper, aiming at enhancing the diversity gain of MM-OFDM-IM(-IQ) schemes, coordinate interleaved (CI-)MM-OFDM-IM and linear constellation precoded (LCP-) MM-OFDM-IM-IQ are proposed, both of which achieve a diversity order of two without loss of spectral efficiency. The optimal rotation angle for CI-MM-OFDM-IM and the optimal precoding matrix for LCP-MM-OFDM-IM-IQ, in the sense of maximizing their coding gains, are derived in closed form. Computer simulations corroborate the advantages of the proposed schemes in terms of diversity and bit error rate performance toward next-generation wireless networks.

In this paper, we study the performance of a downlink non-orthogonal multiple access-based cooperative relay system with a single relay over Nakagami-m fading channels, where both decode-and-forward (DF) and amplify-and-forward (AF) protocols are considered. We assume that only statistical channel state information is available to the system and used to determine the decoding order of cell-edge users' data. For DF relaying, both ergodic sum rate and outage probability are solved in closed form. For AF relaying, closed-form asymptotic ergodic sum rate and outage probability are provided. Numerical results verify the accuracy of the analysis and demonstrate that the DF protocol significantly outperforms the AF one in terms of ergodic sum rate even the channel's near-far effect weakens. In addition, the DF protocol exhibits better outage performance than the AF one at low signal-to-noise ratio (SNR), though the superiority becomes negligible with the increasing SNR.

Full-duplex has emerged as a promising technology that enables a communication node to transmit and receive at the same time and same frequency band. One limitation of full-duplex is that the self-interference (SI) is very strong. In this paper, an effective SI cancellation scheme operated in the digital domain is proposed. It is facilitated by the property that the SI channel is reciprocal and the transmitted data is known by both the transmitter and the receiver. It converts the strong SI to the inter-symbol interference through a combination of the signals received in successive time slots. This conversion leads to a reduction in the number of independent signal flows but can be partially compensated for by transmitting more bits using spatial modulation and involving more time slots in the SI cancellation, which enhances the achievable rate. To achieve optimal performance, the transmitted symbols at one node need to be artificially rotated. We also derive a closed-form expression for an upper bound on the average bit error rate, and its error-free information transmission capability is investigated. Monte Carlo simulations over Rayleigh fading channels between two nodes are conducted and advantages are revealed.

In this paper, we propose an index modulated orthogonal frequency division multiplexing spread spectrum (IM-OFDM-SS) scheme, which combines the techniques of SS and IM under the framework of OFDM. In this scheme, the information bits are jointly conveyed by the indices of spreading codes and the conventional$M$-ary modulated symbols. A low-complexity maximal ratio combining (MRC) detector is designed, in which the receiver first detects the spreading codes and then de-spreads and demodulates the symbols. The bit error rate (BER) performance of IM-OFDM-SS systems in the presence of channel estimation errors is analyzed. An upper bound and approximate average bit error probability associated with maximum-likelihood and MRC detection, respectively, are derived. Subsequently, we extend the idea of IM-OFDM-SS to multi-code and multi-user scenarios, proposing generalized (G-)IM-OFDM-SS and IM-based multi-carrier code division multiple access (IM-MC-CDMA), respectively. Simulation results verify the analyses and show that the proposed IM/ GIM-OFDM-SS outperforms the existing OFDM-IM and OFDM-SS schemes significantly. In addition, the IM-MC-CDMA exhibits a lower BER than MC-CDMA at the same SEs for either multi-user or single-user detection.

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM), which transmits an OFDM signal with information bits embedded onto multiple distinguishable signal constellations of the same cardinality and their permutations, is a recently proposed IM technique in the frequency domain. It is capable of achieving higher spectral efficiency and better error performance than classical OFDM and existing frequency-domain IM schemes. In this paper, we propose the scheme of generalized (G-) MM-OFDM-IM, which allows a different subcarrier to utilize a signal constellation of a different size while conveying the same number of IM bits. Considering phase shift keying constellations, we present design guidelines for GMM-OFDM-IM to achieve an optimal error performance in the asymptotically high signal-to-noise ratio region. A computationally efficient and near-optimal detector based on the idea of sequential decoding is also tailored to GMM-OFDM-IM, which avoids the detection of an illegitimate constellation permutation. Monte Carlo simulations are conducted to validate the inherent properties and advantages of GMM-OFDM-IM.

