Satellite communications have been considered a key part of global connectivity, effectively supporting diverse applications such as the Internet of Things (IoT) and real-time communication services. However, security-sensitive devices face significant challenges due to the threat of eavesdropping satellites, which compromise data confidentiality. Existing approaches often rely on deterministic models and fail to account for the stochastic nature of eavesdropping threats and the dynamic demands of satellite networks, limiting their applicability in practical scenarios. To address these challenges, this work proposes a novel secure access strategy for satellite mobile edge computing (SatMEC) systems, integrating a stochastic risk assessment model and an evolutionary game-theoretic framework. The proposed solution leverages a probabilistic model to evaluate the spatial distribution of eavesdropping satellites, quantifies the eavesdropping risk via the concept of eavesdropping capacity, and incorporates a dynamic service selection strategy that balances secrecy capacity and queuing delay.Furthermore, a distributed algorithm is developed to enable IoT devices to select service satellites based on real-time utility optimization adaptively. Extensive simulation experiments validate the effectiveness of the proposed strategy, demonstrating its ability to improve system security, balance the network load, and enhance overall performance in large-scale and dynamic satellite network environments. The results highlight the reliability and scalability of the proposed solution, making it a practical approach for secure and efficient access in LEO satellite networks.

Guided by the principle of “Never Trust, Always Verify”, Zero Trust Architecture (ZTA) mandates continuous monitoring and analysis of users and entities, highlighting the critical role of behavior analytics. However, the growing volume of audit data and its complex contextual information render many existing behavior analytics methods insufficient. Moreover, most approaches rely on high-quality labeled data for supervised training, limiting their effectiveness against previously unseen malicious behaviors. To address these challenges, we propose the Large Language Model for Behavior Analytics (LLMBA) framework. LLMBA leverages a Large Language Model (LLM) to analyze behavioral patterns of internal users and entities, capitalizing on the LLM’s strong ability to model sequential data. We introduce a multi-level behavior encoding scheme to capture both contextual and temporal information from behavior records, producing rich input representations for the LLM-enhanced model. The LLM is fine-tuned using self-supervised learning, enabling the detection of unknown malicious behaviors. To reduce the computational and storage overhead inherent in LLMs, we apply knowledge distillation to compress the model while maintaining high detection performance. Extensive experiments on the CERT Insider Threat dataset demonstrate that LLMBA outperforms state-of-the-art baselines in detection accuracy. Furthermore, the compressed student model achieves superior performance compared with existing methods under comparable runtime constraints, making LLMBA highly suitable for real-world deployment.

The low-altitude economy (LAE) is inherently dependent on unmanned aerial vehicles (UAVs) as its core operational infrastructure, with applications spanning logistics, surveillance, and smart city development. Despite growing attention, LAE's practical deployment faces substantial obstacles, particularly in maintaining reliable UAV operations. These UAVs require secure and efficient wireless communication services delivered by base stations (BSs), yet their high mobility, combined with multipath effects, creates significant channel estimation challenges that threaten the seamless connectivity. According to the fixed airspace and planned routes characteristics of LAE scenarios, in this paper we construct the radio map to assist channel estimation using sensing information. First, a grid-based UAV channel measurement scheme is proposed to collect CSI data labeled with discrete locations and velocities. Then, a new generative adversarial network (GAN)-based model named continuous vector-conditioned GAN (CVCGAN) is developed to complete the discrete map by establishing the mapping from continuous sensing information to their channel space. After that, an integrator is designed to fuse the channel state information (CSI) provided by radio map with that estimated by pilots. Simulation results validate the superiority of the proposed method, which outperforms state-of-the-art (SOTA) approaches including ChannelNet, conditional GAN (CGAN), RadioUNet and long short-term memory (LSTM).

