Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7-24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNext, a deep learning framework designed to predict 7 GHz channel impulse responses (CIRs) by leveraging abundant 3.5 GHz CIRs. The framework integrates an RT-based dataset pipeline with attention U-Net (AU-Net) variants for gain and phase prediction. The proposed AU-Net-Aux model achieves a median gain error of 0.58 dB and a phase prediction error of 0.27 rad on unseen complex environments. Furthermore, we extend CIR-UNext into a foundation model, Channel2ComMap, for throughput prediction in MIMO-OFDM systems, demonstrating superior performance compared with existing approaches. Overall, CIR-UNext provides an efficient and scalable solution for cross-band prediction, enabling applications such as localization, beam management, digital twins, and intelligent resource allocation in 6G networks.

Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models’ strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt-enabled large AI model (LAM) for CSI feedback. The LAM employs powerful Transformer blocks and is trained on extensive datasets from various scenarios. Meanwhile, the channel distribution (environmental knowledge), represented as the mean of channel magnitude in the angular-delay domain, is incorporated as a scenario-specific prompt within the decoder to further enhance reconstruction quality. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.

Integrated sensing and communication (ISAC) is a key feature of future cellular systems, enabling applications such as intruder detection, monitoring, and tracking using the same infrastructure. However, its potential for structural health monitoring (SHM), which requires the detection of slow and subtle structural changes, remains largely unexplored due to challenges such as multipath interference and the need for ultra-high sensing precision. This study introduces a novel theoretical framework for SHM via ISAC by leveraging reconfigurable intelligent surfaces (RIS) as reference points in collaboration with base stations and users. By dynamically adjusting RIS phases to generate distinct radio signals that suppress background multipath interference, measurement accuracy at these reference points is enhanced. We theoretically analyze RIS-aided collaborative sensing in three-dimensional cellular networks using Fisher information theory, demonstrating how increasing observation time, incorporating additional receivers (even with self-positioning errors), optimizing RIS phases, and refining collaborative node selection can reduce the position error bound to meet SHM’s stringent accuracy requirements. Furthermore, we develop a Bayesian inference model to identify structural states and validate damage detection probabilities. Both theoretical and numerical analyses confirm ISAC’s capability for millimeter-level deformation detection, highlighting its potential for high-precision SHM applications.

This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

Channel State Information (CSI) provides a detailed description of the wireless channel and has been widely adopted for Wi-Fi sensing, particularly for high-precision indoor positioning. However, complete CSI is rarely available in real-world deployments due to hardware constraints and the high communication overhead required for feedback. Moreover, existing positioning models lack mechanisms to detect when users move outside their trained regions, leading to unreliable estimates in dynamic environments. In this paper, we present FPNet, a unified deep learning framework that jointly addresses channel feedback compression, accurate indoor positioning, and robust anomaly detection (AD). FPNet leverages the beamforming feedback matrix (BFM), a compressed CSI representation natively supported by IEEE 802.11ac/ax/be protocols, to minimize feedback overhead while preserving critical positioning features. To enhance reliability, we integrate ADBlock, a lightweight AD module trained on normal BFM samples, which identifies out-of-distribution scenarios when users exit predefined spatial regions. Experimental results using standard 2.4 GHz Wi-Fi hardware show that FPNet achieves positioning accuracy above 97% with only 100 feedback bits, boosts net throughput by up to 22.92%, and attains AD accuracy over 99% with a false alarm rate below 1.5%. These results demonstrate FPNet’s ability to deliver efficient, accurate, and reliable indoor positioning on commodity Wi-Fi devices.

Mid-band extra-large-scale multiple-input multiple-output (XL-MIMO), emerging as a critical enabler for future communication systems, is expected to deliver significantly higher throughput by leveraging the extended bandwidth and enlarged antenna aperture. However, power consumption remains a significant concern due to the expanded system dimension, underscoring the need for thorough investigations into efficient system design and deployment. To this end, an in-depth study is conducted on mid-band XL-MIMO systems. Specifically, a comprehensive power consumption model is proposed, encompassing the power consumption of major hardware components and signal processing procedures, while capturing the influence of key system parameters. Considering typical near-field propagation characteristics, closed-form approximations of throughput are derived, providing an analytical framework for assessing energy efficiency (EE). Based on the proposed framework, the scaling law of EE with respect to key system configurations is derived, offering valuable insights for system design. Subsequently, extensions and comparisons are conducted among representative multi-antenna technologies, demonstrating the superiority of mid-band XL-MIMO in EE. Extensive numerical results not only verify the tightness of the throughput analysis but also validate the EE evaluations, unveiling the potential of energy-efficient mid-band XL-MIMO systems.

Mid-band extra large-scale multiple-input multiple-output (XL-MIMO) systems are considered a key enabler for future wireless communications, offering enhanced throughput and extended coverage. Combined with subarray-based architecture and distributed signal processing, the computational complexity and implementation overhead are reduced. However, uncertain channel characteristics associated with the novel frequency band present significant bottlenecks, hindering the development of hardware architecture and algorithm design. Meanwhile, channel characteristics across distributed processing units remain insufficiently explored. In response, a mid-band channel sounder is constructed, and extensive measurement campaigns are carried out across various typical scenarios. Initially, mid-band channel characteristics are unveiled and compared across different scenarios. Subsequently, the mid-band XL-MIMO channel characteristics are analyzed using a virtual array comprising 256 array antennas and 64 transceiver chains. Moreover, motivated by the potential of distributed processing, mid-band XL-MIMO channel characteristics are particularly investigated from the perspectives of subarrays and sub-bands, encompassing subarray-wise non-stationarities, consistencies, far-field approximations, and sub-band characteristics. Through the combination of analysis and measurement validation, several insights and benefits are revealed, particularly relevant to distributed architecture and processing, which provides practical guidance for the real-world deployment of mid-band XL-MIMO systems.

Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.

Reconfigurable Intelligent Surfaces (RIS) are an emerging technology that holds significant promise for customizing wireless channels to meet specific communication requirements. Accurate channel state information (CSI) is essential for fully realizing the potential of RIS. However, due to the passive nature of RIS and the large number of reflecting elements, acquiring CSI for the base station (BS)-RIS-user equipment (UE) link presents considerable challenges. In this paper, we propose a deep learning (DL)-based framework for downlink CSI acquisition. Specifically, we introduce a novel DL-based channel estimation framework, termed PPNet, which facilitates efficient pilot transmission. The key innovation of PPNet lies in the joint design and optimization of pilot signals from the BS and phase shifts from the RIS, both represented through neural networks, alongside the channel estimation module. By capturing environment-specific features with neural networks, PPNet enables more efficient utilization of pilot power. Furthermore, we propose a beamforming-oriented CSI acquisition framework, RIS-E2ENet, which jointly optimizes the entire CSI acquisition process, including channel estimation, CSI feedback, and active/passive beamforming design, to enhance CSI acquisition efficiency. To adapt to the dynamic nature of real-world environments, RIS-E2ENet incorporates a lightweight UE-side neural network design, enabling low-overhead online training. Extensive evaluations show that the proposed frameworks improve spectral efficiency by 58.55%, while maintaining minimal pilot and feedback overhead.

The increasing demand for services such as live streaming and virtual reality places significant pressure on wireless communication systems. Enhancing system performance or reducing bandwidth consumption is critical for delivering high-quality video experiences. Semantic communication, which focuses on the transmission of meaning, offers a promising solution. However, existing approaches are often limited to single scenarios, rely on simple channels, lack adaptability to dynamic wireless environments, and remain untested in practical air interfaces. To address these challenges, we propose a foundation model-aided universal video semantic communication framework designed for pixel-wise reconstruction across diverse scenarios. This framework enables the transmission of entire videos using joint source-channel coding (JSCC) based on optical flow estimation and leverages multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) for efficient semantic delivery in 3rd generation partnership project (3GPP) standard channels. In scenarios requiring full transmission for regions of interest and selective transmission for other areas, the framework employs a foundation model for segmentation, followed by JSCC and delivery. Furthermore, we introduce a channel condition number-adaptive semantic remapping method based on an attention mechanism to mitigate the effects of wireless fading. To validate our approach, we implement the framework on a testbed and develop two online demonstrations. Simulations and over-the-air experiments confirm significant improvements in video quality and substantial reductions in bandwidth overhead compared to existing methods.

Integrated Sensing and Communication (ISAC) has the potential to enhance both energy and spectral efficiency in modern communication systems. Although Probability Hypothesis Density (PHD)-based Simultaneous Localization and Mapping (SLAM) is a key algorithm for positioning and environmental mapping in ISAC, the advantages of multi-base-station (multi-BS) fusion remain underexplored, despite the considerable attention given to multi-sensor and multi-user data fusion in existing research. This paper leverages the distinct roles of Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) channel parameters, employing LoS for agent localization and NLoS for environment mapping. An Extended Kalman Filter (EKF) framework is proposed to fuse LoS path angle parameters for localization, for which the corresponding Cramér-Rao Lower Bound (CRLB) is derived. To facilitate landmark mapping, a virtual reference point (VRP) is introduced to model reflecting surfaces consistently across base stations (BSs), replacing the conventional approach of using multiple virtual anchors for multiple BSs. Furthermore, map fusion algorithms are developed to address the challenges of merging PHD-SLAM maps with varying observation quality and overlapping fields of view. To reduce the computational complexity of particle-based PHD-SLAM, agent location estimates derived from EKF fusion are used as priors, significantly improving particle efficiency and enabling the unified exploitation of LoS and NLoS data for comprehensive situational awareness. Simulation and experimental results confirm that the proposed EKF-based LoS fusion strategy significantly improves sensing performance while maintaining low computational overhead.

To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user’s position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while the propagation environments between different base stations and a specific user vary and their respective channels are uncorrelated, the user’s position remains constant and unique across all channels. Building on this, the proposed PCEnet framework leverages the position as a bridge between channels to establish a mapping between the characteristics of different channels, thereby using one acquired channel to assist in the estimation and feedback of others. Specifically, this approach first utilizes neural networks (NNs) to infer the user’s position from the obtained channel. The estimated position, shared among BSs through a central processing unit (CPU), is then fed into an NN to design pilot symbols and concatenated with the feedback information to the channel reconstruction NN to reconstruct other channels, thereby significantly enhancing channel acquisition performance. Additionally, we propose a simplified strategy where only the estimated position is used in the reconstruction process without modifying the pilot design, thereby reducing latency. Furthermore, we introduce a position label-free approach that infers the relative user position instead of the absolute position, eliminating the need for ground truth position labels during the localization NN training. Simulation results demonstrate that the proposed PCEnet framework reduces pilot and feedback overheads by up to 50%.

Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager.

The low-altitude economy is emerging as a key driver of future economic growth, necessitating effective flight activity surveillance using existing mobile cellular network sensing capabilities. However, traditional monostatic and localization-based sensing methods face challenges in fusing sensing results and matching channel parameters. To address these challenges, we model low-altitude surveillance as a compressed sensing (CS)-based imaging problem by leveraging the cooperation of multiple base stations and the inherent sparsity of aerial images. Additionally, we derive the point spread function to analyze the influences of different antenna, subcarrier, and resolution settings on the imaging performance. Given the random spatial distribution of unmanned aerial vehicles (UAVs), we propose a physics-embedded learning method to mitigate off-grid errors in traditional CS-based approaches. Furthermore, to enhance rare UAV detection in vast low-altitude airspace, we integrate an online hard example mining scheme into the loss function design, enabling the network to adaptively focus on samples with significant discrepancies from the ground truth during training. Simulation results demonstrate the effectiveness of the proposed low-altitude surveillance framework. The proposed physics-embedded learning algorithm achieves a 97.55% detection rate, significantly outperforming traditional CS-based methods under off-grid conditions. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/LAEImager.

The Channel Quality Indicator (CQI) is a fundamental component of channel state information (CSI) that enables adaptive modulation and coding by selecting the optimal modulation and coding scheme to meet a target block error rate. While AI-enabled CSI feedback has achieved significant advances, especially in precoding matrix index feedback, AI-based CQI feedback remains underexplored. Conventional subband-based CQI approaches, due to coarse granularity, often fail to capture fine frequency-selective variations and thus lead to suboptimal resource allocation. In this paper, we propose an AI-driven subcarrier-level CQI feedback framework tailored for 6G and NextG systems. First, we introduce CQInet, an autoencoder-based scheme that compresses per-subcarrier CQI at the user equipment and reconstructs it at the base station, significantly reducing feedback overhead without compromising CQI accuracy. Simulation results show that CQInet increases the effective data rate by 7.6% relative to traditional subband CQI under equivalent feedback overhead. Building on this, we develop SR-CQInet, which leverages super-resolution to infer fine-grained subcarrier CQI from sparsely reported CSI reference signals (CSI-RS). SR-CQInet reduces CSI-RS overhead to 3.5% of CQInet's requirements while maintaining comparable throughput. These results demonstrate that AI-driven subcarrier-level CQI feedback can substantially enhance spectral efficiency and reliability in future wireless networks.

Reconfigurable intelligent surfaces (RISs) enhance communication performance by adjusting the propagation directions of incident signals. However, joint beamforming design requires the acquisition of channel state information, often leading to significant pilot overhead in RIS-assisted systems, particularly when the number of reflective elements is large. In this study, we analyze the characteristics of the cascaded channel and propose a method that combines amplitude correlation with existing structured sparsity. Leveraging these characteristics, we first derive an on-grid channel estimation method, demonstrating the effectiveness of incorporating additional characteristics in cascaded channel estimation. We then extend the proposed algorithm to off-grid channel estimation by refining the coarsely estimated channel using alternating optimization and gradient descent. Furthermore, we adapt the algorithm to enhance estimation accuracy with the support of digital twin (DT) technology, utilizing a few pilots to refine the channel generated by DT. Simulation results show up to a 5 dB improvement in normalized mean squared error compared to state-of-the-art channel estimation algorithms that employ structured sparsity. Additionally, with DT assistance, the proposed algorithm achieves nearly a two-fold performance improvement over traditional algorithms that do not incorporate amplitude correlation and structured sparsity.

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, signal detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to improve robustness against discrepancies between the time-varying channel conditions encountered during training and those experienced during testing. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.

Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7–24 GHz). Realizing this vision faces two challenges. First, physics-based ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a data-driven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages low-frequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

The mid-band frequency range, combined with extra-large-scale antenna arrays (ELAA), is emerging as a critical enabler for future communication systems. However, deploying mid-band ELAA systems presents significant challenges due to the high complexity and overhead associated with signal processing tasks such as channel state information (CSI) acquisition. This paper introduces an efficient transmission framework that incorporates a distributed hardware architecture, distributed channel modeling, and a dual time-scale transmission protocol. Building upon this framework, a practical implementation is proposed, leveraging the discrete Fourier transform (DFT)-based radio frequency (RF) front-ends and linear receivers as the hardware foundation. Additionally, a novel transmission strategy is developed, exploiting both statistical and instantaneous CSI. The proposed framework includes approximations of the ergodic spectral efficiency (SE) to guide DFT beam selection based on statistical CSI. Furthermore, two user scheduling strategies are introduced, utilizing statistical CSI and location information, respectively, with angular division implemented in a distributed manner. Reduced-dimensional instantaneous CSI is then employed for both local and centralized processing. To support system design, the proposed transmission strategy’s ergodic SE performance is analyzed, focusing on the DFT RF front-end and the eigenvalue characteristics of channel correlation matrices. Numerical results reveal that the proposed framework and transmission strategy achieve SE comparable to fully-digital architectures, while significantly reducing overhead and complexity.

Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs—capable of error correction in language tasks—to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction.

