Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is considered as a promising waveform for Internet of Things (IoT) due to its capability to mitigate interference among asynchronous users. However, as a multicarrier technique, the FBMC-OQAM signal also has a high peak-to-average power ratio (PAPR), which is detrimental to low-power transmission. To address this issue, this article proposes a novel discrete Fourier transform (DFT)-spread scheme to exhaustively reduce the PAPR of the FBMC-OQAM signal. In this scheme, the transmitting data symbols are first mapped with a conjugate symmetry rule and then coded by the DFT. Using this method, the preprocessing step in FBMC-OQAM that involves separating the real and imaginary parts of the quadrature amplitude modulation (QAM) symbols can be avoided. Compared with existing DFT-spread FBMC-OQAM schemes, the proposed scheme demonstrates better PAPR performance while maintaining equivalent spectral efficiency. Additionally, the computational complexity of the proposed scheme experiences only a slight increase, thereby preserving a relatively low computational burden. Numerical simulation results validate the effectiveness of the proposed scheme. Furthermore, the effect of the prototype filter on the PAPR is investigated, and a tradeoff exists between the PAPR and out-of-band performance.

Adverse weather conditions such as snow, fog, and rain pose significant challenges to LiDAR-based perception models by introducing noise and corrupting point cloud measurements. To address this issue, we propose TripleMixer, a robust and efficient point cloud denoising network that integrates spatial, frequency, and channel-wise processing through three specialized mixer modules. TripleMixer effectively suppresses high-frequency noise while preserving essential geometric structures and can be seamlessly deployed as a plug-and-play module within existing LiDAR perception pipelines. To support the development and evaluation of denoising methods, we construct two large-scale simulated datasets, Weather-KITTI and Weather-NuScenes, covering diverse weather scenarios with dense point-wise semantic and noise annotations. Based on these datasets, we establish four benchmarks: Denoising, Semantic Segmentation (SS), Place Recognition (PR), and Object Detection (OD). These benchmarks enable systematic evaluation of denoising generalization, transferability, and downstream impact under both simulated and real-world adverse weather conditions. Extensive experiments demonstrate that TripleMixer achieves state-of-the-art denoising performance and yields substantial improvements across all downstream tasks without requiring retraining. Our results highlight the potential of denoising as a task-agnostic preprocessing strategy to enhance LiDAR robustness in real-world autonomous driving applications.

The ideal phase shift model and the acquisition of channel state information (CSI) are practically difficult to realize in reconfigurable intelligent surface (RIS) assisted communication systems. Therefore, these have greatly troubled the beamforming design of multiple-input–multiple-output (MIMO) systems via RIS. This article presents a robust beamforming design framework in the RIS-enhanced MIMO communication networks in the presence of considering practical phase shifts for the RIS and imperfect CSI of the cascade channels. More specifically, we propose an alternative optimization framework and adopt the distributed alternating direction method of multipliers (ADMMs) method to solve the sum-rate maximization problem by combining the true environment model (TEM) and the mismatch environment model (MEM) with beamforming. In particular, the proposed scheme is also used for the MEM scenario. It shows that the sum rate of the ADMM scheme with MEM under low SNR is about 3 bps/Hz more than the barrier function penalty method, approximately 6 bps/Hz more than the accelerated projected gradient method. Next, when the SNR is 5 dB, the above three schemes are greater than the semidefinite relaxation (SDR) method. But, the above methods are smaller than the SDR method in MEM. Then, the sum rate of the SDR method with the TEM is approximately 1.6 times of the MEM, while the CPU time of both is consistent with the computational complexity. Therefore, the differences between the two models affect the performance achieved by different methods. The proposed approach sheds a light to engineers working in wireless communication on enhancing the transmission robustness of existing wireless networks.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) network and its low-complexity user-centric (UC) alternative rely on accurate and up-to-date channel state information (CSI) for combining and/or precoding a large number of signals received by the distributed antenna array to achieve their anticipated performance gains. When serving highly mobile Internet of Things (IoT) devices, limited Line-of-Sight (LoS) information and nonnegligible channel aging (CA) effects will undermine CSI acquisition and inevitably degrade system performance. As a result, fundamental limit assessment and performance degradation mitigation under imperfect CSI are important for practical system designs. In this article, we focus on the performance of UC CF-mMIMO IoT systems in the interaction of sufficient and insufficient LoS knowledge, nonisotropic Non-LoS components, and heterogeneous CA effects. The novel and exact closed-form expressions for uplink spectral efficiency (SE) under the impaired CSI are derived. Numerical results verify the correctness of the SE expressions and reveal that the system parameters, such as resource block length and pilot overhead, should be optimally preconfigured according to environmental information and transmission tasks to alleviate the performance degradation caused by the imperfect CSI. Finally, a UC soft handover scheme is designed to enhance the mobility support of CF-mMIMO IoT systems in practical implementation.

