This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.

This paper proposes a movable-antenna-based index modulation (MA-IM) framework that exploits the spatial mobility of a single reconfigurable antenna to create additional information-bearing dimensions for next-generation wireless systems. By discretizing the continuous movable region into a dense set of candidate sampling points and selecting representative anchors for indexing, the proposed framework converts spatial degrees of freedom into a practical modulation resource. Building on this framework, we develop a family of anchor-selection strategies with different levels of channel awareness, including geometry-based, SNR-based, max--min channel-domain, and joint constellation-aware designs. For the resulting MA-IM schemes, joint maximum-likelihood (ML) detectors are derived, along with a low-complexity two-stage detector, and unified analytical upper bounds on the average bit error probability (ABEP) are established based on the joint index--modulation constellation. The results reveal that directly indexing all sampling points is generally unreliable, highlighting the necessity of anchor optimization. The performance of MA-IM is shown to depend on key system parameters, including channel richness, spatial correlation, the number of index states, and the modulation order. In particular, increasing the number of index states and increasing the QAM order affect MA-IM in fundamentally different ways, even under the same transmission rate. Among the proposed schemes, the joint constellation-aware anchor design achieves the best error performance, demonstrating that optimizing channel-domain separation alone is insufficient and that effective MA-IM design must account for the geometry of the joint signal constellation. Simulation results further show that, with properly designed anchors, MA-IM can approach or even outperform same-spectral-efficiency QAM baselines.

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segmented into multiple parts, forming a sequence of sub-pulses. Each sub-pulse can adopt a different carrier frequency, leading to frequency hops between sub-pulses. This design is termed frequency index modulation (FIM), enabling the embedding of communication information into different carrier frequencies for transmission. To further enhance the data transmission rate at the communication end, this paper incorporates quadrature amplitude modulation (QAM) into waveform design. The paper derives the ambiguity function of the proposed waveform and analyzes its Doppler and range resolution, establishing upper and lower bounds for the range resolution. In processing SAR signals, the receiver first removes QAM symbols, and to address phase discontinuities between sub-pulses, a phase compensation algorithm is proposed to achieve coherent processing. For the communication receiver, the user first performs de-chirp processing and then demodulates QAM symbols and FIM index symbols using a two-step maximum likelihood (ML) algorithm. Numerical simulations further confirm the theoretical validity of the proposed approach.

For next-generation green communication systems, this article proposes an innovative communication system based on frequency-diverse array-multiple-input multiple-output (FDA-MIMO) technology, which aims to achieve high data rates while maintaining low power consumption. This system utilizes frequency offset index realign modulation, multiple-antenna spatial index modulation, and spreading code index modulation techniques. In the proposed generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation (GCIM-FORMASM) system, the coming bits are divided into five parts: spatial modulation bits by activating multiple transmit antennas, frequency offset index bits of the FDA antennas, including frequency offset combination bits and frequency offset realign bits, spreading code index modulation bits, and modulated symbol bits. Subsequently, this paper utilizes the orthogonal waveforms transmitted by the FDA to design the corresponding transmitter and receiver structures and provide specific expressions for the received signals. Meanwhile, to reduce the decoding complexity of the maximum likelihood (ML) algorithm, we propose a three-stage despreading-based low complexity (DBLC) algorithm leveraging the orthogonality of the spreading codes. Additionally, a closed-form expression for the upper bound of the average bit error probability (ABEP) of the DBLC algorithm has been derived. Analyzing metrics such as energy efficiency and data rate shows that the proposed system features low power consumption and high data transmission rates, which aligns better with the concept of future green communications. The effectiveness of our proposed methods has been validated through comprehensive numerical results.

This paper studies the problem of adaptive distributed target detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the target is embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model establishes distributed target detection as a summation expression, which is different from the traditional detection models in MIMO or phase array radars that discuss only point-like target. Next, according to the rules of generalized likelihood ratio tests (GLRT), Rao and Wald tests, we designed three adaptive detectors without the training data. The numerical results validate the proposed method and all theoretical analyses.

