Low Earth orbit (LEO) satellite communication offers significant advantages over terrestrial communications, characterized by its cost-effectiveness and extensive coverage. Nevertheless, the relative motion between LEO satellites and user terminals results in a larger frequency offset compared to terrestrial communications. Meanwhile, the maximum round-trip delay within the satellite communication beam is considerably greater. To address these challenges, this paper first proposes a novel preamble based on the Zadoff-Chu sequence and creatively utilizes Affine Frequency Division Multiplexing (AFDM) waveforms for the transmission of the preamble for the first time. Subsequently, to enhance the accuracy of timing advance (TA) detection, a detection algorithm is proposed to distinguish the first-path delay. Finally, the simulation results confirm that the AFDM based preamble outperforms the preambles based on Orthogonal Frequency Division Multiplexing (OFDM) in mitigating the impact of frequency offset in LEO satellite communication without the requirement of pre-compensation.

This paper investigates the hybrid beamforming for reconfigurable intelligent surface (RIS)-assisted multiuser multiple-input multiple-output (MIMO) systems. In order to stabilize the quality of service (QoS) for each user, the transmit power is minimized under multiple mean squared error (MSE) constraints. The optimization problems of analog beamforming and RIS reflection beamforming are solved by the proposed inner approximation-iterative coordinate ascent (IA-ICA) method. Compared with the semidefinite relaxation algorithm (SDR) and latest inner majorization-minimization (iMM) method, our approach always converges to lower transmit power. Moreover, a modified subgradient method (MSM) is developed to tackle the classical convex digital precoder optimization problem and obtain the optimal values. Simulation results verify that the proposed method can obtain higher performance solutions than the existing work. The proposed IA-ICA method is more suitable for solving the non-convex quadratically constrained quadratic program (QCQP) with extra constant modulus constraints.

Non-terrestrial networks (NTNs) are poised to play a critical role in 6G and future communication technologies for providing reliable, wide-reaching, cost-effective connectivity. In NTNs, random access is fundamental for establishing reliable and efficient communication between user equipment (UE) and satellite. Since NTNs are subject to challenges such as high dynamics, long-range propagation, and variable Doppler effects, both timing advanced (TA) and carrier frequency offset estimation (CFO) must be accurate to enhance the performance, reliability, and efficiency of random access. Based on conjugate symmetric Zadoff-Chu (CSZC) sequences, we propose a novel joint TA and CFO estimation method in this paper. In the proposed method, the fractional frequency offset is iteratively estimated and compensated to eliminate its adverse impact on the ZC sequence in the correlation. Accurate TA and integer CFO estimations are achieved by leveraging the excellent zeroautocorrelation and symmetry properties of CSZC sequences. Simulations show that the proposed method outperforms the existing methods in terms of both TA and CFO estimation performance in NTN environments with low Signal-to-Noise Ratio and large Doppler shifts.

With rapid development of intelligent vehicles, radio resource allocation in cellular D2D-based V2X communication (C-V2X) has become a key area of research. Traditional resource allocation methods face challenges as difficulty in optimizing complex problems and high computational complexity. While deep reinforcement learning (DRL) has recently emerged as a promising solution, existing DRL-based algorithms exhibit inherent limitations, including quantization errors from discretized power levels and costly retraining associated with weightpredefined optimization. This study introduces an innovative framework to address these challenges. For parameterized action spaces, this paper introduces a multi-agent hybrid policy proximal optimization (MAHPPO) algorithm for continuous power control and spectrum allocation in the cooperative$\mathrm{C}-\mathrm{V} 2 \mathrm{X}$networks, eliminating quantization errors. Furthermore, we develop a parameter transfer-based method to efficiently compute the Pareto Front, significantly reducing resource consumption linked to dynamic weight adjustments. Simulation results demonstrate that the proposed method ultimately yields favorable resource allocation outcomes, enhancing user service quality.

In high-dynamic environments, such as satellites, drones, and autonomous vehicles, the performance of traditional adaptive beamforming methods can be significantly affected by rapidly moving interference. Such interference may shift out of nulls and sometimes enter the main lobe-an issue that most existing methods struggle to handle. To overcome the challenge of static null broadening and main lobe invasion, a new adaptive beamforming algorithm is proposed through two synergistic innovations: real-time null broadening and dynamic main lobe interference suppression. By adding a time attenuation factor in the covariance matrix reconstruction and using virtual interference rotation, the algorithm achieves real-time null broadening and adapts to suppress interference effectively. Additionally, the moving-MUSIC algorithm is used to detect the main lobe interference and estimate its direction, followed by projection elimination to remove it. The simulation results show that the proposed method effectively maintains performance. In conclusion, the proposed method offers a reliable solution for interference suppression in dynamic environments, enhancing the robustness and performance of adaptive beamforming systems.