We investigate the effect of spatially and temporally correlated channels on the transmission performance of multicell multiuser massive multiple-input–multiple-output (MIMO) vehicular networks in generic nonisotropic scattering environments. A new channel model is established to evaluate the harmfulness of the nonisotropic-scattered Angle-of-Departure/Angle-of-Arrival (AoD/AoA) spread and the high mobility of users on the uplink transmission. We derive the expressions of achievable spectral efficiency (SE), taking into account the effects of Line-of-Sight propagation, channel aging, and pilot contamination. Specifically, two novel receive combining schemes, namely, the aging-aware maximum ratio combining and the aging-aware minimum-mean-square error combining, are presented to mitigate the SE decline caused by outdated channel state information. A low-complexity pilot assignment algorithm is proposed to suppress pilot contamination. We find that the quasi-static assumption of the channel may be unsafe for the system design of the vehicular networks even within a single transmission block period lasting from hundreds of microseconds to a few milliseconds. We observe that there exists an optimal block size$C_{\mathrm {opt}}$that maximizes area SE. Especially,$C_{\mathrm {opt}}$can be expressed as a function of movement speed, AoD spread, and AoA spread. Numerical results are presented to validate the efficacy of the proposed schemes and highlight the importance of correct performance evaluation for practical massive MIMO system designs.

This article proposes a method for enhancing vehicle monocular 3-D perception using vehicle reidentification (Re-ID) and collaborative vehicle infrastructure systems (CVIS), aimed at enhancing the perception range and safety of the majority of intelligent connected vehicles currently using cameras. The method initially employs a monocular 3-D perception approach to extract images and rough 3-D information of traffic targets from the vehicle side. Following that, an adaptive compression method called Adaptive-FALSH is introduced, which, combined with vehicle Re-ID technology, enables efficient compression and correlation of vehicle Re-ID features. Ultimately, a perception fusion method dubbed Hamming registration Hamming fusion is proposed, merging monocular 3-D detection results from the vehicle side and high-precision perception results from the roadside. This method quickly extend roadside perception results in real-time to the vehicle side, thereby enhancing the vehicle’s perception range. Experimental results demonstrate that this method efficiently merges 3-D target perception information from both vehicle and road sides using just a few hundred bytes, without the need for high-precision maps and global positioning system assistance. While reducing communication data volume, this method also effectively widens the perception range of intelligent connected vehicles.

The efficient exchange of perception information between vehicles and infrastructure is crucial for implementing vehicle-infrastructure (VI) collaborative intelligent driving. To address the high real-time requirements of VI communication and lack of intelligence and flexibility in VI cooperation, this study proposes an efficient collaborative perception method based on vehicle re-identification for VI collaboration scenarios. The real-time requirements of such scenarios are addressed and a lightweight vehicle re-identification network called ShuffleBNLSH is designed. This network is combined with a hash algorithm to quickly generate ID information for collaborative sensing targets. Based on the state of the VI communication channel, the network can adaptively extract the bit features of the perceived vehicle target, adjust the feature length, and quickly perform feature matching for vehicle target re-identification. To rapidly fuse the VI collaborative perception information combined with the re-identification results and LiDAR 3D perception information from the vehicle and infrastructure, we designed a mini-ICP algorithm that can automatically select feature points and perform point-cloud registration. Experimental results show that the amount of data transmitted by a single target in cooperative sensing can be as small as hundreds of bits during the fusion of sensing targets on the vehicle and infrastructure sides. This reduces the bandwidth requirements for fusing perception targets, accelerates feature transmission and matching, and expands the perception range of VI collaborative autonomous vehicles without GPS information.

This paper investigates the performance of cascaded channel estimation in the millimeter-wave multiple-input multiple-output (MIMO) systems via a reconfigurable intelligent surface (RIS). The main goal is to design a low-overhead cascaded channel estimation scheme that provides for a good performance of wireless networks deployed in hot spots. However, the high-dimensional channel of RIS links and the passive feature of RIS without signal processing capability make the acquisition of channel state information a challenging, and thus, channel estimation in RIS-assisted wireless communication systems requires high pilot overhead. In the practical scenario, there are limited scatters around the base station and the RIS. The angular cascaded channel has a few non-zero elements, which exhibit the sparsity. Benefiting from these special channel characteristics, we propose a Sparsity Generalized Orthogonal Matching Pursuit based cascaded channel estimation scheme by integrating the structured sparsity into the GOMP algorithm to reduce pilot overhead. the proposed solution improves estimation performance by approximately 2 dB, while reducing average running time to a level comparable to the Oracle LS scheme. Meanwhile, our scheme incurs a pilot cost decrease of approximately 14.3% compared to traditional approaches, when the NMSE is -2 dB. The proposed scheme sheds a light to engineers working in wireless communication on enhancing the channel estimation performance of existing wireless systems.