This paper investigates the adaptive target detection problem in FDA-MIMO radar embedded in Gaussian noise with an unknown covariance matrix, considering the scenario of mismatched steering vectors. Firstly, we assume that the covariance matrix of the test data and training data has the same structure with different magnitude levels. Next, the subspace model is adopted to enhance the robustness of the detector against mismatched signals. Furthermore, TS-GLRT detector is designed based on the generalized likelihood ratio test (GLRT), then we validated its constant false alarm rate (CFAR) property. Finally, simulation results demonstrate the effectiveness of the proposed approach.

Satisfiability Modulo Theories (SMT) based network configuration verification tools are powerful tools in preventing network configuration errors. However, their fundamental limitation is efficiency, because they rely on generic SMT solvers to solve SMT problems, which are in general NP-complete. In this paper, we show that by leveraging network domain knowledge, we can substantially accelerate SMT-based network configuration verification. Our key insights are: given a network configuration verification formula, network domain knowledge can (1) guide the search of solutions to the formula by avoiding unnecessary search spaces; and (2) help simplify the formula, reducing the problem scale. We leverage these insights to design a new SMT- based network configuration verification tool called NetSMT. Extensive evaluation using real-world topologies and synthetic network configurations shows that NetSMT achieves orders of magnitude improvements compared to state-of-the-art methods.

Considering that frequency diverse array multiple-input multiple-output (FDA-MIMO) possesses extra range information to enhance sensing performance, this paper explores the FDA-MIMO-based integrated sensing and communication (ISAC) system. To reinforce the system communication capability, we propose the frequency offsets permutation index modulation (FOPIM) scheme, which conveys extra information bits by selecting and permutating frequency offsets from a frequency offsets pool. For the system communication function, considering the fact that the traditional maximum likelihood detection method suffers from high complexity and bit error rate (BER), the maximum likelihood-based two-stage detection (MLTSD) approach is presented to overcome this issue. For the system sensing function, we employ the two-step maximum likelihood estimator (TSMLE) to stepwise estimate the angle and range of the interested target. Furthermore, we derive the closed-form expressions for the tight upper bound on the communication BER, along with the sensing Cramér-Rao bound (CRB). The simulation results validate the theoretical analysis, demonstrating that the proposed system exhibits lower BER and superior range resolution than independent MIMO communication and sensing modules.

For multiple-input multiple-output (MIMO) frequency diverse array (FDA) communications, this paper proposes a frequency offsets index modulation (FOIM) scheme, which conveys extra information by selecting transmitting frequency offsets from a frequency offsets pool. To improve system spectral efficiency, we firstly design an orthogonal baseband waveform for FDA with the exact expression, then the corresponding receiver structure is proposed. Further, considering that the traditional maximum likelihood (ML) detection algorithm suffers from high complexity, an output combined maximum likelihood (OCML) approach is presented. Moreover, the closed-form expressions for the upper bound on bit error rates (BERs) of both ML and OCML methods are derived, as well as the counterpart of the system capacity. The simulation results show that the capacity of the proposed FOIM scheme outperforms the MIMO scheme, and meanwhile, our method achieves a higher communication rate when compared with the quadrature spatial modulation (QSM) scheme. Additionally, the proposed OCML algorithm can lead the BER performance of FOIM to be superior to that of the aforementioned approaches with significantly lower computational complexity.

Reproducing research results is important for the networking community. The current best practice typically resorts to: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; or (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private ones are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). We first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report our observations and lessons and discuss future open research questions of this proposal.

In this paper, we design the physical layer security of a dual-hop spatial modulation system provided the relay node transmits its own private information. Meanwhile, the quadrature virtual channel spatial modulation (QVCSM) is proposed to enhance the physical layer security in the second hop. To achieve system security in both angle and range dimensions, the frequency diverse array (FDA) is used in the relay node with a decode-and-forward (DF) protocol. Moreover, the baseband directional modulation (DM) and artificial noise (AN) are jointly utilized to suppress the received signal at the passive eavesdropper. Additionally, the Ergodic secrecy rate of the proposed system is analyzed, whereas the closed-form expressions for tight upper bounds on the bit error rates (BERs) of the source and relay are derived for both the destination and eavesdropper. The simulation results show that the proposed FDA relay design outperforms the traditional phase array (PA) relay system in terms of the system secrecy rate. In contrast to the quadrature spatial modulation (QSM) method, the proposed designs achieve an improved suppression performance of the eavesdropper.