Reconfigurable Intelligent Surface (RIS) is a promising technology with the potential to tailor wireless channels to specific communication needs. In RIS-assisted communications, channel estimation has long been a challenge due to the passive nature and the large number of RIS elements. In this paper, we introduce a novel deep learning-based downlink channel estimation framework, named PPNet, which facilitates efficient pilot transmission. The core innovation of PPNet lies in the joint design and optimization of pilot signals from the base station and phase shifts from the RIS, both of which are represented using neural networks, together with the channel estimation module. Simulation results show that the proposed framework significantly improves the estimation performance with limited pilot overhead.

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.

Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.

Wi-Fi sensing has gained substantial attention in recent years, particularly for indoor positioning applications. Conventional indoor wireless positioning methods typically assume access to complete channel state information (CSI), which is often impractical in real-world systems. This paper proposes a novel approach for indoor positioning utilizing a compressed beamforming feedback matrix (BFM), which is inherently integrated into the Wi-Fi protocol for meeting CSI feedback requirements. We introduce FPNet, a joint neural network model, in which the BFM is compressed into codewords by an encoder at the client station (STA) side. These codewords are subsequently transmitted to the access point (AP) side, which features a decoder and a positioning network responsible for reconstructing the codewords and estimating positions. The encoder and decoder are trained end-to-end. FPNet is implemented with standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental results demonstrate that this approach not only improves net throughput by up to 22.92% but also achieves positioning accuracy exceeding 97%.

The low-altitude economy has emerged as a critical focus for future economic development, emphasizing the urgent need for flight activity surveillance utilizing the existing sensing capabilities of mobile cellular networks. Traditional monostatic or localization-based sensing methods, however, encounter challenges in fusing sensing results and matching channel parameters. To address these challenges, we propose an innovative approach that directly draws the radio images of the low-altitude space, leveraging its inherent sparsity with compressed sensing (CS)based algorithms and the cooperation of multiple base stations. Furthermore, recognizing that unmanned aerial vehicles (UAVs) are randomly distributed in space, we introduce a physicsembedded learning method to overcome off-grid issues inherent in CS-based models. Additionally, an online hard example mining method is incorporated into the design of the loss function, enabling the network to adaptively concentrate on the samples bearing significant discrepancy with the ground truth, thereby enhancing its ability to detect the rare UAVs within the expansive low-altitude space. Simulation results demonstrate the effectiveness of the imaging-based low-altitude surveillance approach, with the proposed physics-embedded learning algorithm significantly outperforming traditional CS-based methods under off-grid conditions.

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for enhancing wireless coverage and transmission rates while reducing hardware costs and power consumption. This work addresses the limitations of separately optimizing RIS deployment and beamforming by proposing a unified joint deployment and beamforming framework tailored for multi-user multi-input single-output systems. By formulating RIS control as a Markov decision process, we develop a deep reinforcement learning framework that integrates a graph neural network to exploit the inherent topology of wireless communication networks. To reduce the action space and improve learning efficiency, the framework leverages discrete Fourier transform codebooks. Simulation results demonstrate that the proposed approach achieves up to a twofold improvement in weighted sum rate compared to fixed RIS deployment strategies, all while eliminating the need for explicit cascaded channel estimation and accurate channel model.

The superimposed pilot (SIP) transmission scheme shows great potential for improving spectral efficiency in MIMO-OFDM systems. However, it also introduces complex challenges for receiver design, particularly due to pilot contamination and data interference. To address these issues, the joint channel estimation, signal detection, and decoding (JCDD) framework has emerged as a promising solution, utilizing iterative refinement to enhance receiver performance. Despite this, existing JCDD methods either focus heavily on theoretical analysis, often neglecting practical application scenarios, or experience performance limitations due to inherent design flaws. In this paper, we propose an advanced iterative JCDD receiver that effectively mitigates the negative effects of pilot contamination and data interference. Our approach improves traditional linear minimum mean-square error (LMMSE) channel estimation by incorporating state-of-the-art techniques—specifically variational message passing (VMP) and deep learning (DL)—allowing for better adaptation to varying channel conditions. Extensive empirical evaluations demonstrate that our proposed SIP receiver not only surpasses the conventional orthogonal pilot (OP) scheme but also exhibits outstanding adaptability in mismatched channel environments, thanks to the VMP and DL-based improvements.

In the near-field region of extremely large-scale multi-input multi-output (XL-MIMO) systems, efficient channel estimation is a critical challenge, often demanding substantial computational resources. This issue becomes even more pressing in dynamic, time-varying environments where both users and scatterers are in motion, necessitating lower computational complexity for real-time channel estimation. In this paper, we propose an innovative solution for near-field XL-MIMO systems with time-varying channels, introducing a fast and accurate channel estimation and tracking scheme inspired by keypoint detection techniques from computer vision. First, we design and train a high-precision, anchor-free channel keypoint detector (CKDet) using a fine-grained orthogonal matching pursuit (OMP) framework as an effective channel estimator. Building on this, we present a novel conditional cascaded OMP-based channel tracking scheme that exploits spatial correlations between consecutive time slots to significantly reduce computational complexity. After obtaining the keypoint locations at all time slots, we apply the Hungarian algorithm to match users and scatterers across all time slots, enabling the construction of motion trajectories for use in environmental sensing applications. Experimental results validate the proposed channel tracking algorithm, showcasing its superior performance, speed, and resilience across a range of signal-to-noise ratios (SNR).