Advances in information and control technology act as enablers for the utilization of Connected Autonomous Vehicles (CAVs). Despite the extensive research on trajectory control, most investigations assume that either the communication process is perfect or CAVs know their exact location and system state. To this end, we propose a novel trajectory control scheme that allows a Centralized Manager (CM) to account for the limited communication resources and trajectory uncertainty due to vehicle state evolution errors and measurement errors. In particular, the scheme includes covariance-based context-aware timely status update strategy optimization using Kalman estimation techniques and robust trajectory control using the recently developed theory of covariance steering. Moreover, the original stochastic trajectory control problem under non-convex feasible regions is converted to a deterministic mixed integer programming (MIP) problem in terms of the accessible estimated state. Simulation results illustrate the effectiveness and robustness of the proposed scheme and reveal the impact of the different update frequencies on the trajectory.

Cell-free (CF) massive multiple-input multiple-output network and its low-complexity user-centric (UC) alternative rely on accurate channel knowledge for combining and precoding to achieve their claimed performance gains. When serving vehicular users with high mobility, the non-negligible channel aging effect will inevitably degrade system performance, and accurate performance evaluation is essential to system design. In this paper, we investigate the aging uplink (UL) spectral efficiency (SE) in the UC CF networks with non-isotropic scattering conditions. We adopt the von Mises distribution to model the angle-of-departure (AoD), resulting in an analytically tractable channel autocorrelation function that allows us to analyze the time-varying properties of the channel for an arbitrary AoD spread and the user’s moving direction. We derive a closed-form signal-to-interference-and-noise ratio expression with large-scale fading decoding (LSFD) for an achievable UL SE. The simulated results in a 3GPP-recommended vehicular network scenario show that there is an optimal subframe length to achieve the maximum area average SE. The LSFD cooperative strategy of the UC CF network significantly increases the optimal subframe length in the non-isotropic scattering environment, which reduces the pilot overhead and improves the spectrum utilization eventually.

Massive multiple-input multiple-output (MIMO) relies on accurate channel estimation for precoding and receiving to achieve its claimed performance advantages. When serving vehicular users, the rapid channel aging effect greatly hinders its advantages, and a careful system design is required to ensure an efficient use of wireless resources. In this paper, we investigate this problem for the first time in a non-isotropic scattering scenario. The von Mises distribution is adopted for the angle of arrival (AoA), resulting in a tunable channel temporal correlation coefficient (TCC) model, which can adapt to different AoA spread conditions through the k parameter and incorporates the isotropic Jakes-Clarke model as a special case. The simulated results in a Manhattan grid-type multi-cell network clearly demonstrate the impact of channel aging on the uplink spectral efficiency (SE) performance and moreover, in order to maximize the area average SE, the size of the transmission block should be optimally selected according to some linear equations of k.

Millimeter-wave (mmWave) vehicular communication is a key technology that enables autonomous vehicles to collaborate in environment perception, thereby improving traffic efficiency and safety to a new level. Many recent works have focused on relay-based solutions to overcome the inherent defects of mmWave, such as the severe path loss and its sensitivity to blockages. However, the selfishness of the vehicles is often ignored. Considering the application-oriented nature of vehicular communication, we propose an information value-based relay strategy for mmWave vehicle-to-infrastructure (V2I) transmission in this paper. Specifically, the vehicles are allowed to make relay decisions based on the evaluation of the value of messages from their own perspectives. To this end, a simple relay probability model based on the required awareness range is introduced. Through the use of stochastic geometry to model the vehicular network, the outage performance is analyzed and the results are validated by simulations. Impacts of both network and application related parameters on the outage performance are investigated. These preliminary results laid the foundation for the further expansion of the information value-based relay strategies to a wider range of network settings.