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.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communication systems. However, due to the dependence of subcarrier symbols within each subblock and the strong interchannel interference, it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a low-complexity detector based on the sequential Monte Carlo (SMC) theory for the detection of MIMO-OFDM-IM signals. The proposed detector, which draws samples based on the importance weights at the subblock level, achieves near-optimal error performance with considerably reduced computational complexity. Simulation and numerical results in terms of bit error rate (BER) and number of complex multiplications (NCM) corroborate the superiority of the proposed detector.

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.

Due to the low speed of sound in water, the underwater acoustic (UWA) signal suffers obvious time scaling when relative motion exists between the transceivers. In general, given a single scale, the scaling-induced distortion can be rectified by a resampling operation. However, the scaling coefficients of the channel paths may be different from each other, especially in shallow water. In recent years, a multi-scale multi-lag (MSML) model has been used to describe the high speed mobile UWA channels in the literature. In this paper, we analyze the performance of OFDM system under this model. Considering the resalmper as a part of the channel, we get a an equivalent MSML channel. We first derive the sub-channel gains in closed form, and then get the analytical expression of the average channel gains and inter-carrier interference (ICI) under given statistics of the channel paths. Finally, we discuss the average achievable rate of the system and get a lower bound of that in closed form.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efciency. In this paper, we study a NOMA based dual-hop X-relay system, where a decode-and- forward relay assists the communication between the sources and the destinations. We assume that the statistical channel state information is known to the network. In such an assumption, the achievable sum-rate at high signal-to-noise ratio is further solved in closed form. Especially, the corresponding power allocation schemes, including the theoretically optimal and practical suboptimal ones, are proposed. Analyses and simulation results show that in the considered system, NOMA signicantly outperforms the conventional orthogonal multiple access schemes in terms of sum-rate performance, especially when channel's near-far effect is signicant.

Recently, a new multiple-input multiple-output (MIMO) transmission scheme termed as receive spatial modulation (RSM) is proposed, which activates a subset of receive antennas to carry additional information bits through the index domain. In this paper, a novel variant of RSM, which is referred to as enhanced RSM (ERSM), is proposed to further improve the system performance, especially in high-mobility scenarios. For ERSM, the log-likelihood ratio (LLR) and maximum likelihood (ML) detectors are developed, which exhibit very low implementation complexity. With the proposed ML detector, the ERSM is analyzed using different performance metrics, including the asymptotic average bit error probability (ABEP), the exact coding gain, and the achievable rate. Both the analysis and simulation results corroborate the superiority of the proposed scheme.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based multi-pair two-way relay (TWR) network, in which a rate splitting scheme and a successive group decoding strategy are employed. By exploiting the interference signals received from neighbor users with the leverage of the full-duplex technique, we enhance the decoding ability of each user and further achieve an effective multiuser interference management for the network. We propose different decoding strategies for different types of nodes by processing the received signals with only local incoming channel state information in different manners. Moreover, under the limited group decoding size, each individual node decodes its own desired messages along with a fraction of the interference successively. We further investigate the joint uplink and downlink fair rate allocation problem based on the max-min criterion, and the solution to which also contains the optimal group decoding schedule. Simulation results in terms of ergodic rate and outrage probability corroborate the superiority of our NOMA-based multi-pair TWR network over the OMA-based counterpart.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) performs transmission by considering two modes over OFDM subcarriers, which are the null and the conventional$M$-ary signal constellation. The spectral efficiency (SE) of the system, however, is limited, since the null mode itself does not carry any information and the number of subcarrier activation patterns increases combinatorially. In this paper, a novel IM scheme, called multiple-mode OFDM-IM (MM-OFDM-IM), is proposed for OFDM systems to improve the SE by conveying information through multiple distinguishable modes and their full permutations. A practical and efficient mode selection strategy, which is constrained on the phase shift keying/quadrature amplitude modulation constellations, is designed. Two efficient detectors that provide different tradeoffs between the error performance and detection complexity are also proposed. The principle of MM-OFDM-IM is further extended to the in-phase and quadrature components of OFDM signals, and the method of generating multiple modes from the$M$-ary pulse amplitude modulation constellation for this modified scheme is also introduced. Bit error rate (BER) analyses are provided for the proposed schemes. Monte Carlo simulations on BER corroborate the analyses and show that the proposed schemes appear as promising multi-carrier transmission alternatives by outperforming the existing OFDM-IM counterparts.

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system.