In this paper, we propose a multilayer channel prior model to better characterize the time-varying features and clustering properties of the channel based on the sparsity of millimeter-wave (mmWave) massive multiple-input multipleoutput (MIMO) channels. Specifically, we utilize a first-order Auto-Regressive (AR) process to simulate the correlation of channel gains over multiple measurements, as well as the angular spread within scattering clusters. Based on the Expectation Maximization (EM) algorithm and the sparse Bayesian learning (SBL) framework, we develop an estimation scheme that considers both the channel temporal correlation and the intra-block correlation. The simulation results show that the proposed scheme exhibits performance advantages with adequate consideration of the channel characteristics.

In this paper, a low-complexity enhanced memory polynomial search (EMPS) model is proposed for complex nonlinear memory effect scenarios in digital pre-distortion (DPD). Since the traditional polynomial models cannot describe the complex nonlinear memory effect of power amplifiers (PAs) well, this paper extends the memory polynomial basis functions to enhance modeling capabilities. Combining the basis function multiplexing strategy and the iterative greedy search method, the proposed model can achieve acceptable linearization performance with low complexity. Moreover, operations such as amplitude segmentation function or phase compensation can be introduced to extend the proposed model and increase the modeling capability. Experimental results show that the proposed model not only performs well in fully sampled wideband scenarios but also is able to obtain better DPD results in undersampled wideband scenarios.

Aeronautical communication (AC) is considered as one of the core technologies for future wireless networks. In practical AC scenarios, the communication links suffer from severe Doppler effect caused by the high mobility of the aerial user equipment (AUE) on the aircraft, resulting in a rapidly time-varying propagation channel. In addition, compared with conventional terrestrial communication systems, an AC system is usually expected to support a much wider cell coverage. In this case, the round trip delay (RTD) of signal transmissions becomes larger and more difficult to be estimated, thus making it challenging to design a reliable random access scheme for AC scenarios. To address the above-mentioned issues, in this paper, we propose a new random access preamble detection algorithm for AC systems. The proposed scheme employs short preamble formats and detects the preamble by exploiting a shifted partial combination mechanism. Thus, it is capable of offering a better detection performance and improved spectral efficiency (SE), while still maintaining a large coverage under high Doppler shifts. Furthermore, we design an RTD estimation approach based on the so-called dual-end correlation operation, which can help to achieve more robust RTD estimates. The superior performance of the proposed technique is verified through both simulations and lab experiments in an emulated challenging scenario, where as many as 30 AUEs traveling at a velocity of 1260 km/h within a cell radius of 200 km can be supported.

In this paper, we study beamforming design for multi-user MIMO cognitive radio systems, where a secondary base station transmits multiple data streams to multiple secondary users while imposing interference on primary users. We focus on the weighted sum rate (WSR) maximization problem with the sum power constraint (SPC) and the interference constraints (ICs) by optimizing the beamforming matrices. Firstly, through an analysis of the generalized utility optimization problem, we prove that the WSR maximization problem with a single quadratic constraint can be simplified to an unconstrained WSR maximization problem with adaptive covariance matrices, which can be further solved by the weighted minimal mean square error (WMMSE) method with much lower complexity. Then, we propose the modified subgradient method (MSM)-reduced (R)-WMMSE algorithm for the general scenario and the null-space projection (NSP)-R-WMMSE algorithm for the special scenario with zero ICs. Finally, theoretical and numerical results show superior performances of the proposed algorithms compared to benchmark schemes in terms of computational complexity.

The vision for the 6th generation wireless systems (6G) aims to achieve a space-air-ground integrated seamless global coverage network. Low Earth Orbit (LEO) satellite communication is a cost-effective solution that offers extensive coverage over terrestrial communications. Nevertheless, the relative motion between LEO satellites and user terminals results in a larger Carrier Frequency Offset (CFO) compared to terrestrial communications. Meanwhile, the maximum round-trip delay within the satellite communication beam is considerably greater. To address these challenges, this paper creatively proposes the utilization of Affine Frequency Division Multiplexing (AFDM) waveform for the transmission of preambles for the first time. Specifically, this paper firstly focuses on the Discrete Fourier Transform (DFT) based AFDM waveform for Random Access (RA) preamble transmission in LEO satellite communications. It is demonstrated that AFDM modulation is able to eliminate the CFO of the Zadoff-Chu sequence. Then, to enhance the accuracy of Timing Advance (TA) detection, a detection algorithm is proposed to distinguish the first-path delay. Subsequently, three distinct preamble formats are designed for different scenarios proposed in the 6G use-case requirements. Finally, the simulation results confirm that the AFDM preambles outperform the preambles based on Orthogonal Frequency Division Multiplexing (OFDM) in mitigating the impact of CFO in LEO satellite communications, without the requirement of pre-compensation.