A novel generalized polarization-space modulation (GPSM) is proposed for polarized multiple-input multiple-output (MIMO) systems with a limit number of radio frequency (RF) chains. In the spatial domain, multiple dual-polarized (DP) transmit antennas are activated, and then combinations of those indices are used to convey information. While in the polarization domain, depending on the random input bits, only one polarization state is selected for each active DP transmit antenna to transmit information following the rule of the polarized shift keying. At the receiver, the maximum likelihood detector is employed as a benchmark to detect information bits being used to select the polarization state and activated DP antennas. In the detector, imperfect channel state information (CSI) is taken into account. Two less computationally complex detectors, i.e., a linear detector and a sphere decoding (SD) detector are proposed to relieve the computational burden. Sacrificing the average bit error probability (ABEP) performance, the proposed linear detector can reduce the computational complexity significantly. The proposed SD detector can achieve the optimum ABEP performance, while reducing computational complexity by reducing the search space. A closed-form union upper bound (UUB) on the ABEP of the GPSM system with imperfect CSI at the receiver is analytically derived and validated through simulations. From the UUB, a loose asymptotic bound on the ABEP, which sheds light on deriving the diversity gain and the coding gain, is derived. Numerical results show that the signal-to-noise ratio loss caused by increasing the number of transmit antennas is less than 3 dB while the spectral efficiency is increased by 7 b/z/Hz. Therefore, the GPSM can be a promising candidate of down link massive MIMO systems to achieve a high spectral efficiency with a limit number of RF chains.

The Block Korkine-Zolotareff (BKZ) reduction can trade off between performance and complexity by adjusting the size of the local reduction blocks, and therefore, offers the opportunity to approach the optimal performance achieved by the KZ reduction at a lower cost, when adopted by the lattice reduction (LR) aided successive interference cancellation (SIC) receiver for multiple-input multiple-output (MIMO) systems. In this paper, a novel complex-domain BKZ (CBKZ) reduction algorithm is developed by employing an on-demand complex-domain Schnorr-Euchner (SE) enumeration strategy for local shortest lattice vector searching, and a tailored unimodular transform scheme for basis updating. The sorted-QR decomposition coupled with size reduction is employed for pre-processing, rather than the widely adopted Lenstra-Lenstra-Lovász (LLL) reduction. Simulation results show that the proposed CBKZ reduction achieves close-to-optimal performance by using relatively small block sizes, and at the same time, causes only a small proportion of the overall computations required by its competitors: the complex LLL (CLLL) reduction and CLLL-deep reduction. In addition, a unified mathematical framework is provided to encompass the traditional SIC detection with ordering, the LR-aided SIC detection, and the integer-forcing (IF) SIC detection, under the same umbrella.

Channel-width adaptation can significantly improve the connectivity, capacity and reduce the power consumption in wireless networks. The OFDMA-based channel-width adaptation based on traffic demand has been studied in wireless networks with sufficient spectral resources. However, the traffic demand may not be fully satisfied in resource-limited scenarios. In this paper, we study the channel-width adaptation problem in wireless mesh networks (WMNs) with limited spectral resources. More specifically, considering diverse quality-of-service (QoS) requirements, a QoS-aware channel-width adaptation scheme is proposed. First, resource allocation with QoS-aware channel-width adaptation is modelled as an optimization problem. Genetic algorithm is employed to get a near-optimal solution. Then, in order to reduce the computational complexity, a greedy algorithm is developed to suit the highly dynamic traffic demand. Simulation results show that the proposed low-complexity algorithm can guarantee the QoS support in resource-limited scenarios.

The Reduced Power Subframes (RPS) are encouraged to be applied in the LTE-Advanced Heterogeneous Networks (HetNets), to reduce the capacity loss caused by the Almost Blank Subframes (ABS). However, the RPS are supposed to be used in the macrocells only. In fact, the RPS can also be used in femtocells to mitigate the interference that the macrocell edge users suffers, but its performance is not investigated yet. In this paper, we introduce the RPS both in the macrocells and the femtocells. The results of the Signal to Interference Ratio (SIR) coverage probability under the stochastic geometry framework are derived in a closed-form which is verified through Monte Carlo simulation. Based on these results, the macrocell edge users' SIR coverage and the average rate coverage of the network (the average fraction of users achieving a target rate) are analysed numerically. Our proposed scheme enhanced both the SIR of the macro edge users and the average rate coverage probabilities.