Index modulation concept has attracted considerable research interest in the past few years. As a realization of index modulation in the frequency domain, orthogonal frequency division multiplexing with index modulation (OFDM-IM) has recently been proposed, which conveys information bits through both the subcarrier activation patterns and the amplitude phase modulation constellation points. This paper proposes two enhanced OFDM-IM schemes aimed at achieving higher spectral efficiency and diversity gain, respectively. The first one, termed OFDM with hybrid in-phase/quadrature index modulation (OFDM-HIQ-IM), explores the I- and Q- dimensions jointly for index modulation, allowing transmission of more index modulation bits in each subcarrier group. The second one, termed linear constellation precoded OFDM-IQ-IM (LP-OFDM-IQ-IM), spreads information symbols across two adjacent active subcarriers through linear constellation precoding to harvest additional diversity gain. By maximizing the minimum squared Euclidean distance, two different realizations of LP-OFDM-IQ-IM are derived, which leads to a rotated and a diamond-shaped constellation, respectively. The proposed OFDM-HIQ-IM and LP-OFDM-IQ-IM, as revealed by both theoretical analyses and computer simulations, enable low-complexity detection and exhibit superior error rate performance over the existing OFDM-IM schemes.

Generalized spatial modulation (GSM) is a relatively new multi-input multi-output transmission technique that enables a flexible trade-off between the achievable transmission rate and the cost of radio frequency chains. However, due to the constraint of transmit antenna combination and the variation of interchannel interference, the efficient low-complexity demodulation of GSM signals is challenging, especially when soft demodulation is needed. In this paper, we propose two low-complexity algorithms based on the deterministic sequential Monte Carlo (SMC) technique for the demodulation of GSM. The type-I SMC demodulator, which uses the conventional successive interference cancellation as the kernel and draws antenna-wise samples from the extended constellation, is proposed for the overdetermined GSM system. The type-II SMC demodulator, which consists of two stages and uses the orthogonal matching pursuit as the kernel in the first stage, is proposed for the underdetermined GSM system. A key component in both algorithms is an efficient online scheme to eliminate the illegal samples during the sampling process. Both proposed algorithms achieve near-optimal performances with complexity linear in terms of the antenna size. Moreover, owing to their soft-input soft-output nature, they can be employed in a turbo receiver for a coded GSM system.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efficiency. NOMA differs from recently adopted orthogonal multiple access techniques in that it superimposes multiple data flows at the same time and same frequency band but with different power levels, and resorts to the successive interference cancellation (SIC) for data decoding. This paper studies NOMA in a dual-hop decode-and-forward (DF) relaying where two relays cooperate to deliver messages to the destination. Specifically, the achievable sum-rate is analyzed and a optimal power allocation (PA) scheme is designed for the proposed system. Performance comparison with the two conventional cooperative schemes that rely on the maximal ratio combining (MRC) and the max-min criterion, respectively, is also made. Comparison results reveal the advantages of the proposed scheme on not only the sum-rate, but also the energy efficiency.

Index modulated orthogonal frequency division multiplexing (IM-OFDM) is a newly proposed technique, which achieves significantly improved error performance and peak-to-average power ratio (PAPR) in comparison with classical OFDM due to the activation of partial subcarriers. However, in the presence of inter-carrier interference (ICI), error detection of the subcarrier indices may easily occur, such that the performance of IM-OFDM is severely degraded and can be even worse than classical OFDM. To solve this problem, in this paper, we propose to tailor the ideas of classical ICI self-cancellation and two-path cancellation to IM-OFDM with two mapping methods, i.e., symmetric mapping and mirror mapping. Thanks to the joint design of the grouping and mapping methods, the proposed schemes inherit the IM-OFDM virtue of partially activated subcarriers but with reduced ICI. Monte Carlo Simulations validate that the proposed schemes significantly outperform conventional OFDM with ICI cancellation in additive white Gaussian noise (AWGN) channels with frequency deviation without sacrifice of the spectral efficiency and increase of the computational complexity.

