Joint Channel Estimation and Signal Detection for MIMO-OFDM: A Novel Data-Aided Approach with Reduced Computational Overhead

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

AI for CSI Prediction in 5G-Advanced and Beyond

Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.

AdapCsiNet: Environment-Adaptive CSI Feedback via Scene Graph-Aided Deep Learning

Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead, their generalization capability across varying propagation environments remains limited due to their data-driven nature. Existing solutions based on online training improve adaptability but impose significant overhead in terms of data collection and computational resources. In this work, we propose AdapCsiNet, an environment-adaptive DL-based CSI feedback framework that eliminates the need for online training. By integrating environmental information -- represented as a scene graph -- into a hypernetwork-guided CSI reconstruction process, AdapCsiNet dynamically adapts to diverse channel conditions. A two-step training strategy is introduced to ensure baseline reconstruction performance and effective environment-aware adaptation. Simulation results demonstrate that AdapCsiNet achieves up to 46.4% improvement in CSI reconstruction accuracy and matches the performance of online learning methods without incurring additional runtime overhead.

Sensing With Random Communication Signals

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission.

Integrated Communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/CRISense.

Hybrid Beamforming with Orthogonal delay-Doppler Division Multiplexing Modulation for Terahertz Sensing and Communication

The Terahertz band holds a promise to enable both super-accurate sensing and ultra-fast communication. However, challenges arise that severe Doppler effects call for a waveform with high Doppler robustness while severe propagation path loss urges for an ultra-massive multiple-input multiple-output (UM-MIMO) structure. To tackle these challenges, hybrid beamforming with orthogonal delay-Doppler multiplexing modulation (ODDM) is investigated in this paper. First, the integration of delay-Doppler waveform and MIMO is explored by deriving a hybrid beamforming-based UM-MIMO ODDM input-output relation. Then, a multi-dimension sensing algorithm on target azimuth angle, elevation angle, range and velocity is proposed, which features low complexity and high accuracy. Finally, a sensing-centric hybrid beamforming is proposed to design the sensing combiner by minimizing the Cram\'er-Rao lower bounds (CRLB) of angles. After that, the precoder that affects both communication and sensing is then designed to maximize the spectral efficiency. Numerical results show that the sensing accuracy of the proposed sensing algorithm is sufficiently close to CRLB. Moreover, the proposed hybrid beamforming design allows to achieve maximal spectral efficiency, millimeter-level range estimation accuracy, millidegree-level angle estimation accuracy and millimeter-per-second-level velocity estimation accuracy. Take-away lessons are two-fold. Combiner design is critical especially for sensing, which is commonly neglected in hybrid beamforming design for communication. Furthermore, the optimization problems for communication and sensing can be decoupled and solved independently, significantly reducing the computational complexity of the THz monostatic ISAC system.

Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection

With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardware, the detection task is sometimes deployed on remote computing devices or the cloud to execute complex algorithms, which brings massive data transmission overhead. In response, this paper proposes an optical flow-driven semantic communication framework for the stereo-vision 3D object detection task. The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy. Specifically, we develop an optical flow-driven module to jointly extract and recover semantics from the left and right images to reduce the loss of the left-right photometric alignment semantic information and improve the accuracy of depth inference. Then, we design a 2D semantic extraction module to identify and extract semantic meaning around the objects to enhance the transmission of semantic information in the key areas. Finally, a fusion network is used to fuse the recovered semantics, and reconstruct the stereo-vision images for 3D detection. Simulation results show that the proposed method improves the detection accuracy by nearly 70% and outperforms the traditional method, especially for the low signal-to-noise ratio regime.

Hybrid Beamforming Design for Bistatic Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities. The associated high costs can be mitigated by adopting a hybrid beamforming structure. However, the well-studied monostatic ISAC systems face challenges related to full-duplex operation. To address this issue, this paper focuses on a three-dimensional bistatic configuration that requires only half-duplex base stations. To intuitively evaluate the error bound of bistatic sensing using orthogonal frequency division multiplexing waveforms, we propose a positioning scheme that combines angle-of-arrival and time-of-arrival estimation, deriving the closed-form expression of the position error bound (PEB). Using this PEB, we develop two hybrid beamforming algorithms for joint waveform design, aimed at maximizing achievable spectral efficiency (SE) while ensuring a predefined PEB threshold. The first algorithm leverages a Riemannian trust-region approach, achieving superior performance in terms of global optima and convergence speed compared to conventional gradient-based methods, but with higher complexity. In contrast, the second algorithm, which employs orthogonal matching pursuit, offers a more computationally efficient solution, delivering reasonable SE while maintaining the PEB constraint. Numerical results are provided to validate the effectiveness of the proposed designs.

Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture

This paper proposes a correlation-based three-stage channel estimation strategy with low pilot overhead for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multi-user (MU) MIMO systems, in which both users and base station (BS) are equipped with a hybrid RF architecture. In Stage I, all users jointly transmit pilots and recover the uncompressed received signals to estimate the angle of arrival (AoA) at the BS using the discrete Fourier transform (DFT). Based on the observation that the overall cascaded MIMO channel can be decomposed into multiple sub-channels, the cascaded channel for a typical user is estimated in Stage II. Specifically, using the invariance of angles and the linear correlation of gains related to different cascaded subchannels, we use compressive sensing (CS), least squares (LS), and a one-dimensional search to estimate the Angles of Departure (AoDs), based on which the overall cascaded channel is obtained. In Stage III, the remaining users independently transmit pilots to estimate their individual cascaded channel with the same approach as in Stage II, which exploits the equivalent common RIS-BS channel obtained in Stage II to reduce the pilot overhead. In addition, the hybrid combining matrix and the RIS phase shift matrix are designed to reduce the noise power, thereby further improving the estimation performance. Simulation results demonstrate that the proposed algorithm can achieve high estimation accuracy especially when the number of antennas at the users is small, and reduce pilot overhead by more than five times compared with the existing benchmark approach.

Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.