Orthogonal time frequency space (OTFS)/vector OFDM (VOFDM) is widely regarded as a promising waveform for next-generation mobile communications. However, its spectral characteristics are not yet fully understood. The bandwidth allocation scheme, which is crucial for OTFS’s integration into practical wireless standards, also remains unexplored. In this paper, we investigate the spectral characteristics of OTFS signals by analyzing their power spectral density (PSD). We demonstrate that the PSD of discrete-time OTFS signals is periodic with a period of 1/MTs, whereMis the size of the time/Doppler domain in OTFS, a.k.a., the vector size in VOFDM, andTsis the sampling interval length of digital to analog converter (DAC), resulting in M identical spectral components within the spectral range [− 1/2Ts, 1/2Ts) of the continuous-time OTFS signal. The periodicity makes bandwidth allocation for OTFS/VOFDM signaling substantially challenging. Furthermore, we establish a relationship between the PSD of OFDM signals and that of OTFS signals, revealing that, when the information symbols are independent, the PSD of OTFS signals is equal to the sum of the PSDs of the component-expanded OFDM (CEP-OFDM) signals. Lastly, we derive a relationship between the information symbols and the corresponding OTFS spectrum, and based on which, we propose a null-space-based linear precoding (NSLP) method for OTFS signals to enable flexible bandwidth allocation. Numerical results validate our analytical results regarding the PSD of OTFS signals and show the effectiveness of our proposed NSLP method in tailoring the spectrum of OTFS signals.

Vector OFDM (VOFDM) is equivalent to OTFS and is good for time-varying channels. However, due to its vector form, its signal spectrum is not as clear as that of the conventional OFDM. In this paper, we study the discrete spectrum of discrete VOFDM signals. We obtain a linear relationship between a vector of information symbols and a vector of the same size of components evenly distributed in the discrete VOFDM signal spectrum, and show that if a vector of information symbols is set to 0, then a corresponding vector of the same size of the discrete VOFDM signal spectrum is 0 as well, where the components of the 0 vector are not together but evenly distributed in the spectrum. With the linear relationship, the information symbol vectors can be locally precoded so that any of the discrete spectrum of VOFDM signals can be set to 0, similar to that of the conventional OFDM signals. These results are verified by simulations.

Radio map is a promising technology that connects the user equipment (UE) location and its channel state information (CSI). By applying a radio map, the beamforming vector can be generated based solely on the location of the UE, thereby significantly saving pilot effort. However, the effectiveness of radio map-based beamforming is influenced by several factors, such as positioning errors and channel dynamics. To improve the adaptability and performance of radio map-based beamforming, in this paper we consider integrating location information with reduced pilots and examine the trade-off between pilot overhead and beamforming performance. In particular, an end-to-end method for joint extrapolation, denoising and performing beamforming with reduced pilots is proposed. Subsequently, the beamforming vector obtained from reduced pilots is integrated with that generated by the radio map. Considering the overhead caused by pilots, the support vector machine (SVM) is applied to determine whether it is worth introducing pilots for integration. According to the numerical simulations, the proposed end-to-end method with reduced pilots is superior to that with full pilots in terms of spectral efficiency (SE) and can be improved by integrating with the radio map. In addition, the application of SVM can effectively identify the integration needs, further reducing unnecessary pilot effort and thereby increasing SE.

It is crucial to automatically detect anomalous patterns in system logs to protect computer systems from cyber attacks and malfunctions. However, as log data is becoming increasingly complex and labeled logs are difficult to obtain, it poses serious challenges to existing methods. To this end, this paper introduces the pre-training and fine-tuning paradigm to the log analysis domain and proposes a novel log anomaly detection framework. We propose the masked log reconstruction approach to pre-train a Transformer-based foundation model and fine-tune it for the event prediction task to obtain the anomaly detector. Our training methods exploit the sequential information within unlabeled logs with self-supervised learning. Experimental results on two public datasets demonstrate the performance superiority of our framework compared with existing state-of-the-art methods. More importantly, it is suitable for real-world scenarios where labeled logs are difficult to acquire.