Differs from conventional phased-array, frequency diverse array (FDA) employs a frequency increment across the array elements and thus produces a range-angle-dependent beampattern. This additional range dependence finds applications in target detection/localization, high-resolution synthetic aperture radar (SAR) imaging, and SAR deceptive jamming. To capture the essence behind those applications, we analyze the range, angle, and Doppler resolution capabilities of FDA radar signal via ambiguity function. As conventional ambiguity function is defined for a single-input single-output (SISO) signal, we extend it to the generalized ambiguity function for FDA radar signal, parameterized with the range, angle, and Doppler shifts of a target. Both theoretical analysis and numerical results show that the FDA radar with a large frequency increment has potentials in high-resolution ranging but will suffer from additional range ambiguity within the range bin and Doppler ambiguity. Moreover, the use of frequency increment brings FDA radar a virtual transmit aperture but decreases the directional gain.

In this letter, we present an adaptive Bayesian detection framework for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The targets are detected in Gaussian clutter with unknown but stochastic covariance matrix. We designed two detectors in the Bayesian framework, namely, Bayesian Rao (BRao) detector and Bayesian Wald (BWald) detector without training data. Numerical results reveal that the proposed detectors outperform conventional non-Bayesian counterparts. It is necessary to note that the BWald detector requires higher computational complexity than the BRao detector.

In this letter, we propose a frequency diverse array multiple-input multiple-output radar detection architecture for distributed targets embedded in Gaussian interference with unknown but stochastic covariance matrix. At the design stage, we model distributed targets within one range cell as a linear combination of several contributions and assume that the interference covariance matrix obeys the inverse complex Wishart distribution. Then, we devise an adaptive decision rule by jointly exploiting the maximum likelihood approach and the Bayesian framework. Unlike existing contributions in this context, the proposed detector does not require the conventional set of training data to estimate the interference covariance matrix. The numerical examples validate the effectiveness of the proposed method also in comparison with suitable counterparts.

Spaceborne bistatic synthetic aperture radar (BiSAR) system with geosynchronous-earth-orbit (GEO) transmitter and a low-earth-orbit (LEO) receiver has many advantages which are fine resolution, higher signal-to-noise (SNR), shortest revisit cycle. However, BiSAR data are often azimuth variant because of the sum of two hyperbolic range equation which bring technical challenges to image focus. We address an improved nonlinear chirp scaling (NLCS) algorithm in this paper. The key is introduced a cubic perturbation function in the azimuth domain and used an modified NLCS algorithm to complete azimuth compression, the method of series of reversion (MSR) to obtain an accurate signal spectrum. Simulation results shows the effective of our proposed method.

In this letter, we propose a scattered-wave deceptive jamming approach using a frequency diverse array (FDA) radar against spaceborne synthetic aperture radars (SARs). As an emerging array radar, FDA transmits multiple carrier frequencies across its elements, and thus, a multiple deceptive jamming is potentially produced in the range dimension of the SAR image. Moreover, we consider the scattered-wave jamming, since it has low complexity when used to create spurious scenes and control the jammer locations. The theoretical analysis and numerical simulations show that the scattered-wave jamming with FDA can generate multiple fake targets with the number of targets being dependent on the number of FDA carrier frequencies.

Frequency diverse array (FDA) is an emerging array technique, its essential array characteristics and corresponding potential applications have not been well studied. As array manifold plays an essential role in various array applications, this letter presents an FDA manifold sensitivity framework to analyze the impact of element position uncertainties. Both absolute sensitivity and relative sensitivity are derived for the FDA. The manifold sensitivity analysis is suited for identifying the important position of each array element in an FDA antenna. Moreover, the overall sensitivity can be used to evaluate the robustness of an FDA configuration. All analytical manifold sensitivity characteristics are verified by numerical results.

A uniform linear motion is generally assumed in traditional synthetic aperture radar(SAR) processing algorithms. However, it is inevitable that if there is 3-D acceleration, the image formulation processing complexity will be significantly increased, especially in highly squinted situations. This paper aims to handle the imaging problems in the highly squinted SAR with three-dimensional acceleration, which utilizes the Taylor formula to expand the distance equation and further resolve it. Based on the formulated range formula, we further utilize the two- dimensional non-uniform Fast Fourier Transform (2D-NUFFT) to obtain focused SAR imagery. The proposed methods are verified with simulation results.