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.