CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks

Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra, effectively enhancing spectrum efficiency for Wi-Fi and other wireless systems. As an enabler for multi-band transmission, channel fingerprints (CF), also known as the channel knowledge map or radio environment map, are used to assist channel state information (CSI) acquisition and reduce computational complexity. In this paper, we propose CF-CGN (Channel Fingerprints with Cycle-consistent Generative Networks) to extrapolate CF for multi-band massive MIMO transmission where licensed and unlicensed spectra cooperate to provide ubiquitous connectivity. Specifically, we first model CF as a multichannel image and transform the extrapolation problem into an image translation task, which converts CF from one frequency to another by exploring the shared characteristics of statistical CSI in the beam domain. Then, paired generative networks are designed and coupled by variable-weight cycle consistency losses to fit the reciprocal relationship at different bands. Matched with the coupled networks, a joint training strategy is developed accordingly, supporting synchronous optimization of all trainable parameters. During the inference process, we also introduce a refining scheme to improve the extrapolation accuracy based on the resolution of CF. Numerical results illustrate that our proposed CF-CGN can achieve bidirectional extrapolation with an error of 5-17 dB lower than the benchmarks in different communication scenarios, demonstrating its excellent generalization ability. We further show that the sum rate performance assisted by CF-CGN-based CF is close to that with perfect CSI for multi-band massive MIMO transmission.

Channel Charting-assisted Non-orthogonal Pilot Allocation for Uplink XL-MIMO Transmission

Extremely large-scale multiple-input multiple-output (XL-MIMO) is critical to future wireless networks. The substantial increase in the number of base station (BS) antennas introduces near-field propagation effects in the wireless channels, complicating channel parameter estimation and increasing pilot overhead. Channel charting (CC) has emerged as a potent unsupervised technique to effectively harness varying high-dimensional channel statistics to enable non-orthogonal pilot assignment and reduce pilot overhead. In this paper, we investigate near-field channel estimation with reduced pilot overhead by developing a CC-assisted pilot scheduling. To this end, we introduce a polar-domain codebook to capture the power distribution of near-field XL-MIMO channels. The CC-assisted approach uses such features as inputs to enable an effective low-dimensional mapping of the inherent correlation patterns in near-field user terminal (UT) channels. Building upon the mapped channel correlations, we further propose a near-field CC-assisted pilot allocation (NCC-PA) algorithm, which efficiently enhances channel orthogonality among pilot-reusing UTs. Numerical results confirm that the NCC-PA algorithm substantially elevates the wireless transmission performance, offering a marked improvement over the conventional far-field CC-PA approach.

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.

Distributed satellite information networks: Architecture, enabling technologies, and trends

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

On the Spectral Efficiency of Multi-user Holographic MIMO Uplink Transmission

With antenna spacing much less than half a wavelength in confined space, holographic multiple-input multiple-output (HMIMO) technology presents a promising frontier in next-generation mobile communication. We delve into the research of the multi-user uplink transmission with both the base station and the users equipped with holographic planar arrays. To begin, we construct an HMIMO channel model utilizing electromagnetic field equations, accompanied by a colored noise model that accounts for both electromagnetic interference and hardware noise. Since this model is continuous, we approximate it within a finite-dimensional space spanned by Fourier space series, which can be defined as the communication mode functions. We show that this channel model samples Green's function in the wavenumber domain in different communication modes. Subsequently, we tackle the challenging task of maximizing the spectral efficiency (SE) of the system, which involves optimizing the continuous current density function (CDF) for each user. Using the aforementioned approximation model, we transform the optimization variables into expansion coefficients of the CDFs on a finite-dimensional space, for which we propose an iterative water-filling algorithm. Simulation results illustrate the efficacy of the proposed algorithm in enhancing the system SE and show the influence of the colored noise and the system parameters on the SE.

Ubiquitous Integrated Sensing and Communications for Massive MIMO LEO Satellite Systems

The next sixth generation (6G) networks are envisioned to integrate sensing and communications in a single system, thus greatly improving spectrum utilization and reducing hardware costs. Low earth orbit (LEO) satellite communications combined with massive multiple-input multiple-output (MIMO) technology holds significant promise in offering ubiquitous and seamless connectivity with high data rates. Existing integrated sensing and communications (ISAC) studies mainly focus on terrestrial systems, while operating ISAC in massive MIMO LEO satellite systems is promising to provide high-capacity communication and flexible sensing ubiquitously. In this paper, we first give an overview of LEO satellite systems and ISAC and consider adopting ISAC in the massive MIMO LEO satellite systems. Then, the recent research advances are presented. A discussion on related challenges and key enabling technologies follows. Finally, we point out some open issues and promising research directions.

Polar-Coded Tensor-Based Unsourced Random Access with Soft Decoding

The unsourced random access (URA) has emerged as a viable scheme for supporting the massive machine-type communications (mMTC) in the sixth generation (6G) wireless networks. Notably, the tensor-based URA (TURA), with its inherent tensor structure, stands out by simultaneously enhancing performance and reducing computational complexity for the multi-user separation, especially in mMTC networks with a large numer of active devices. However, current TURA scheme lacks the soft decoder, thus precluding the incorporation of existing advanced coding techniques. In order to fully explore the potential of the TURA, this paper investigates the Polarcoded TURA (PTURA) scheme and develops the corresponding iterative Bayesian receiver with feedback (IBR-FB). Specifically, in the IBR-FB, we propose the Grassmannian modulation-aided Bayesian tensor decomposition (GM-BTD) algorithm under the variational Bayesian learning (VBL) framework, which leverages the property of the Grassmannian modulation to facilitate the convergence of the VBL process, and has the ability to generate the required soft information without the knowledge of the number of active devices. Furthermore, based on the soft information produced by the GM-BTD, we design the soft Grassmannian demodulator in the IBR-FB. Extensive simulation results demonstrate that the proposed PTURA in conjunction with the IBR-FB surpasses the existing state-of-the-art unsourced random access scheme in terms of accuracy and computational complexity.

An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results show that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.

Joint Channel Estimation and Prediction for Massive MIMO with Frequency Hopping Sounding

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyper-parameter learning, we leverage the approximations of the off-grid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.

Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension of the precoders to be designed. By proving that the precoder set satisfying the per-BS power constraints forms a Riemannian submanifold of a linear product manifold, we transform the constrained precoder design problem in Euclidean space to an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices, which is beneficial in practice. The complexity analyses show the high computational efficiency of RCG precoder design. Simulation results demonstrate the numerical superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.