Polarized 6D Movable Antenna for Wireless Communication: Channel Modeling and Optimization

In this paper, we propose a novel polarized six-dimensional movable antenna (P-6DMA) to enhance the performance of wireless communication cost-effectively. Specifically, the P-6DMA enables polarforming by adaptively tuning the antenna's polarization electrically as well as controls the antenna's rotation mechanically, thereby exploiting both polarization and spatial diversity to reconfigure wireless channels for improving communication performance. First, we model the P-6DMA channel in terms of transceiver antenna polarforming vectors and antenna rotations. We then propose a new two-timescale transmission protocol to maximize the weighted sum-rate for a P-6DMA-enhanced multiuser system. Specifically, antenna rotations at the base station (BS) are first optimized based on the statistical channel state information (CSI) of all users, which varies at a much slower rate compared to their instantaneous CSI. Then, transceiver polarforming vectors are designed to cater to the instantaneous CSI under the optimized BS antennas' rotations. Under the polarforming phase shift and amplitude constraints, a new polarforming and rotation joint design problem is efficiently addressed by a low-complexity algorithm based on penalty dual decomposition, where the polarforming coefficients are updated in parallel to reduce computational time. Simulation results demonstrate the significant performance advantages of polarforming, antenna rotation, and their joint design in comparison with various benchmarks without polarforming or antenna rotation adaptation.

Robust Secure Communications in Near-Field ISCAP Systems with Extremely Large-Scale Antenna Array

This paper investigates robust secure communications in a near-field integrated sensing, communication, and powering (ISCAP) system, in which the base station (BS) is equipped with an extremely large-scale antenna array (ELAA). In this system, the BS transmits confidential messages to a single legitimate communication user (CU), simultaneously providing wireless power transfer to multiple energy receivers (ERs) and performing point target sensing. We consider a scenario in which both the ERs and the sensing target may act as potential eavesdroppers attempting to intercept the confidential messages. To safeguard secure communication, the BS employs a joint beamforming design by transmitting information beams combined with dedicated triple-purpose beams serving as energy and sensing signals, as well as artificial noise (AN) for effectively jamming potential eavesdroppers. It is assumed that only coarse location information of the ERs and sensing targets or eavesdroppers is available at the BS, leading to imperfect channel state information (CSI). Under this setup, we formulate a robust beamforming optimization problem with the objective of maximizing the secrecy rate for the CU, while ensuring worst-case performance requirements on both target sensing and wireless energy harvesting at the ERs. To address the non-convex robust joint beamforming problem and facilitate the deployment of a low-complexity algorithm, we employ the S-procedure alongside an eavesdropping CSI error-bound determination method to acquire a high-quality solution.

Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information

This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels. Leveraging the channel's sparsity in the joint AD domain, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimate. Harnessing the powerful implicit prior learning capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel. Specifically, we introduce the preliminary estimated channel as side information, and derive the evidence lower bound (ELBO) of the log-marginal distribution of the target NF channel conditioned on the preliminary estimated channel, which serves as the optimization objective for the proposed generative diffusion model (GDM). Additionally, we introduce a more generalized version of the GDM, the non-Markovian GDM (NM-GDM), to accelerate the sampling process, achieving an approximately tenfold enhancement in sampling efficiency. Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE compared to existing benchmark schemes within NF XL-MIMO systems. Furthermore, our approach exhibits enhanced generalization capabilities in both the NF or far-field (FF) regions.

Dynamic Precoding for Near-Field Secure Communications: Implementation and Performance Analysis

The increase in antenna apertures and transmission frequencies in next-generation wireless networks is catalyzing advancements in near-field communications (NFC). In this paper, we investigate secure transmission in near-field multi-user multiple-input single-output (MU-MISO) scenarios. Specifically, with the advent of extremely large-scale antenna arrays (ELAA) applied in the NFC regime, the spatial degrees of freedom in the channel matrix are significantly enhanced. This creates an expanded null space that can be exploited for designing secure communication schemes. Motivated by this observation, we propose a near-field dynamic hybrid beamforming architecture incorporating artificial noise, which effectively disrupts eavesdroppers at any undesired positions, even in the absence of their channel state information (CSI). Furthermore, we comprehensively analyze the dynamic precoder's performance in terms of the average signal-to-interference-plus-noise ratio, achievable rate, secrecy capacity, secrecy outage probability, and the size of the secrecy zone. In contrast to far-field secure transmission techniques that only enhance security in the angular dimension, the proposed algorithm exploits the unique properties of spherical wave characteristics in NFC to achieve secure transmission in both the angular and distance dimensions. Remarkably, the proposed algorithm is applicable to arbitrary modulation types and array configurations. Numerical results demonstrate that the proposed method achieves approximately 20\% higher rate capacity compared to zero-forcing and the weighted minimum mean squared error precoders.

Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization

Navigating autonomous vehicles in open scenarios is a challenge due to the difficulties in handling unseen objects. Existing solutions either rely on small models that struggle with generalization or large models that are resource-intensive. While collaboration between the two offers a promising solution, the key challenge is deciding when and how to engage the large model. To address this issue, this paper proposes opportunistic collaborative planning (OCP), which seamlessly integrates efficient local models with powerful cloud models through two key innovations. First, we propose large vision model guided model predictive control (LVM-MPC), which leverages the cloud for LVM perception and decision making. The cloud output serves as a global guidance for a local MPC, thereby forming a closed-loop perception-to-control system. Second, to determine the best timing for large model query and service, we propose collaboration timing optimization (CTO), including object detection confidence thresholding (ODCT) and cloud forward simulation (CFS), to decide when to seek cloud assistance and when to offer cloud service. Extensive experiments show that the proposed OCP outperforms existing methods in terms of both navigation time and success rate.

