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.

Multi-link operation (MLO) is regarded as one of the most disruptive features in the upcoming IEEE 802.11be standard, known as Wi-Fi 7. However, the performance characterization of heterogeneous multi-link IEEE 802.11be networks, which consist of Multi-Link Devices (MLDs) and legacy Single-Link Devices (SLDs), remains largely unknown. The challenge originates from the lack of proper modeling of multi-link channel access schemes. In this paper, a novel model is established to study the throughput optimization of heterogeneous two-link IEEE 802.11be networks. MLDs adopt one representative synchronous multi-link channel access scheme with the primary channel. Based on the proposed model, explicit expressions of throughput of MLDs and legacy SLDs are both characterized and verified by simulation results. The network throughput is further maximized by optimally choosing the transmission probabilities of SLDs and MLDs. The analysis shows that MLO can enable MLDs to achieve higher device throughput than SLDs, yet the maximum network throughput of heterogeneous networks decreases compared to homogeneous networks composed solely of MLDs or legacy SLDs.

How to effectively support massive access and data transmission in Internet of Things scenarios has been a long-standing and critical issue for various wireless communication networks. To address this issue, a flexible and efficient medium access control protocol is the key. In this paper, we propose Slotted Aloha with Successive Transmission (SAST) scheme, in which upon the successful transmission of the Head-of-Line (HoL) packet, the node delivers the remaining packets with probability 1 until the buffer is cleared or a collision occurs, thereby capitalizing on immediate channel availability. By formulating vacation queuing models of both node and channel, the access/data throughput and access/data delay are explicitly characterized and optimized by properly choosing the transmission probability of the HoL packet. Our analysis reveals that the maximum data throughput of SAST scheme is 0.5, higher than$e^{-1}$in classic slotted Aloha. The practical insights of the analysis are also demonstrated by taking the example of 2-step Small Data Transmission (SDT) random access in 5G. It is shown that the SAST scheme can be seamlessly implemented into 5G and the comparison with 2-step SDT random access reveals that SAST can improve the throughput performance while significantly reduce the signaling overhead, nearly halved in the saturated case and up to 70% reduction in the unsaturated case.

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.

In the IEEE 802.11ax Wireless Local Area Networks (WLANs), Orthogonal Frequency Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN amendment. However, with the growth of the number of devices, it is difficult for the Access Point (AP) to schedule uplink transmissions, which calls for an efficient access mechanism in the OFDMA uplink system. Based on Multi-Agent Proximal Policy Optimization (MAPPO), we propose a Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm to improve the throughput and guarantee the fairness. Motivated by the Mean-Field games (MFGs) theory, a novel global state and action design are proposed to ensure the convergence of MFMAPPO in the massive access scenario. The Multi-Critic Single-Policy (MCSP) architecture is deployed in the proposed MFMAPPO so that each agent can learn the optimal channel access strategy to improve the throughput while satisfying fairness requirement. Extensive simulation experiments are performed to show that the MFMAPPO algorithm 1) has low computational complexity that increases linearly with respect to the number of stations 2) achieves nearly optimal throughput and fairness performance in the massive access scenario, 3) can adapt to various diverse and dynamic traffic conditions without retraining, as well as the traffic condition different from training traffic.

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.

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.

Downlink mobile edge computing (MEC) networks are requiblack to serve increasing large number of Internet of Things (IoT) devices with limited battery capacity. In order to serve massive user equipments with low power consumption requirements, in this paper, we propose an energy-efficient multi-carrier non-orthogonal multiple access (MC-NOMA) design which allows more than two IoT devices to multiplex and access the same subcarrier band. With the aim to minimize the total transmit energy while meeting the demands of each IoT device such as the low latency, in our design, we first derive the optimal successive interference cancellation (SIC) policy and minimum power allocated to every IoT device. Then we propose an optimal greedy algorithm to allocate the frequency blocks, and formulate the optimization of the computational resource allocation as a min-max problem. Subsequently, we characterize the MC-NOMA network with the potential game model, and present a scheduling scheme to manage massive IoT devices. Simulation results demonstrate that our proposed scheme can consume 3-10 dB less energy in a MEC network deployed with 256 IoT devices compablack with the conventional orthogonal multiple access (OMA) scheme and non-orthogonal multiple access (NOMA) scheme.

Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on improving performance (e.g., communication efficiency, energy efficiency, and scalability) of centralized architectures, which include a number of distributed devices and a server. However, centralized architectures may cause congestion at the central node, which is not applicable under some circumstances. To tackle this issue, we introduce a decentralized edge learning framework over wireless networks via unreliable device-to-device (D2D) links and improve its learning performance. The unreliable transmission caused by the channel uncertainty has a negative effect on model convergence. To enhance the performance, we formulate an optimization problem to minimize the overall model deviation under a given latency requirement by jointly optimizing the broadcast data rate and bandwidth allocation. Then, the optimal solution of broadcast data rate is derived and an algorithm for obtaining the optimal bandwidth allocation is developed. Besides, we also propose a decentralized edge learning protocol without a central server and provide the convergence analysis. Finally, extensive simulations are conducted to demonstrate the performance advantages of our proposed algorithm compared against the baseline algorithm.