In this work, we investigate the frequency synchronization and system performance in massive multiple-input multiple-output (MIMO) systems with one-bit analog-to digital converters (ADCs). First, we tackle the challenges arising from severe multi-user interference (MUI) and the non-linearity inherent in one-bit ADCs in orthogonal frequency division multiplexing (OFDM) based on Bussgang decomposition and receive beamforming. To assess the accuracy of the CFO estimation, we analyze its theoretical mean square error (MSE) and investigate how quantization noise influences synchronization precision. Additionally, we introduce a multi-user inference (MUI)-plus-noise whitening technique to mitigate the correlation of the equivalent noise. Finally, we derive an approximate expression for the uplink achievable rate using maximal-ratio combining (MRC) detection scheme. Extensive numerical simulations confirm the effectiveness of the proposed approach.

In recent years, multi-modal semantic segmentation in autonomous driving has garnered significant attention due to its effectiveness under challenging lighting conditions. However, current segmentation approaches primarily focus on segmentation techniques without addressing the critical communication challenges inherent in internet of vehicles (IoV). Unlike traditional communications that transmit source data, semantic communications transmit only task-relevant semantic information, significantly reducing data traffic while ensuring the accuracy of task execution. This paper introduces a novel cooperative multi-modal semantic communication framework designed to enhance semantic segmentation in autonomous driving systems. By compressing redundant information and transmitting only essential semantic features, the proposed scheme enables continuous data transmission with drastically reduced data volume. Moreover, this scheme not only improves communication efficiency but also ensures reliable segmentation performance across diverse data modalities. Experimental results validate the effectiveness of proposed scheme, demonstrating its ability to achieve high compression ratios, robust segmentation performance, and re-silience to channel noise under varying lighting conditions.

As the number of users connected to communication networks such as cellular networks and Internet of Things (IoT) networks increases, massive multiple-input multiple-output (MIMO) technique has been widely adopted to improve the spectral and energy efficiency. However, the multi-user frequency synchronization problem must be solved before channel estimation and data detection. Concurrent estimation of multiple carrier frequency offsets (CFO) at base station could be very challenging due to the coexisting and intertwined effects of multiple CFOs and uplink channels in the received signal. In this paper, we consider the frequency synchronization and channel estimation for multi-user uplink massive MIMO systems. To solve the complex multi-CFO estimation problem, we first derive the efficient joint multi-user frequency synchronization algorithm based on the maximum likelihood (ML) criterion, whose high computational complexity is reduced by the proposed Gauss-Newton method. Furthermore, we develop a multi-stage iteration update filtering (MIUF) based multi-user CFO and channel estimation method. The least squares (LS) algorithm is adopted to estimate the channels, based on which the filtering matrix is carefully designed to perform multi-user interference (MUI) suppression. Moreover, considering the effect of CFO error on the channel estimation, an iterative procedure is designed to improve MUI suppression and estimation accuracy. We also analyze the CFO estimation performance and obtain the theoretical expression of mean squared error (MSE). Finally, the effect of CFO error on channel estimation is derived. Numerical results are provided to corroborate the effectiveness of the proposed methods and their superiority over the existing ones.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless communication coverage. However, there is a risk of eavesdropping on the offloading information of terminal users (TUs) because of UAV light-of-sight (LoS) transmission. In this paper, we propose a Dinkelbach-guided deep reinforcement learning (DRL) scheme for secure communication in the UAV-aided MEC network. Specifically, the security calculating efficiency of the network is maximized by optimizing offloading decision and resource allocation under the condition of the data queue stability and minimum calculating requirement. The problem is intractable due to the fractional structure and binary constraint. Firstly, we deal with the fractional structure by taking advantage of Dinkelbach optimization. Then, offloading decision is generated based on DRL and the resource is allocated by successive convex approximation (SCA). Simulation results show that the proposed Dinkelbach-guided DRL scheme efficiently improves the security calculating efficiency of the network.

A channel time variation suppression scheme based on beamforming is proposed for high-mobility orthogonal frequency division multiplexing (OFDM) downlink transmissions. The residual channel time variation after Doppler shift compensation by finite resolution receive beamforming can be measured by the Doppler spread. The interchannel interference (ICI) due to the time-varying channel is then analyzed and the relation between the signal-to-interference ratio (SIR) and Doppler spread is given. Based on the derived Doppler spread and ICI, a beamforming network optimization scheme is proposed to reduce the channel time variation. A closed-form solution is first obtained to minimize the average ICI, which exploits the structure of a tridiagonal Toeplitz matrix. Next, an optimization problem is formulated to minimize the maximum Doppler spread to further verify the impact of average ICI on channel time variation. The sequential parametric convex approximation (SPCA) algorithm is exploited to solve the non-convex min-max problem. Numerical results verify the theoretical analysis.

In this paper, we consider the frequency synchronization issue in orthogonal frequency division multiplexing distributed antenna systems. There may exist multiple carrier frequency offsets (CFOs) between the transmitter and distributed receive antennas. To solve the multi-CFO estimation problem, we first derive the blind maximum likelihood estimator, which has superior estimation performance, but exhibits high computational complexity. Furthermore, we develop a blind pairing-based efficient estimator (PBEE), which divides the multiple antennas into pairs of two. The two CFOs in each pair are jointly estimated with a carefully designed cost function, which proves to be exactly a cosine function parameterized by the two CFOs even with the effect of noise. Consequently, the joint estimation of the two CFOs can be simplified to the one-dimensional search optimization problem. The PBEE can achieve better CFO estimation performance than the existing competitors. From the theoretical mean square error (MSE) analysis of PBEE, we further derive the optimal antenna pairing scheme by minimizing the signal-to-noise ratio degradation caused by the uncompensated residual CFOs. Finally, the Cramér-Rao bound of CFO estimation is derived and numerical results are provided to corroborate the proposed studies.