Hybrid analog and digital beamforming (HBF) can be utilized to reduce the number of radio frequency chains in millimeter-wave massive multiple-input multiple-output (MIMO) systems. This letter investigates a HBF design for multiuser MIMO systems. By applying the fractional programming, an universal algorithm is proposed to maximize the sum rate of the multiuser system with the fully-connected and partially-connected architectures. The closed-form digital beamformers are updated by the prox-linear block coordinate descent (BCD) method. Then we transform the non-convex analog precoder optimization problem into a quadratically constrained quadratic program (QCQP), which is widely existed in analog beamforming and reconfigurable intelligent surface design. An iterative coordinate ascent (ICA) algorithm is proposed for the kind of problem, which can obtain high-performance solutions and much more computationally efficient than the methods using linear approximations. Moreover, we give an optimality condition to ensure the converged solution is globally optimal. Numerical results show the superior performance of the proposed ICA algorithm and HBF scheme.

In the vehicle-to-everything (V2X) communication, cellular device-to-device (D2D) communication offers numerous advantages, such as enhanced data rates and decreased traffic load. So cellular D2D-based V2X communication has emerged as a focal point of interest. Confronted with the challenge of acquiring complete channel state information (CSI) in a highly mobile vehicular environment, this paper endeavors to optimize system energy efficiency (SEE) while meeting the demands of heterogeneous links solely through statistical CSI. We propose a resource allocation algorithm that jointly optimizes power control and spectrum allocation in V2X networks. Firstly, a low-complexity iterative power control algorithm is introduced to obtain the optimal power control for a single reuse pair. Secondly, the Munkres algorithm is employed to determine the optimal spectrum allocation. Finally, simulation results show that the proposed algorithm outperforms the existing corresponding resource allocation method, in enhancing SEE.

Compared to uniform linear arrays (ULAs), uniform planar arrays (UPAs) provide a more flexible and compact deployment structure for massive multiple-input multiple-output (MIMO). Considering the large number of antennas in UPA, the conventional downlink channel estimation method has a high overhead in pilot training. To deal with this problem, we develop an off-grid sparse Bayesian learning (SBL) algorithm for downlink channel estimation based on compressed sensing (CS). Specifically, we eliminate the coupling of azimuth-angles and elevation-angles in the steering vectors by angle redefinition. Moreover, we introduce a selection strategy for grid point refinement to guarantee algorithm convergence. Simulation results reveal that the proposed off-grid SBL algorithm exhibits better performance than orthogonal matching pursuit (OMP) and existing SBL-based algorithms.

By exploiting the inherent hardware characteristics of wireless devices, radio frequency fingerprint identification (RFFI) has been widely applied in device authentication and spoofing attack detection to improve the security. However, due to the dependence on the training sample size, the state-of-the-art deep learning (DL)-based identification methods will face serious overfitting problem with inadequate training samples. Besides, in noncooperative scenarios, most existing methods cannot tackle the challenge of zero-shot identification, i.e., identifying the objects outside of the training data with no prior samples, which obstructs their practical applications. To solve these two problems, in this article, we propose a novel identification method that combines the deep neural network (DNN) and the unsupervised clustering. In this design, after offline training, the triplet loss convolutional neural network (TLCNN) can be utilized to extract the features of the radio frequency signals outside of the training set. Then, the$K$-means++ clustering algorithm is applied to the extracted features to realize zero-shot RFFI. Moreover, to improve the identification performances of the proposed design under small sample conditions, the random integration (RI) augmentation and the variational mode decomposition (VMD) are exploited to preprocess the input signals, and a$K$-value estimation method for the$K$-means++ clustering is proposed to ensure the effectiveness of the proposed design. Experiment on digital mobile radio (DMR) portable radios validates that the proposed design can achieve the best identification performances and the minimal time and space costs compared with the benchmark schemes.