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmission efficiency due to substantial pilot overhead and computational complexity. This study introduces an innovative approach that integrates CSI acquisition and RIS configuration, leveraging the channel-altering capabilities of the RIS to reduce both the overhead and complexity of CSI acquisition. The focus is on multi-RIS-assisted systems, featuring both direct and reflected propagation paths. By applying a fast-varying reflection sequence during RIS configuration for channel training, the complex problem of channel estimation is decomposed into simpler, independent tasks. These fast-varying reflections effectively isolate transmit signals from different paths, streamlining the CSI acquisition process for both uplink and downlink communications with reduced complexity. In uplink scenarios, a positioning-based algorithm derives partial CSI, informing the adjustment of RIS parameters to create a sparse reflection channel, enabling precise reconstruction of the uplink channel. Downlink communication benefits from this strategically tailored reflection channel, allowing effective CSI acquisition with fewer pilot signals. Simulation results highlight the proposed methodology’s ability to accurately reconstruct the reflection channel with minimal impact on the normalized mean square error while simultaneously enhancing spectral efficiency.

Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under sudden attenuation. Since acknowledgment feedback is subject to long propagation delays when retransmission is unavoidable, a novel error detection method is proposed to roughly detect semantic errors at the regenerative satellite. With the proposed detectors at both the satellite and the gateway, the quality of the received images can be ensured. The simulation results demonstrate that the proposed method can significantly reduce bandwidth requirements, adapt to complex satellite scenarios, and protect semantic information with an acceptable transmission delay.

In the domain of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is crucial. This paper proposes RIS-CoCsiNet, a novel deep learning-based framework aimed at significantly enhancing feedback efficiency. The proposed method leverages the inherent correlation among neighboring user equipments (UEs) by categorizing RIS-UE CSI information into two parts: shared information among nearby UEs and unique information specific to each individual UE. By exploiting the correlation in RIS-UE CSI, redundant transmission of shared information can be substantially reduced, thereby minimizing the overhead associated with repeatedly feeding back this shared data. Unlike conventional autoencoder-based CSI feedback frameworks, our approach incorporates an additional decoder and a combination neural network (NN) at the base station. These components recover the shared information from the feedback CSI of two neighboring UEs and combine it with the individual information, respectively, without requiring any modifications at the UEs. Through end-to-end learning, the encoders at neighboring UEs are trained to collaboratively feedback shared information while independently feeding back the unique information. For UEs equipped with multiple antennas, a baseline NN architecture with long short-term memory (LSTM) modules is introduced to capture the correlation among nearby antennas. Additionally, since the RIS-UE CSI phase is not sparse, we propose magnitude-dependent phase feedback strategies that incorporate statistical or instantaneous CSI magnitude information into the phase feedback process. Extensive simulations across two diverse channel datasets validate the effectiveness of RIS-CoCsiNet.

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 athttps://github.com/kiwi1944/CRISense.

Artificial intelligence (AI)-based channel state information (CSI) prediction, aimed at enhancing CSI accuracy and reducing overhead, has shown significant advancements over traditional model-based prediction methods. Despite these advantages, its practical deployment has been hindered by unreliable prediction performance due to AI instability. This study introduces a reliable AI-based CSI prediction framework by implementing a proxy-based performance monitoring mechanism. Specifically, we deploy a lightweight proxy at the user equipment (UE), trained via knowledge distillation to accommodate the UE’s limited capacities. This proxy mimics the output of the CSI prediction network at the base station (BS) side, enabling the UE to monitor the accuracy of the predicted CSI and prevent undesirable outcomes. To overcome the deployment challenges in operational systems, we detail the practical implementation procedures of our proposed method, covering both offline training and online operation phases. Simulation results show that our proxy-based monitor can achieve over 90% consistency with the CSI prediction network at the BS side and avoid over 85% of unsatisfactory prediction outcomes under various practical considerations, demonstrating remarkable generalization capabilities across different configurations.

The deployment of multiple reconfigurable intelligent surfaces (RIS) is a promising strategy to enhance wireless system performance. However, joint beamforming in multi-RIS assisted systems faces significant challenges due to the increased number of optimization variables, non-convex objective functions, and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and the successive convex approximation algorithm, maximizing the weighted sum rate in a double-RIS assisted downlink multi-user multiple-input single-output system. We also present a general framework for model-driven deep learning that addresses the limitations of existing methods, which often lack flexibility to different channels and suffer from a large training burden due to the high-dimensional action space of deep reinforcement learning (DRL). Initially, we configure the step size in the proposed algorithm as trainable, accelerating convergence. Then, a recurrent neural network generates the step size for iterations, allowing dynamic iteration extension in varying environmental conditions. We enhance the neural network’s self-adaptability by introducing a model-driven DRL algorithm, integrating expert knowledge into the DRL actor network’s design. Simulation results demonstrate up to 30% performance improvement over traditional algorithms, achieved by our model-driven framework. The proposed model-driven DRL shows higher capacity for dynamic extension and rapid adaptation to new environments.

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread, and slight Doppler effects were also observed following the onset of precipitation. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model in the sub-6GHz band that leverages multipath and temporal features. Two variants are developed: RainGaugeNet-R using ResNet1D and RainGaugeNet-T using a Transformer encoder. Using only 20 seconds of CSI data, RainGaugeNet-R achieves up to 95% an average classification accuracy in line-of-sight(LoS) scenarios and 85% in non-line-of-sight(NLoS) conditions. RainGaugeNet-T attains 90% accuracy in LoS and 99% in NLoS settings, demonstrating superior robustness. Both models significantly outperform state-of-the-art baselines while maintaining low computational complexity.