Reflecting intelligent surface (RIS) has been widely used to enhance radio signals in wireless communications. However, RIS’s capabilities, other than signal enhancement, are rarely investigated, which restricts RIS’s role in assisting wireless communications. In this article, we propose to apply RIS for joint signal enhancement and collision alleviation to the contention-based wireless access standards, e.g., IEEE 802.11 wireless local area network (WLAN). Inspired by the capture effect’s capability in collision alleviation, we design an opportunistic passive beamforming (OPBF) scheme that artificially introduces channel fluctuations to manage the capture effect. We realize the OPBF scheme through three steps, i.e., reflection pattern design, random index generation, and index-guided pattern switching. In addition, we develop a hardware prototype for validations in WiFi networks. The experiment results show that our scheme can effectively reduce collision rate and improve the system throughput.

The large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and inter-ring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.

Current intelligent reflecting surface (IRS) configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and then apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.

Port scanning is an extensively used technique by attackers to probe for vulnerabilities in network systems. Since fast port scans can be effectively detected by many existing methods, some advanced attackers perform slow port scans in order not to be suspected. A highly stealthy slow scan can last for dozens of days, which brings significant challenges to current intrusion detection approaches. Besides, the existing port scan detection methods are all based on full traffic. They are not suitable for high-speed networks because of huge computational and storage resource consumption. According to the protocol characteristics and the connection patterns of port scans, we construct a traffic feature set that can not only distinguish the specific scan types, but also remain effective for the sampled traffic. Furthermore, we customize a data structure Scan Detection Sketch (SDS) for feature extraction. Experimental results using public datasets show that our method can detect slow port scans in a 10Gbps high-speed network with high accuracy and acceptable memory consumption. And the proposed method works well even for slow port scans lasting more than 60 days.

Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from “adapting to wireless channels” to “changing wireless channels”. However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create the smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.

Jittering effects significantly degrade the performance of UAV millimeter-wave (mmWave) communications. To investigate the impacts of UAV jitter on mmWave communications, we firstly model UAV mmWave channel based on the geometric relationship between element antennas of the uniform planar arrays (UPAs). Then, we extract the relationship between (I) UAV attitude angles & position coordinates and (II) angle of arrival (AoA) & angle of departure (AoD) of mmWave channel, and we also derive the distribution of AoA/AoD at UAV side from the random fluctuations of UAV attitude angles, i.e., UAV jitter. In beam training design, with the relationship between attitude angles and AoA/AoD, we propose to generate a rough estimate of AoA and AoD from UAV navigation information. Finally, with the rough AoA/AoD estimate, we develop a compressed sensing (CS) based beam training scheme with constrained sensing range as the fine AoA/AoD estimation. Particularly, we construct a partially random sensing matrix to narrow down the sensing range of CS-based beam training. Numerical results show that our proposed UAV beam training scheme assisted by navigation information can achieve better accuracy with reduced training length in AoA/AoD estimation and is thus more suitable for UAV mmWave communications under jittering effects.

Intelligent reflecting surface (IRS) offers a cost-effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training designs for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to estimate angle of arrival (AoA) and angle of departure (AoD) of the line of sight (LoS) path between BS/AP (or IRSs) and MT. Then, with the estimated AoDs, we propose an iterative positioning algorithm that achieves centimeter-level positioning accuracy. The obtained location information is not only a fringe benefit but also enables us to cross verify and enhance the estimation of AoA and AoD, and it also facilitates the estimation of blockage indicator. Numerical results show the superiority of our proposed beam training scheme and verify the performance gain brought by location information.

Spatial modulation is an efficient transmission scheme for RF-chain limited MIMO systems. In this paper, we design a beam-switching based spatial modulation for mmWave MIMO uplink systems. At user side, a power iteration based algorithm is proposed to generate spatial symbols under the constant modulus constraint. With the generated spatial codebook, the optimal transmission mode is determined. At base station side, a round-robin path selection algorithm is proposed to select paths that are separated in angle of arrival. Inter-user interference is then suppressed by constraining the power of the received spatial symbols over selected paths only. Finally, ordered successive interference cancelation is applied to further reduce interference among users. Numerical results show that the proposed beam-switching based spatial modulation is superior to transmission over the strongest propagation path and is able to yield a satisfactory error performance in mmWave MIMO uplink.