Orthogonal frequency division multiplexing (OFDM) is prone to frequency offset which gives rise to intercarrier interference (ICI). Recently, a novel OFDM transmission scheme called OFDM with index modulation (IMOFDM) is proposed, which outperforms conventional OFDM in the absence of frequency offset. As in IM-OFDM, partial subcarriers are set to be idle by spatial modulation, the potential of IM-OFDM in ICI reduction is expected. In this paper, we find that this potential vanishes in some real scenarios with ICI, which leads to IM-OFDM exhibiting even worse performance than conventional OFDM. To improve this situation, a novel ICI cancellation scheme is designed, which integrates the ICI self-cancellation technique into the IM-OFDM framework. Via careful designs, the proposed scheme not only provides a solution to ICI problems of IM- OFDM, but also achieves an attractive tradeoff between the spectral efficiency and ICI cancellation performance of the system. Simulations validate that in the presence of carrier frequency offset (CFO), the proposed scheme significantly outperforms existing IM-OFDM and more importantly shows better performance than the conventional OFDM with ICI self-cancellation.

Frequency domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency domain equalization systems as it achieves high spectral efficiency at the expense of tiny signal distortion. In FDPMT, pilot positions are dynamically selected to achieve a relatively low level of signal distortion, rendering pilot position detection (PPD) indispensable at the receiver. In this letter, by exploiting both the prior known pilots and the statistical information of the data tones, we propose a novel PPD scheme based on the maximum-likelihood criterion. Simulation results show that our proposed PPD scheme significantly improves the PPD accuracy and outperforms the existing counterparts in terms of bit error rate.

In this paper, we propose a novel virtual multi-input-multi-output (VMIMO) architecture to convey information from the source node (SN) to the destination node (DN) via multiple relay nodes (RNs). As it is built on dual-hop networks with differential spatial modulation (DSM), we call it dual-hop hybrid DSM (DH-HDSM). In order to harvest the diversity provided by the SN and reduce the complexity at the RNs, DH-HDSM forms a precoding aided DSM pattern in the first hop and applies incoherent detection in a centralized or distributed manner at the RNs. Based on the decode-and-forward protocol, DSM transmission is carried out in the second hop and achieves high energy efficiency via statistical single RN activation at any time instant. We analyze the performance of DH-HDSM in terms of the average bit error probability and make comparisons with dual-hop hybrid spatial modulation (DH-HSM) and differential VMIMO systems with either a single relay or best relay selection. Theoretical results and Monte Carlo simulations confirm that DH-HDSM outperforms DH-HSM and other differential relay schemes in terms of error performance when the DN is equipped with multiple antennas.

Motivated by the concept of spatial modulation (SM), a differential scheme has been recently proposed. This scheme, termed as differential (D-)SM, dispenses with channel estimation while maintains a similar bit error rate (BER) performance to SM. The conventional optimal DSM detector based on a maximum likelihood (ML) criterion, however, gives rise to prohibitive computational complexity when either the space-domain or signal-domain constellation size is large. In this paper, we propose a low-complexity yet optimal detection algorithm for DSM by utilizing sphere decoding (SD). The complexity analysis concerning Euclidian distance equation for DSM-SD is derived. Simulation results show that the proposed DSM-SD algorithm maintains an identical BER performance and achieves a significant reduction of computational complexity compared with the DSM-ML algorithm. The DSM- SD algorithm is especially efficient when the number of receive antennas is large. Moreover, compared with SD applied to SM, its application to DSM is verified to be more useful and attractive.

In this paper, we propose a novel Multiple Input Multiple Output (MIMO) scheme based on Spatial Modulation (SM) and termed as Pre-coding aided Differential Spatial Modulation (PDSM). By mapping information to the receiver side space-time transmit blocks and conducting differential modulation, the PDSM scheme achieves further reduced receiver side complexity in comparison with Pre-coding aided Spatial Modulation (PSM). A general upper bound on the Average Bit Error Probability (ABEP) achieved by the PDSM architecture with an arbitrary number of transmit antennas and two receive antennas is derived, and an exact closed-form ABEP expression is provided for BPSK signaling in Rayleigh fading environment. The PSM architecture with the same system configuration is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a less-than-3dB Signal-to- Noise power Ratio (SNR) penalty of the considered system in comparison with the benchmark.

Orthogonal frequency division multiplexing (OFDM) is promising for underwater acoustic (UWA) communications as it is robust against large delay spread. However, OFDM suffers from intercarrier interference (ICI) caused by the serious Doppler effect in UWA channels. Attentive to the property that the ICI is mainly contributed by the adjacent subcarriers in UWA channels, in this paper, we propose a novel low-complexity frequency-domain equalizer to combat the ICI for OFDM UWA communications. Specifically, the coefficients of the equalizer are obtained by using the nor- malized gradient algorithm which is based on the minimum symbol error rate (MSER) criterion. The proposed equalizer uses very few (typical 3) FIR taps to mitigate the ICI for each subchannel, yet achieves better SER performance than the conventional equalizer based on the minimum mean square error (MMSE) criterion.