The super augmented nested array (SANA) is a new sparse array (SA) proposed by expanding, changing the inter-element spacing (IES), and splitting the nested array (NA). The proposed SANA further enhances the sparsity of the NA to achieve lower mutual coupling (MC) and higher uniform degrees of freedom (uDOF) than the existing SAs. Specifically, the proposed SANA contains five uniform linear arrays (ULAs), and all sensor positions can be obtained from a closed-form expression. Moreover, the weight functions and achievable uDOF of the proposed SANA are analyzed in detail. It has been shown through simulations that SANA has a significant advantage over existing SAs for direction-of-arrival (DOA) estimation.

This paper considers a MIMO cognitive radio system, where a secondary base station (SBS) transmits signal to multiple secondary users (SUs), while imposing interference to multiple primary users (PUs) in the primary network. We aim at maximizing the weighted sum rate (WSR) of all the SUs subjected to the power constraint of SBS and interference constraints of multiple PUs, by optimizing the linear beamformers matrices associated with all the SUs. We first reformulate the original problem into a convex weighted minimum mean square error (WMMSE) problem, and then a modified subgradient method (MSM) is proposed to solve the WMMSE problem. Simulation results also show that the proposed MSM algorithm outperforms other existing algorithms with lower computational complexity.

With the rapid construction of the Starlink project, the feasibility and superiority of LEO communication satellites have been fully demonstrated. Studying the random access process of LEO satellite is the trend of the times. First, we design a conjugate overlapping multi-segment cascading preamble format based on tag sequences according to the preamble design criteria and the channel characteristics of satellite communication, which increases the number of available preambles. Then, we design the corresponding time advance (TA) detection and collision detection algorithms. Simulation results show that the success rate of random access and collision detection of the proposed preamble is higher than that of the existing designs in satellite communication channels with large delay and large frequency offset characteristics, which proves the robustness of the proposed method. Therefore, the LEO satellite preamble designed in this paper improves anti-frequency offset capability and the random access efficiency, which also provides a certain degree of value for the further widespread application of LEO satellite communication.

Hybrid analog and digital precoding architectures facilitate the practical implementation of millimeter wave (mmW) massive multiple-input multiple-output (mmWmMIMO) systems by reducing the number of employed radio frequency chains. The spectral efficiency (SE) optimization problems of these systems are non-convex and NP-hard due to the joint optimization between the analog and digital precoding and the constant modulus constraints required by the analog phase shifters. To address this problem, we propose a decoupled two-stage design where in the first stage, a closed approximation of the effective channel is proposed, hence the SE maximization problem is recast as a effective channel gain maximization problem, then the analog precoding matrices are determined, which are taken into account in the second stage to design the digital precoding matrix to maximize the system’s SE. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding schemes.

The family of aeronautical communication systems is considered as one of the core technologies for the beyond-the-fifth-generation (B5G) networks, thus attracting rapidly increasing attention from both industry and academia. In this paper, a new multiple-input multiple-output (MIMO) precoding scheme is proposed for aeronautical communication systems, where the transmit power for the aerial user equipment (UE) approaching the target cell is maximized based on the direction information. Specifically, we first transform the precoding optimization problem into a linear programming problem, and then develop two algorithms to derive the precoders for two transmission modes, namely the multi-stream and single-stream transmissions. For the multi-stream case, an algorithm based on manifold optimization is proposed, which guarantees the equal power allocation for each antenna. For the single-stream case, we derive a precoder by a numerically stable algorithm that extends the Newton-Raphson method. Simulation results show that the proposed precoding schemes can improve the link performance of the UEs with known directions, while ensuring that the performance of the UEs with unknown directions can still be close to that achievable by conventional schemes.

In this paper, we propose a novel visible light positioning (VLP) scheme under multi-path propagation, which is a practical scenario that has not been well studied for VLP. The new scheme exploits the so-called dual-mode light-emitting diode (DM-LED) of different radiation lobe mode numbers at the transmitter and a photodiode at the receiver. Specifically, we devise a method that utilizes a radiation angle measurement approach for DM-LED, derive the Cramér-Rao lower bound (CRLB) of the estimated distance and analyze the characteristics of key parameters. In addition, two localization algorithms based on linear and nonlinear least squares methods are developed. Simulation results show that under the ideal light-of-sight scenario, the CRLB of the proposed VLP scheme can be close to that of the conventional received signal strength (RSS) aided VLP scheme. Furthermore, we implement the first ever prototype DM-LED lamp to validate the new DM-LED aided VLP system model. Both simulation and experiment results demonstrate that the proposed system outperforms its RSS-aided VLP counterpart in terms of positioning accuracy in the more realistic, and thus more challenging multi-path scenario, even with a tilting receiver.