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation cannot be directly applied to multi-panel MIMO. In this paper, we study channel estimation for multi-panel mmWave MIMO. We firstly model channel estimation of multi-panel MIMO as a block-sparse signal recovery problem. Then, according to maximum likelihood criterion, we propose an orthogonal projection method to firstly detect the support of the sparse channel response vector and then perform least square estimation, based on which we also propose a low-complexity greedy support detection in order to reduce computational complexity. Finally, through analyzing pairwise error probability of support detection, we verify the performance gain of joint multi-panel channel estimation over single panel channel estimation, and we further find that joint multi-panel channel estimation with independently generated random combining matrices outperforms that with identically generated combining matrices. Numerical results verify the superiority of our proposed joint multi-panel orthogonal projection based channel estimation over existing schemes.

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation and hybrid precoding cannot be directly applied to multipanel MIMO. In this paper, we study channel estimation and hybrid precoding for multi-panel MIMO. We first transform the channel response vector into angular domain and then reconstruct channel state information (CSI) by using the estimated angular CSI. Moreover, by exploiting the sparse nature of mmWave channels, a sparsity-based channel estimation method is proposed to reduce training overhead and the computational complexity of hybrid precoding. Numerical results show that the proposed channel estimation and hybrid precoding scheme achieve satisfactory spectral efficiency performance with greatly reduced training overhead and low computational complexity.

In this paper, we study transmit codebook designs for spatial modulation with analog phase shifters. The proposed spatial modulation with analog phase shifters can send more spatial symbols than conventional spatial modulation, while reserving the low complexity of single RF-chain MIMO structure. The main idea is to transmit Ntdifferent phase-shifted copies of a digitally modulated signal through Nttransmit antennas. In each time slot, the transmitted information bits are divided into two groups. The first group of the bits is mapped to the digitally modulated signal symbol. The second group of the bits is mapped to the spatial symbol, which determines the phase shift of each antenna. The transmit signal designs are carried out in open loop and closed loop scenarios, respectively. In open loop scenario, the transmit codebook is designed in two steps, i.e., generate the optimal spatial codebook according to Grassmannian line packing criterion, and find the optimal tradeoff between the cardinality of spatial codebook and the modulation order of signal symbol. In the closed loop scenario, the transmit codebook is designed according to the structure of Huffman tree. Extensive simulation results are given to validate the effectiveness of the proposed scheme in open loop scenario and close loop scenario, respectively.

In this paper, optimal signal constellation for downlink 2-user NOMA is investigated from both information theoretical and system level perspectives. Specifically, utilizing the gradient relationship between mutual information and minimum mean squared error, we first propose the optimal superposition coded constellation that maximizes the sum weighted mutual information. In order to verify the effectiveness of the proposed design, we then implement the proposed design in a low-density parity-check code (LDPC) coded modulated NOMA system. Numerical results demonstrate that, in the considered system, the promised achievable rate of our optimized superposition coded constellation can be achieved with a negligible gap.

In this paper, antenna subset selection for downlink millimeter wave (mmWave) massive MIMO systems is studied. A low complexity method is proposed to select antenna subset on a uniform planar array (UPA) in base station. The antenna subset selection is reduced to find the optimal positions of the sub-arrays on the UPA. Numerical results show that our proposed antenna subset selections with two or four sub-arrays are superior to transmission using all antennas when the number of users is two in high SNR regime.

In this paper, we propose an analog phase shifter aided spatial modulation scheme. The proposed scheme is able to send more spatial symbols than conventional spatial modulation (SM), while reserving the low complexity of single RF-chain MIMO structure. The main idea is to transmit Ntdifferent phase-shifted copies of the digitally modulated signal through Ntantennas. In each time slot, the transmitted information bits are divided into two groups. The first group of the bits are mapped to the analog phase modulated symbol. The second group of the bits are mapped to the spatial symbol, which determines the phase shift of each antenna. We further study the optimization of transmit codebook in two steps, i.e., generate the optimal spatial codebook according to Grassmannian line packing criterion, and find the optimal tradeoff between the size of signal codebook and the size of spatial codebook. Theoretical and numerical studies show that the proposed scheme has better performance than conventional SM with the same number of transmit antennas.

Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

Antenna switch enables multiple antennas to share a common RF chain, thus an additional spatial dimension, i.e., antenna index, can be utilized in the design of single RF chain MIMO and information can be conveyed via both signal space and spatial dimension. In this paper, we propose a unified adaptive transmission scheme - adaptive spatial modulation that allocates information into signal space and spatial dimension in order to maximize the overall channel capacity for single RF chain MIMO. The proposed adaptive spatial modulation is realized by using Huffman coding, i.e., designing variable length codes to activate the transmit antenna with different probabilities. The optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations for channel capacity. Numerical results show that the proposed adaptive spatial modulation offers considerable performance improvement over both spatial modulation and transmit antenna selection.

As Network Function Virtualization (NFV) becomes reality and cloud computing offers a scalable pay-as-you-go charging model, more network operators would like to outsource their Service Function Chains (SFC) to the public clouds in order to reduce the operational cost. However, how to minimize the operational cost with Quality of Service (QoS) guarantee when outsourcing SFC is still an open problem. In this paper, we are to study this problem when there are large number of candidate cloud providers with diverse pricing schemes of network functions. In addition, extra delay is introduced as the result of outsourcing SFCs. Firstly, we formulate this problem as an Integer Linear Programming (ILP) model. Then we design an efficient heuristic algorithm named QoS-Guaranteed SFC Outsourcing algorithm (QGSO) based on Hidden Markov Model (HMM). The extensive simulations show that QGSO saves up to 75.8% cost compared with that of deploying network functions in local network. QGSO also achieves up to 42.6% cost savings compared with the result of first-fit based optimization algorithm.

Content-Centric Networking (CCN) is a novel networking paradigm that treats the named contents, not the hosts, as the first-class citizens of the network. In the forwarding plane, CCN employs a stateful forwarding scheme, which maintains per-packet state information in Pending Interest Table (PIT). By employing stateful forwarding, CCN enables native support for content requests aggregation and multicast. However, the stateful forwarding scheme requires large-sized PITs with extremely high access speed to store per-packet state information, leading to scalability issue. To overcome the issue, this paper proposes a Hybrid forwarding scheme based on content POPularity (HyPOP) for CCN. HyPOP classifies the contents into popular and unpopular contents, and uses the stateful and Bloom Filter based stateless forwarding schemes to forward the popular and unpopular contents, respectively. The mathematical analysis results demonstrate that if PITs only store state information for popular contents, the small-sized PITs are sufficient for achieving satisfactory forwarding performance. Furthermore, the extensive simulation results also verify that HyPOP can reduce the size of PIT significantly and achieve promising forwarding performance.

In this paper, we study the max–min Euclidean distance-based multiple-input multiple-output (MIMO) transmit codebook design with channel state information at transmitter. Different from existing max–min precoder designs, a shaping-based codebook design method is proposed to construct the codebook from a structured signal lattice. Hyperrectangle shaping is found to be superior to the shaping region of the precoder-based design in terms of power efficiency. Based on the proposed hyper-rectangle shaping region, the number of available spatial dimensions used for codebook construction is first determined through maximizing the approximated constellation figure of merit. Then, the codebook is derived by lattice precoding and shifting the unshaped latticed points inside the hyperrectangle region. Numerical results illustrate that our proposed shaping-based codebook is strictly better than present precoder-based codebook designs and is more robust under different MIMO channel conditions.

In this paper, we propose diagonal precoder designs of spatial modulation (SM) by minimizing symbol error rate (SER) upper bound. A general precoder design method applicable for any signal-to-noise ratio (SNR) is first proposed. We then carry out asymptotic analysis of the precoder design in the regimes of high and low SNR. In high SNR regime, our proposed precoder design problem is found to be a non-convex quadratically constrained quadratic program (QCQP) problem that can be solved by semi-definite relaxation (SDR) technique. In low SNR regime, we find the closed form expression of the optimal solution that is equivalent to best antenna selection. Simulation results illustrate that our proposed algorithm can significantly enhance the SER performance and has a better performance than other algorithms.