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.

Deep learning has been widely applied to channel estimation (CE), yielding significant performance improvements. However, existing research primarily focuses on static channel scenarios, leading to substantial performance degradation in dynamic environments. Furthermore, the use of fixed pilot patterns fails to adequately capture channel dynamics, resulting in unnecessary pilot overhead. In this study, we propose a Vision Transformer-based joint pilot design (PD) and CE network (PD-CEViT) for orthogonal frequency division multiplexing (OFDM) systems. The PD module leverages maximum Doppler shift and delay spread information to determine pilot positions, effectively capturing channel variations in dynamic scenarios. To further improve CE accuracy and robustness across diverse environments, the coarse CE from the PD module is passed to a CE module that utilizes a Vision Transformer (ViT), forming the joint PD-CEViT structure. Additionally, we introduce a pilot number switch network, named SwitchPD-CEViT, which dynamically adjusts between different PD-CEViT configurations based on the current channel conditions. This strategy balances network performance and pilot overhead, accommodating varying pilot requirements across different scenarios. Simulation results demonstrate that our proposed structure more effectively tracks channel variations compared to fixed pilot patterns. Even under challenging conditions with large Doppler shifts and delay spreads, our method significantly outperforms traditional and deep learning approaches in terms of mean square error (MSE) performance. Moreover, the integration of channel information further enhances estimation performance and robustness. Meanwhile, SwitchPD-CEViT achieves superior CE performance with reduced pilot overhead by efficiently managing pilot utilization.

Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integration. This paper proposes a novel multi-modal SLAM framework that addresses these limitations through three key contributions. First, a Bayesian estimation framework is developed for cooperative multi-user SLAM, along with a two-stage algorithm for robust radio map construction under dynamic and heterogeneous sensing conditions. Second, a multi-modal localization strategy is introduced, fusing SLAM results with camera-based multi-object tracking and inertial measurement unit (IMU) data via an error-aware model, significantly improving UE localization in multi-user scenarios. Third, a sensing-aided beam management scheme is proposed, utilizing global radio maps and localization data to generate UE-specific prior information for beam selection, thereby reducing inter-user interference and enhancing downlink spectral efficiency. Simulation results demonstrate that the proposed system improves radio map accuracy by up to 60%, enhances localization accuracy by 37.5%, and significantly outperforms traditional methods in both indoor and outdoor environments.

In pursuit of enhanced quality of service and higher transmission rates, communication within the mid-band spectrum, such as bands in the 6-15 GHz range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is considered a potential enabler for future communication systems. However, the characteristics introduced by mid-band XL-MIMO systems pose challenges for channel modeling and performance analysis. In this paper, we first analyze the potential characteristics of mid-band MIMO channels. Then, an analytical channel model incorporating novel channel characteristics is proposed, based on a review of classical analytical channel models. This model is convenient for theoretical analysis and compatible with other analytical channel models. Subsequently, based on the proposed channel model, we analyze key metrics of wireless communication, including the ergodic spectral efficiency (SE) and outage probability (OP) of MIMO maximal-ratio combining systems. Specifically, we derive closed-form approximations and performance bounds for two typical scenarios, aiming to illustrate the influence of mid-band XL-MIMO systems. Finally, comparisons between systems under different practical configurations are carried out through simulations. The theoretical analysis and simulations demonstrate that mid-band XL-MIMO systems excel in SE and OP due to the increased array elements, moderate large-scale fading, and enlarged transmission bandwidth.

Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towards extra-large-scale MIMO. However, centralized processing for massive MIMO faces practical obstacles, including excessive computational complexity and a substantial volume of baseband data to be exchanged. To address these challenges, decentralized baseband processing has emerged as a promising solution. This approach involves partitioning the antenna array into clusters with dedicated computing hardware for parallel processing. In this paper, we investigate the gradient-based Markov chain Monte Carlo (MCMC) method—an advanced MIMO detection technique known for its near-optimal performance in centralized implementation—within the context of a decentralized baseband processing architecture. This decentralized design mitigates the computation burden at a single processing unit by utilizing computational resources in a distributed and parallel manner. Additionally, we integrate the mini-batch stochastic gradient descent method into the proposed decentralized detector, achieving remarkable performance with high efficiency. Simulation results demonstrate substantial performance gains of the proposed method over existing decentralized detectors across various scenarios. Moreover, complexity analysis reveals the advantages of the proposed decentralized strategy in terms of computation delay and interconnection bandwidth when compared to conventional centralized detectors.

Reconfigurable intelligent surfaces (RISs) offer the unique capability to reshape the radio environment, thereby simplifying transmission schemes traditionally contingent on channel conditions. Joint spatial division and multiplexing (JSDM) emerges as a low-overhead transmission scheme for multi-user equipment (UE) scenarios, typically requiring complex matrix decomposition to achieve block-diagonalization of the effective channel matrix. In this study, we introduce an innovative JSDM design that leverages RISs to customize channels, thereby streamlining the overall procedures. By strategically positioning RISs at the discrete Fourier transform (DFT) directions of the base station (BS), we establish orthogonal line-of-sight links within the BS-RIS channel, enabling a straightforward pre-beamforming design. Based on UE grouping, we devise reflected beams of the RIS with optimized directions to mitigate inter-group interference in the RISs-UEs channel. An approximation of the channel cross-correlation coefficient is derived and serves as a foundation for the RISs-UEs association, further diminishing inter-group interference. Numerical results substantiate the efficacy of our RIS-customized JSDM in not only achieving effective channel block-diagonalization but also in significantly enhancing the sum spectral efficiency for multi-UE transmissions.