In practical underwater visible light communication (UVLC) scenarios, the communication links inevitably suffer from many stochastic channel effects including multi-path dispersion, scattering, turbulence, etc., and/or from the mobility of the transceiver, therefore resulting in a time-varying, location dependent non-stationary propagation environment. Naturally, how to design a cost-efficient channel estimation (CE) for UVLC systems becomes a significant technical challenge, due to the complicated channel characteristics and implementational constraints in underwater environment. In this paper, we propose a class of Bayesian CE algorithms for fractionally-sampled optical orthogonal frequency division multiplexing (FS-OOFDM) assisted UVLC systems. The so-called Sherman-Morrison formula (SMF) based CEs (SMF-CEs) exploit the property of rank-one structure of the channel covariance and autocorrelation matrices in the delay domain, to achieve a superior performance at a modest computational complexity.

Aeronautical communication (AC) has recently attracted increasing research interests because of the growing demand of on-board data transmission. However, in this high-mobility scenario, channel estimation (CE) is a challenge due to the large Doppler shift effect. In this paper, a new CE algorithm exploiting the a priori delay information is proposed for AC systems. Specifically, the proposed algorithm approximates the channel based on the basis expansion model (BEM) and estimates two salient paths according to the sparse characteristic of aeronautical channels. As a result, the computational complexity of the CE can be effectively reduced. Furthermore, noting that the delay depends on the geographical paths, a coarse estimation is firstly performed to obtain the delay. Then, while such an a priori delay is used as a coarse estimate, the accurate delay is detected by the peak value indices (PVI) of the instantaneous signal frequency correlation (SFC) at the receiver. Simulation results validate that the proposed scheme outperforms the traditional BEM algorithms, even at a user equipment (UE) mobility of as high as 1020m/s.

Recently, visible light communication (VLC) has attracted increasing research interests, thanks to its notable advantages including for example abundant spectra, simultaneous illumination and communication, radiation-free deployment, etc. Benefitting from the widely availability of light emitting diode (LED), the VLC systems employing LED arrays are ready to exploit the multiple-input multiple-output (MIMO) technologies, where a high degree-of-freedom in, for example, the precoding design can be utilized for system performance enhancement. In this paper, we propose a new precoding scheme for multiuser (MU) MIMO-VLC systems, where the classical block diagonalization precoding (BDP) technique is applied based on the properties of the LED’s dynamic range. The resultant weight-adjusted BDP (WA-BDP) scheme generates an interference-free matrix by the traditional BDP approach, and then tunes the weights of the LEDs such that improved channel gains can be obtained. We verify the theoretical analysis by simulation results, which demonstrate the superiority of the proposed scheme in terms of the bit error rate (BER) performance.

Device-to-device (D2D) based packet caching technologies recently attract increasing attention, thanks to their great potentials to facilitate network traffic offloading. Despite the many popular issues arising in the D2D caching area, one of the new perspectives, namely the competition due to the packet request diversity originating from various D2D user equipment (UE) groups is not sufficiently investigated. In this work, we analyze several key aspects of the competition for packet allocation among diverse packet requests. Firstly, we study the impact from diverse group proportions on the system throughput and the packet allocation fairness. Particularly, the novel group separation index (GSI) is introduced, which helps to reflect the packet allocation fairness. We derive and analyze both the upper and lower bounds of GSI. Secondly, we investigate how the concentration levels of diverse packet requests may affect the system performance, such as the impact from the caching size limit on packet allocation. Thirdly, we derive the average energy consumption metric using the binomial point process based network model, which facilitates a comprehensive evaluation of the competition among UE groups. Finally, simulations validate our proposed analysis method, which may provide important design hints for improving existing D2D caching schemes.

In this paper, a novel visible light positioning (VLP) system is proposed. The new system includes multiple dual-mode light-emitting diode (DM-LED) lamps with different radiation lobe mode numbers (RLMN) and a user device equipped with a photodiode (PD). Specifically, a method utilizing a radiation angle measurement (RAM) approach for DM-LED is devised, and the characteristics of key system parameters are studied, taking into account multi-path reflections in practical indoor VLP scenarios. Furthermore, two localization algorithms based on linear and nonlinear least squares methods are developed. Simulation results show that the proposed system significantly outperforms its conventional counterpart in terms of positioning accuracy under the challenging multi-path scenario.

In this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general. Several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI, the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region.