Green Robotic Mixed Reality with Gaussian Splatting

Realizing green communication in robotic mixed reality (RoboMR) systems presents a challenge, due to the necessity of uploading high-resolution images at high frequencies through wireless channels. This paper proposes Gaussian splatting (GS) RoboMR (GSRMR), which achieves a lower energy consumption and makes a concrete step towards green RoboMR. The crux to GSRMR is to build a GS model which enables the simulator to opportunistically render a photo-realistic view from the robot's pose, thereby reducing the need for excessive image uploads. Since the GS model may involve discrepancies compared to the actual environments, a GS cross-layer optimization (GSCLO) framework is further proposed, which jointly optimizes content switching (i.e., deciding whether to upload image or not) and power allocation across different frames. The GSCLO problem is solved by an accelerated penalty optimization (APO) algorithm. Experiments demonstrate that the proposed GSRMR reduces the communication energy by over 10x compared with RoboMR. Furthermore, the proposed GSRMR with APO outperforms extensive baseline schemes, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Complexity-Scalable Near-Optimal Transceiver Design for Massive MIMO-BICM Systems

Future wireless networks are envisioned to employ multiple-input multiple-output (MIMO) transmissions with large array sizes, and therefore, the adoption of complexity-scalable transceiver becomes important. In this paper, we propose a novel complexity-scalable transceiver design for MIMO systems exploiting bit-interleaved coded modulation (termed MIMO-BICM systems). The proposed scheme leverages the channel bidiagonalization decomposition (CBD), based on which an optimization framework for the precoder and post-processor is developed for maximizing the mutual information (MI) with finite-alphabet inputs. Particularly, we unveil that the desired precoder and post-processor behave distinctively with respect to the operating signal-to-noise ratio (SNR), where the equivalent channel condition number (ECCN) serves as an effective indicator for the overall achievable rate performance. Specifically, at low SNRs, diagonal transmission with a large ECCN is advantageous, while at high SNRs, uniform subchannel gains with a small ECCN are preferred. This allows us to further propose a low-complexity generalized parallel CBD design (GP-CBD) based on Givens rotation according to a well-approximated closed-form performance metric on the achievable rates that takes into account the insights from the ECCN. Numerical results validate the superior performance of the proposed scheme in terms of achievable rate and bit error rate (BER), compared to state-of-the-art designs across various modulation and coding schemes (MCSs).

Sum-Rate Maximization for Pinching Antenna-assisted NOMA Systems with Multiple Dielectric Waveguides

This paper investigates the resource allocation design for a pinching antenna (PA)-assisted multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system featuring multiple dielectric waveguides. To enhance model accuracy, we propose a novel frequency-dependent power attenuation model for dielectric waveguides in PA-assisted systems. By jointly optimizing the precoder vector and the PA placement, we aim to maximize the system's sum-rate while accounting for the power attenuation across dielectric waveguides. The design is formulated as a non-convex optimization problem. To effectively address the problem at hand, we introduce an alternating optimization-based algorithm to obtain a suboptimal solution in polynomial time. Our results demonstrate that the proposed PA-assisted system not only significantly outperforms the conventional system but also surpasses a naive PA-assisted system that disregards power attenuation. The performance gain compared to the naive PA-assisted system becomes more pronounced at high carrier frequencies, emphasizing the importance of considering power attenuation in system design.

Multi-Cell Coordinated Beamforming for Integrate Communication and Multi-TMT Localization

This paper investigates integrated localization and communication in a multi-cell system and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the Cramer-Rao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)-based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plus-noise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.

GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications

Massive multiple-input multiple-output (MIMO) offers significant advantages in spectral and energy efficiencies, positioning it as a cornerstone technology of fifth-generation (5G) wireless communication systems and a promising solution for the burgeoning data demands anticipated in sixth-generation (6G) networks. In recent years, with the continuous advancement of artificial intelligence (AI), a multitude of task-oriented generative foundation models (GFMs) have emerged, achieving remarkable performance in various fields such as computer vision (CV), natural language processing (NLP), and autonomous driving. As a pioneering force, these models are driving the paradigm shift in AI towards generative AI (GenAI). Among them, the generative diffusion model (GDM), as one of state-of-the-art families of generative models, demonstrates an exceptional capability to learn implicit prior knowledge and robust generalization capabilities, thereby enhancing its versatility and effectiveness across diverse applications. In this paper, we delve into the potential applications of GDM in massive MIMO communications. Specifically, we first provide an overview of massive MIMO communication, the framework of GFMs, and the working mechanism of GDM. Following this, we discuss recent research advancements in the field and present a case study of near-field channel estimation based on GDM, demonstrating its promising potential for facilitating efficient ultra-dimensional channel statement information (CSI) acquisition in the context of massive MIMO communications. Finally, we highlight several pressing challenges in future mobile communications and identify promising research directions surrounding GDM.