Artificial intelligence (AI) has emerged as a promising tool in channel state information (CSI) feedback tasks. Although current research primarily focuses on improving feedback accuracy through innovative AI approaches, the reliability of these systems in real-world scenarios often goes overlooked. Specifically, a closer examination of the feedback accuracy of individual CSI samples reveals significant variations, underscoring the imperative need for performance monitoring of AI-based CSI feedback. Building upon this observation, we introduce a pragmatic framework for AI-based CSI feedback. This process involves assessing feedback accuracy (i.e., conducting performance monitoring) on the user side before transmitting the CSI codeword. In particular, this method utilizes a lightweight proxy decoder, trained via knowledge distillation, to emulate the mapping function of the original decoder at the base station. The goal is to generate, at the user end, CSI identical to that produced at the base station by the original, more powerful decoder, thus enable precise prediction of feedback accuracy. Simulation results demonstrate that our proposed performance monitoring method can precisely predict feedback accuracy with low complexity and accurately detect low-quality feedback samples with a detection rate of nearly 95%, ensuring reliable transmission.

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.

This paper introduces a novel smartphone-enabled localization technology for ambient Internet of Things (IoT) devices, leveraging the widespread use of smartphones. By utilizing the passive movement of a smartphone, we create a virtual large array that enables direct localization using only angle-of-arrival (AoA) information. Unlike traditional two-step localization methods, direct localization is unaffected by AoA estimation errors in the initial step, which are often caused by multipath channels and noise. However, direct localization methods typically require prior environmental knowledge to define the search space, with calculation time increasing as the search space expands. To address limitations in current direct localization methods, we propose a machine learning (ML)-based direct localization technique. This technique combines ML with an adaptive matching pursuit procedure, dynamically generating search spaces for precise source localization. The adaptive matching pursuit minimizes location errors despite potential accuracy fluctuations in ML across various training and testing environments. Additionally, by estimating the reflection source’s location, we reduce the effects of multipath channels, enhancing localization accuracy. Extensive three-dimensional ray-tracing simulations demonstrate that our proposed method outperforms current state-of-the-art direct localization techniques in computational efficiency and operates independently of prior environmental knowledge.

The trend toward using increasingly large arrays of antenna elements continues. However, fitting more antennas into the limited space available on user equipment (UE) within the currently popular Frequency Range 1 spectrum presents a significant challenge. This limitation constrains the capacity-scaling gains for end users, even when networks support a higher number of antennas. To address this issue, we explore a user-centric collaborative MIMO approach, termed UE-CoMIMO, which leverages several fixed or portable devices within a personal area to form a virtually expanded antenna array. This paper develops a comprehensive mathematical framework to analyze the performance of UE-CoMIMO. Our analytical results demonstrate that UE-CoMIMO can significantly enhance the system’s effective channel response within the current communication system without requiring extensive modifications. Further performance improvements can be achieved by optimizing the phase shifters on the expanded antenna arrays at the collaborative devices. These findings are corroborated by ray-tracing simulations. Beyond the simulations, we implemented these collaborative devices and successfully conducted over-the-air validation in a real 5G environment, showcasing the practical potential of UE-CoMIMO. Several practical perspectives are discussed, highlighting the feasibility and benefits of this approach in real-world scenarios.

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by $\mathbf{4 8. 5 \%}$ and UE localization accuracy by $13.5 \%$, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings.

Location information of the smart devices (SDs) can be utilized in many applications on the user equipment (UE) side. A moving UE can form a virtual large array (VLA) and receive near-field signals, which can be used to find the SDs' location. Newtonized Orthogonal Matching Pursuit (NOMP), a super-resolution method, estimates the SD's location by applying Newton's refinement (NR) step after OMP estimation. However, OMP, NOMP, and many other methods are generally based on grid search methods and require the appropriate search space. Traditionally, the search space was defined by prior room size information, which is hard to obtain in practice. In this paper, we proposed DNOMP, a NOMP-based method that uses deep learning (DL) to offer the search space. The DL model can significantly reduce the computational complexity but cannot find the accurate location if the training and testing data are from different environments. However, the location error decreases through the NR step. Our three-dimensional ray-tracing simulations show that DNOMP can achieve the same accuracy as other state-of-the-art direct localization methods, but it has less computational complexity and does not require prior information; thus, it can be applied in any indoor environment.

Wi-Fi access point (AP) and IoT device localization are essential for smart home functionalities, including indoor localization and privacy protection. Yet, complex multipath channels in indoor settings often hinder precise localization. To overcome this, we introduce an Artificial Intelligence (AI) technique that amalgamates channel state information from proximate trajectory points, thus elevating the accuracy of line of sight (LoS) angle of arrival (AoA) estimation. Our methodology initiates with an AI-based anomaly detection system to eliminate questionable measurements. Thereafter, our AI-optimized LoS-AoA network proficiently identifies the primary LoS path from the several multipaths detected by the multipath estimation process and autonomously fine-tunes the LoS-AoA estimation. Using simulations in an indoor office environment with Wireless Insite, our results reveal that our approach considerably improves LoS-AoA estimations, even under challenging indoor scenarios. Notably, our technique enhanced AP positioning accuracy in 68% of instances, reducing a 2-meter error to 0.6 meters, and in 95% of instances, cutting down a 10-meter error to 2 meters when measured against top benchmarks.

