The evolution of 6G has brought distributed multi-input multi-output (D-MIMO) near-field communications into the spotlight, owing to their potential to significantly enhance wireless capacity and spectral efficiency. This study delves into exploring the optimal characteristics of distributed antenna deployment in D-MIMO systems through the optimization of access point (AP) placement and subarray topology. To guarantee the stability of system configuration, we derive an approximate ergodic sum rate with high accuracy under near-field spherical wavefront propagation that serves as the optimization criterion. Confronting the non-convex challenges, we adopt an approach integrating successive convex approximation (SCA), gradient descent, and alternating optimization methods, which enables us to attain a near-optimal solution with low complexity. Numerical simulations highlight the significant superiority of our proposal to traditional baseline approaches, revealing key characteristics and providing crucial insights for AP deployment and array layout optimization. In densely clustered user scenarios, APs should be deployed near the user aggregation centroid, which may not align with the geometric center. For dispersed user distributions, AP deployment positions should be adjusted more flexibly to regional statistical characteristics rather than confined to central points to accommodate specific user distribution features. Moreover, the array topology exhibits a pattern of alternating dense and sparse distribution, diverging from the attributes observed in centralized MIMO (C-MIMO) architectures, where a consistent medium density is flanked by regions of sparsity.

Prompt engineering for large language models is challenging, as even small prompt perturbations or model changes can significantly impact the generated output texts.Existing evaluation methods of LLM outputs, either automated metrics or human evaluation, have limitations, such as providing limited insights or being labor-intensive.We propose Spotlight, a new approach that combines both automation and human analysis.Based on data mining techniques, we automatically distinguish between random (decoding) variations and systematic differences in language model outputs.This process provides token patterns that describe the systematic differences and guide the user in manually analyzing the effects of their prompts and changes in models efficiently.We create three benchmarks to quantitatively test the reliability of token pattern extraction methods and demonstrate that our approach provides new insights into established prompt data.From a human-centric perspective, through demonstration studies and a user study, we show that our token pattern approach helps users understand the systematic differences of language model outputs.We are further able to discover relevant differences caused by prompt and model changes (e.g.related to gender or culture), thus supporting the prompt engineering process and human-centric model behavior research.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) offers a promising solution to conquer the challenge of severe signal attenuation through buildings for seamless indoor-outdoor connectivity in 6G networks. This paper focuses on hybrid active-passive STAR-RIS assisted indoor-outdoor communications and delves into the comparative impact of diverse non-ideal factors on system performance, such as transceiver hardware impairments, imperfect channel state information (CSI), and STAR-RIS phase noise. Under these system imperfections, our objective is to maximize the minimum user rate by optimizing user grouping, non-orthogonal multiple access (NOMA) decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming, thereby enhancing system robustness and user fairness. Given the complexity of the formulated problem, characterized by interwoven optimization variables, we propose a collaborative convex optimization iterative (CCOI) algorithm by blending successive convex approximation (SCA), semidefinite relaxation (SDR), Gaussian randomization, and convex upper bound approximation. This methodology is capable of adeptly striking a balance between system performance and computational efficiency. Simulation results demonstrate the significant advantages of our proposed CCOI algorithm and provides guiding insights on system design for various defects. Additionally, the hybrid active-passive STAR-RIS structure offers enhanced flexibility and effectively balances the performance trade-offs between conventional active and passive models.

Achieving high-precision interference recognition for autonomous aerial vehicle (AAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of AAV swarm communication. Although deep learning-based interference recognition methods for AAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labeled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this article proposes a data-knowledge-driven method for AAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed AAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments.

In satellite synthetic aperture positioning (SAP), the curvature of the Earth’s surface and the curved orbit lead to nonlinear and asymmetric instantaneous Doppler frequencies, especially when dealing with signals of very long durations. This phenomenon significantly affects the accuracy of center frequency estimation and radiating source positioning. This study presents an ultra-high-resolution positioning algorithm designed to process ultra-long-duration data collected by a single satellite. Initially, a method for estimating the zero-Doppler moment based on sub-aperture chirp rates is proposed to obtain an unbiased estimate of the radiation source’s center frequency. Subsequently, a nonlinear instantaneous Doppler compensation method is proposed, utilizing the estimated center frequency and chirp rates to enhance the coherence of the long-duration data. Furthermore, a long coherent positioning is suggested to generate an ultra-high-resolution positioning image. Ultimately, the efficacy of the proposed algorithm is validated through simulations and acquired data.

Reconfigurable intelligent Surface (RIS) are often proposed as a means for restoring non-line-of-sight (NLoS) links in wireless communication. In this paper, we propose a joint design strategy for multi-RIS deployment locations and passive beamforming in multi-RIS assisted multi-BS networks with blockages. To ensure a stable network topology and low feedback overhead, the ergodic capacity is analyzed using Jensen’s inequality and the binomial expansion theorem, and then maximized as a performance metric. Taking into account the minimum spacing constraint for multi-RIS in real-world scenarios, we optimize the multi-RIS deployment locations using a genetic algorithm, where a Markov chain model can be applied to obtain the global optimal solution. Additionally, we implement the passive beamforming design for multi-RIS using the Riemannian conjugate gradient (RCG) method based on manifold space. Numerical results demonstrate the superiority of our proposed deployment strategy over the benchmark deployment schemes.

For partially-connected massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) communications, an iterative precoding and combining algorithm is proposed to maximize the system spectrum efficiency. At the transmitter, the optimal unconstrained full digital precoder is firstly determined as a benchmark by singular value decomposition (SVD) of the channel matrix, and then resolved to achieve the hybrid analog and digital precoding in an iterative manner. Specifically, the initially formulated non-convex matrix decomposition problem is decomposed into a series of convex optimization sub-problems, with the amplitude of each element in the analog matrix restricted and the phase increment in adjacent iteration processes varies only in a small range. Moreover, with the unconstrained linear minimum mean square error (MMSE) combiner as the benchmark, similar iterations can be executed at the receiver. Numerical results show that the proposed algorithm acquires superior spectrum efficiency than the other classical scheme with partially-connected architecture. Furthermore, in spite of much lower hardware complexity and power consumption, the performance of our algorithm is almost identical to that of the full digital scheme as well as other mechanisms with fully-connected structure.

As a special bistatic configuration, space-missile borne bistatic synthetic aperture radar (SMB-BiSAR) is proposed, which has many potential applications in precision detection and guidance. Compared with traditional bistatic SAR, SMB bistatic SAR has complicated structure, and the imaging performance should be analyzed. In this paper, the geometric model of SMB-BiSAR is constructed in the reference target local (RTL) coordinate system firstly. Then, the range history is introduced with the vector notation. According to the range history, the Doppler properties and 2-D resolution are derived by using gradient method, and the forward-looking ability is demonstrated, which would guide the subsequent research. The simulations verify the validity of the analysis method.

For the design and performance evaluation of potential massive multiple input and multiple output (MIMO) related algorithms, accurate channel models are indispensable. Spherical wavefront and array non-stationarity due to physically large antenna array are two new characteristics specific to massive MIMO propagation. In this paper, spherical wavefront effects can be well characterized by a predefined two-dimensional multi-confocal ellipse scattering geometry as in reference [1] and a 9- state Markov process is developed to capture the channel behaviors by array non-stationarity. Meanwhile, the associated approach to derive the state transition probabilities of the Markov process based on the outfield measurements is detailed. Finally, simulation results verify the validity of our proposal.