An effective self-interference (SI) cancellation scheme operated in the digital domain is proposed for the newly-emerging spatial modulated full duplex (SMFD) system. In the proposal, the SMFD receiver performs the SI cancellation through a combination of signals received in successive time slots by resorting to the properties of the SMFD system, i.e., the SI channel is reciprocal and the transmitted data is known by both the transmitter and the receiver. To achieve the ultimate performance of the proposal, however, the transmitted symbols need to be rotated and the number of time slots involved in the SI cancellation has to be carefully chosen to balance the receiver complexity and the system performance. These issues are also discussed in great detail. Monte-Carlo simulations on the error-free information transmission capability of the SMFD system with the proposed SI cancellation scheme over Rayleigh fading channels are conducted and advantages are revealed.

Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexible tradeoff between the reception reliability and the system complexity. In this paper, we are the first to study the adoption of DSM in a dual-hop amplify-and-forward (AF) relaying system, which consists of a two-antenna source, a single-antenna relay, and a single-antenna destination, so as to reduce the burden of channel tracking on both the relay and the destination. We derive a general upper bound on the average bit error probability (ABEP) achieved by the system. Moreover, an exact closed-form ABEP expression and the asymptotic result are provided for BPSK signaling in Rayleigh fading environment. The same system setup with the adoption of SM at the source is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a 3dB signal-to-noise power ratio (SNR) penalty of the considered system compared with the benchmark.

In this paper, a novel self-interference (SI) cancellation scheme operated in the digital domain is proposed for the full duplex (FD) system configured with two antennas. The basic idea of our scheme is to transfer the strong SI to the inter-symbol interference (ISI), which is easier to cope with, through a combination of the signals received in successive time slots based on the assumptions that the SI channel is reciprocal and the previous transmitted data can be stored. However, this transfer may result in an un-removable error floor when the transmit antenna remains the same during the time slots in the SI cancellation. To solve this problem, we propose to apply differential spatial modulation, which forces two antennas to take turns to transmit signals in every two consecutive time slots. To further compensate for the loss of spatial degrees of freedom owing to the transfer, we propose to involve more time slots in the SI cancellation and allow the transmit antenna to be selected randomly by spatial modulation starting from the third time slot. Simulations on the bit error rate (BER) are conducted, whose results show that the performance of the proposed scheme is improved as more time slots are involved in the SI cancellation, and the proposed scheme with four time slots outperforms the conventional FD system with 3 dB interference-to-noise ratio (INR) for QPSK modulation.

Differential spatial modulation (DSM) is a newly-emerging differential scheme tailored to the spatial modulation technique, which selects only one among a group of antennas for transmission at any time instant. DSM, however, gives rise to prohibitive search complexity when the number of transmit antennas is large. In this letter, a low-complexity suboptimal detector is proposed for DSM. It is designed based on the maximum-likelihood criterion but takes more candidates for the antenna activation orders into account. The detection is performed in two steps: the first step is to confine the number of candidates for the modulated symbols to a small portion by exploiting the symmetry of the signal constellation; the second step is to select the most likely modulated symbols from the output of the first step according to the determined antenna activation order via a Viterbi-like algorithm. Analyses and simulations show that the proposed detector achieves near-optimal performance yet largely reduces the search complexity.

Nowadays, more and more communication systems are deployed under mobile environments with very high velocity. This paper focuses on the channel estimation problem in vehicular scenarios, which is very challenging in view of the extremely time-varying characteristics of mobile channels. Specifically, we propose a novel channel estimator named constructed data pilot (CDP) estimator for the current communication standards by fully exploiting the channel correlation characteristics across two concatenated symbols. On the basis of the CDP estimator, we further resort to two efficient techniques to improve its performance over the entire signal-to-noise-ratio (SNR) region. For the first technique, the time-variant mobile channel is modeled as a first-order Markov process so that the exact autocorrelation value of the two adjacent symbols can be derived. For the second technique, the SNR is estimated and serves as a priori information. Simulation results reveal that our proposed channel estimators outperform existing alternatives with lower computational complexity.