In this paper, the spatial modulation (SM) technique is employed at the source and relay nodes in a full-duplex two-way relay channel (FD-TWRC) to support spectral-efficient bi-directional communications while guaranteeing a low cost implementation. Maximum likelihood detectors are employed at each node that is subject to an intrinsic self-loop interference. We first propose a tight upper bound on the average bit error probability (ABEP). Then, based on the ABEP upper bound, an asymptotic ABEP expression is derived in the high signal-to-noise ratio (SNR) regime. Exploiting the asymptotic ABEP, an exact SNR threshold for the selection between FD-TWRC-SM and half-duplex (HD)-TWRC-SM is derived in a closed form, which sheds light on when it is beneficial to select the FD (or HD) mode. In addition, the power allocation (PA) among sources and relay is investigated, through which an optimal PA factor in terms of ABEP is obtained. All analytical results derived in this paper are verified by Monte Carlo simulations, from which some new insights are obtained on the performance of FD-TWRC-SM.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

To obtain the optimal coefficient matrix for the integer-forcing (IF) linear receiver, the successive minima problem (SMP) on lattices needs to be solved. By decomposing SMP into a series of subspace avoiding problems (SAP) and developing a modified sphere-decoding (SD) algorithm based on the Schnorr-Euchner (SE) enumeration strategy to solve each instance of SAP, two practical algorithms are constructed to solve SMP exactly for real and complex lattices respectively. The initial radius and the starting position on the search-tree of the SAP algorithm are optimized by exploiting the intermediate results obtained in previous rounds. As compared to the Minkowski reduction algorithm, the proposed complex SMP algorithm brings not only more freedom in lattice code design, but also computation reduction in finding the coefficient matrix. Moreover, benefiting from the tree-search initialization optimization, the proposed real SMP algorithm can bring even more computation reduction when the system size is large. However, the proposed algorithms achieve practically the same performance as Minkowski reduction for the IF linear receiver, as the Minkowski-reduced basis approximates successive minima very closely.

The Spatial Modulation (SM) transmission scheme boosts the spectral efficiency and achieves the multiplexing gain by activating a single transmit antenna in each time slot. Radio wave propagation characteristics determined by the environment is a decisive factor for the SM system. In this paper, we investigate the performance of the SM scheme over real-world 4x4 Multi-Input Multi-Output (MIMO) channels measured in typical indoor scenarios. Firstly, a MIMO channel sounder is established. Based on the sounder, 15150 complex 4x4 MIMO channel matrices are measured inside a typical teaching building under both Line of Sight (LOS) and None Line of Sight (NLOS) scenarios. Secondly, by comparing the SM system over the measured channel and commonly-used channel models, we prove that both the Independently and Identically Distribute (i.i.d.) Rayleigh and the Spatial Correlation (SC) channel model are oversimplified, and that only practical experiences can yield definitive answers to the achievable real-world system performance. Thirdly, the Average Bit Error Probability (ABEP) performance of the SM system is studied based on the measured data under a variety of system configurations. The study of different receive antenna array settings (4x4, 4x2 and 4x1 MIMO setups) approves the significance of combining scheme at the receiver. SM systems employing different signal constellations (Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (16QAM)) are also investigated and some interesting results are revealed. Lastly, performance assessment of SM against State-Of-The-Art (SOTA) MIMO schemes (Space-Time Block Code (STBC) and Vertical Bell Labs Layered Space-Time (V-BLAST) code) is conducted. Results show that for a 4x4 MIMO, the low-complexity SM scheme outperforms both STBC and V-BLAST.

Abstract In this paper, based on the analysis of the experimental data using a new post‐processing method for time‐domain channel measurements, a new double‐cluster statistical model for UWB systems with a bandwidth lower than 1 GHz in non‐line‐of‐sight (NLOS) indoor propagation environment is proposed. By using the proposed model, both the model itself and the parameter estimation of the corresponding model are simplified. By defining the polarity of a particular model parameter, the model has the flexibility to deal with both ‘soft NLOS’ and ‘hard NLOS’ indoor propagation environments. Therefore, the channel impulse responses (CIRs) generated by the proposed model ‘resemble’ the measured CIR better than the SV (Saleh‐‐Valenzuela)/IEEE 802.15.3a model not only in terms of the average values, but also in terms of the cumulative distribution functions (CDFs) of the small‐scale statistics. Copyright © 2008 John Wiley & Sons, Ltd.

By exploring the deterministic characteristics of the measurement data, a new propagation model with two deterministic clusters and stochastic arriving rays within each cluster is proposed. When considering cumulative distribution function (CDF) of the three key channel statistics, the proposed model fits the measurement data better than SV/IEEE 802.15.3a model which is seen as standard model for UWB indoor propagation channel. That means, with the additional knowledge of the specific environment geometry, the proposed model generating impulse responses "resemble" the measured channel impulse responses better than IEEE model. Moreover, the proposed model's parameters obtaining procedure is simplified by utilizing simple parameters of physical channel.