Channel Estimation for Movable Antenna Aided Wideband Communication Systems

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.

BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking

Hyperspectral object tracking (HOT) has exhibited potential in various applications, particularly in scenes where objects are camouflaged. Existing trackers can effectively retrieve objects via band regrouping because of the bias in existing HOT datasets, where most objects tend to have distinguishing visual appearances rather than spectral characteristics. This bias allows the tracker to directly use the visual features obtained from the false-color images generated by hyperspectral images without the need to extract spectral features. To tackle this bias, we find that the tracker should focus on the spectral information when object appearance is unreliable. Thus, we provide a new task called hyperspectral camouflaged object tracking (HCOT) and meticulously construct a large-scale HCOT dataset, termed BihoT, which consists of 41,912 hyperspectral images covering 49 video sequences. The dataset covers various artificial camouflage scenes where objects have similar appearances, diverse spectrums, and frequent occlusion, making it a very challenging dataset for HCOT. Besides, a simple but effective baseline model, named spectral prompt-based distractor-aware network (SPDAN), is proposed, comprising a spectral embedding network (SEN), a spectral prompt-based backbone network (SPBN), and a distractor-aware module (DAM). Specifically, the SEN extracts spectral-spatial features via 3-D and 2-D convolutions. Then, the SPBN fine-tunes powerful RGB trackers with spectral prompts and alleviates the insufficiency of training samples. Moreover, the DAM utilizes a novel statistic to capture the distractor caused by occlusion from objects and background. Extensive experiments demonstrate that our proposed SPDAN achieves state-of-the-art performance on the proposed BihoT and other HOT datasets.

Orbital-Angular-Momentum Versus MIMO: Orthogonality, Degree of Freedom,and Capacity

The plane wave based wireless communications have becoming more and more matured, along with the well utilization of the traditional resources such as time and frequency. To further increase the capacity for rapidly increasing capacity demand of wireless communications, it is potential to use the twist wave, which has the orbital angular momentum (OAM). In this paper, we discuss the OAM based wireless communications in the aspect of orthogonality, degree of freedom (DoF), and capacity, where both the transmitter and the receiver use uniform circular array (UCA) antennas. In particular, we compare OAM based wireless communications with multiple-input-multiple-output (MIMO) based wireless communications in terms of DoF and capacity. Numerical results are presented to validate and evaluate that the DoF of OAM based wireless communications is greater than or equal to that of correlated MIMO based wireless communications when the transmitter and the receiver antennas are aligned well. The OAM based wireless communications can achieve larger capacity than the correlated MIMO in high signal-to-noise ratio (SNR) region under line-of-sight scenario.

Fast Transceiver Design for RIS-Assisted MIMO mmWave Wireless Communications

Due to high bandwidth and small antenna size, millimeter-wave (mmWave) integrated line-of-sight (LOS) multiple-input-multiple-output (MIMO) systems have attracted much attention. Reconfigurable intelligent surfaces (RISs), which have the potential to change the characteristics of incident electromagnetic waves with low power cost, can improve the performance or the MIMO mmWave wireless communications. Uniform circular array (UCA) is an effective antenna structure with low complexity transceiver. In this paper, UCA based RIS-assisted MIMO mmWave wireless communications with transmit UCA, the RIS UCAs, and receive UCA are investigated. Since the rotation angles between the transceiver make the channel matrix noncirculant, an algorithm is developed to derive the ranges of the rotation angles based on an acceptable error and reduce the impact of rotation angles on channel matrix. Then, we propose a low-complexity precoding scheme at the transmitter, phase designs at the RIS UCAs, and a phase compensation scheme at the receiver, which can convert the channel matrix into an equivalent circulant channel matrix with a small error. Then, a fast symbol-wise maximum likelihood (ML) detection scheme is proposed to recover the signals with low computational complexity. Simulation results are presented to illustrate the theory.

Discrete Spectrum Analysis of Vector OFDM 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.

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.

A Note On the Clark Conjecture On Time-Warped Bandlimited Signals

In this note, a result of a previous paper on the Clark conjecture on time-warped bandlimited signals is extended to a more general class of the time warping functions, which includes most of the common functions in practice.

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.

A Simple Channel Independent Beamforming Scheme With Parallel Uniform Circular Array

In this letter, we consider a uniform circular array (UCA)-based line-of-sight multiple-input-multiple-output system, where the transmit and receive UCAs are parallel but non-coaxial with each other. We propose a simple channel-independent beamforming scheme with fast symbol-wise maximum likelihood detection.

Asynchronous MIMO-OFDM Massive Unsourced Random Access with Codeword Collisions

This paper investigates asynchronous MIMO massive unsourced random access in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.