In this paper, we investigate the pilot position selection (PPS) problem in the frequency domain pilot multiplexing technique (FDPMT) for channel estimation of single-carrier block transmission with frequency domain equalization (SC-FDE). Unlike the widely accepted point of view that the conventional PPS technique is the optimal one, this paper for the first time questions this viewpoint and demonstrates the suboptimal property of the conventional PPS technique. To further improve the performance of the FDPMT, an optimal PPS technique is proposed based on the minimization of the average bit error rate (BER). Compared with the conventioanl PPS technique, the proposed PPS technique exhibits better performance with similar complexity.

In this paper, by considering the case of two transmit antennas, we propose a novel approach for introducing differential signaling into Spatial Modulation (SM)/Space-Shift Keying (SSK) and thus develop a differential SM/SSK (DSM/DSSK) scheme, where neither the transmitter nor the receiver has access to channel state information. The proposed approach can be applied to any equal energy signal constellations. Performance analysis of the proposed DSM is developed analytically and demonstrated via simulations, leading to some interesting observations and useful conclusions. It is shown that the performance degradation of DSM over SM can be less than 3dB and that the loss decreases as the number of receive antennas decreases.

Two-path transmission schemes have attracted much attention due to its effectiveness in suppressing intercarrier interference (ICI) in orthogonal frequency-division multiplexing (OFDM) systems. This paper proposes a general implementation framework for the existing two-path transmission schemes, which not only reveals the underlying relationship between the existing two-path transmission schemes and the mirror-subcarrier-mapping based ICI self-cancellation technique but also owns the advantage of backward compatibility with normal OFDM systems. Moreover, using the proposed framework, new two-path transmission schemes can be easily developed. With insights provided by this framework, the carrier-to-interference power ratio (CIR) and the bit error probability (BEP) achieved by the existing two-path transmission schemes are further analytically investigated. Monte Carlo simulations validate the analysis.

In this paper, we propose a new regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic scattering wideband multiple-input multiple-output vehicle-to-vehicle (V2V) Ricean fading channels. By correcting the unrealistic assumption widely used in current RS-GBSMs, the proposed model can more practically study the impact of the vehicular traffic density on channel statistics for different time delays. From the proposed model, we derive the Doppler power spectral density (PSD) and find that highly dynamic Doppler spectrum appears for V2V channels. Excellent agreement is achieved between the derived Doppler PSD and measured data, demonstrating the utility of the proposed model. To combat the intercarrier interference (ICI) caused by highly dynamic Doppler spectrum in real orthogonal frequency division multiplexing based V2V systems, this paper proposes a new type of ICI cancellation scheme, named as precoding based cancellation (PBC) scheme. The proposed scheme can be easily implemented into real V2V systems with the same ICI mitigation performance as the current best ICI cancellation scheme that has high complexity. To further improve the performance of the proposed PBC scheme, a new phase rotation aided (PRA) method, namely constant PRA (CPRA) method, is proposed. Compared with the existing PRA method, the CPRA method has better performance and much less implementation complexity. Therefore, the proposed PBC scheme with the CPRA method is the best ICI cancellation scheme for real V2V systems.

In this paper, we present an analytical study of the bit error rate (BER) for interleaved single-carrier frequency-division multiple access (SC-FDMA) systems over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels with fading parameters {m} being integers when minimum mean-square error frequency-domain equalization (MMSE-FDE) is applied. Under the assumption of independent fading characteristics among channel frequency responses (CFRs) at the allocated subcarriers for a specific user, accurate numerical BER computation for square M-ary quadrature amplitude modulation (M-QAM) is developed by exploiting the statistics of the equalized noise. More importantly, the BER derivation is based on the real distribution of the CFRs without applying the widely used approximation of the CFRs in previous literature, resulting in a more accurate BER analysis. Monte-Carlo simulations are conducted to validate the analysis.

Orthogonal Frequency Division Multiplexing (OFDM) systems may suffer from both carrier frequency offset (CFO) and in-phase/quadrature-phase (I/Q) imbalance at the receiver front end. In this paper, a novel type of intercarrier interference (ICI) self-cancellation method using mirror mapping is proposed to combat these impairment. Based on the proposed novel method and by adopting two widely used mapping operations, we altogether derive two different mirror mapping schemes. Analysis and simulation results verify that the proposed mirror mapping schemes not only inherit the efficiency of the existing self-cancellation schemes in suppressing ICI caused by CFO but also have the ability of compressing or even eliminating IQ imbalance.