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G New Radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing Simultaneous Localization and Mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM.

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing Line of Sight (LoS) Angle of Arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection (AD) algorithm to further increase the reliability of LoS-AoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed AD algorithm trained on Wi-Fi data can be directly applied to ultrawideband, also outperforming the most advanced techniques.

Deep learning has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher computational complexity. Pursuing better performance-complexity tradeoffs is crucial to facilitate practical deployment, especially on computation-limited devices, which may have to use lightweight autoencoder with unfavorable performance. To achieve this goal, this paper introduces knowledge distillation (KD) to achieve better tradeoffs, where knowledge from a complicated teacher autoencoder is transferred to a lightweight student autoencoder for performance improvement. Specifically, two methods are proposed for implementation. Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder KD-based method is proposed to reduce training overhead by performing KD only on the student encoder. Additionally, a variant of encoder KD is introduced to protect user equipment and base station vendor intellectual property. Numerical simulations demonstrate that the proposed methods can significantly improve the student autoencoder’s performance, while reducing the number of floating point operations and inference time to 3.05%–5.28% and 13.80%–14.76% of the teacher network, respectively. Furthermore, the variant encoder KD method effectively enhances the student autoencoder’s generalization capability across different scenarios, environments, and bandwidths.

Retrieving range information in three-dimensional (3D) radio imaging is particularly challenging due to the limited communication bandwidth and pilot resources. To address this issue, we consider a reconfigurable intelligent surface (RIS)-aided uplink communication scenario, generating multiple measurements through RIS phase adjustment. This study successfully realizes 3D single-frequency imaging by exploiting the near-field multi-view image correlations deduced from user mobility. We first highlight the significance of considering anisotropy in multi-view image formation by investigating radar cross-section properties and diffraction resolution limits. We then propose a novel model for joint multi-view 3D imaging that incorporates occlusion effects and anisotropic scattering. These factors lead to slow image support variation and smooth coefficient evolution, which are mathematically modeled as Markov processes. Based on this model, we employ the Expectation Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint multi-view single-frequency 3D imaging with limited measurements. Simulation results reveal the superiority of joint multi-view imaging in terms of enhanced imaging ranges, accuracies, and anisotropy characterization compared to single-view imaging. Combining adjacent observations for joint multi-view imaging enables a reduction in the measurement overhead by 80%.

Semantic communication, through the interpretation of the semantic meaning of transmitted data, effectively reduces the required bandwidth. However, current deep learning-based methods face limitations due to their reliance on joint source-channel coding and end-to-end training, hindering adaptability to new channels and user demands. In this study, we introduce the Reconfigurable Intelligent Surface-Semantic Communication (RIS-SC) framework as a solution. This framework dynamically allocates semantic content, leveraging varying degrees of RIS assistance to cater to the evolving needs of users. It takes into account factors such as user mobility and obstacles in the line of sight, enabling the RIS resource to preserve essential semantics even in challenging channel conditions. While this ensures the preservation of core semantics in difficult channel conditions, it may also lead to the loss of some non-essential semantic details under extreme conditions. To counteract this, we have incorporated a reconstruction method that deduces the missing semantic elements, thereby enhancing visual understanding. The RIS-SC framework stands out for its adaptability, ensuring optimal resource distribution for users under favorable conditions and maintaining visual clarity in challenging scenarios. Simulations validate the effectiveness and adaptability of our approach in diverse channel conditions and user demands.

Deep learning (DL)-based channel state information (CSI) feedback has garnered significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by transmitting CSI datasets during the training process. In this study, we introduce a federated edge learning (FEEL)-based training framework for DL-based CSI feedback. This approach differs from the conventional centralized learning (CL)-based framework, where the CSI datasets are collected at the base station (BS) before training. Instead, each user equipment (UE) trains a local autoencoder network and exchanges model parameters with the BS. This approach provides better protection for data privacy compared to CL. To further reduce communication overhead in FEEL, we quantize the uplink and downlink model transmission into different bits based on their influence on feedback performance. Additionally, since the heterogeneity of CSI datasets among different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to enhance feedback performance. This strategy allows for local fine-tuning to adapt the global model to the channel characteristics of each UE. Simulation results indicate that the proposed personalized FEEL-based training framework can significantly improve the performance of DL-based CSI feedback while reducing communication overhead.

Most learning-based channel state information (CSI) feedback efforts concentrate on enhancing feedback accuracy through innovative neural network (NN) designs and exploiting correlations. This paper introduces an integrated learning framework for CSI feedback and localization designed to synergistically improve both tasks. We present a novel unified approach for CSI feedback and downlink CSI-based localization, where feedback is facilitated by an autoencoder, and the downlink CSI-based localization uses the feedback codeword directly without requiring reconstruction. The goal is to simultaneously minimize feedback and localization errors. Additionally, for users with access to coarse position data, we propose a refined framework that integrates this information into both the feedback mechanism and localization processes. This coarse positional knowledge is incorporated into the encoding and decoding stages to reduce feedback errors and is inputted into the localization NN to enhance localization accuracy. The improved framework is refined through an end-to-end training strategy, focusing on concurrently reducing feedback and localization errors. Simulation results using ray tracing channel datasets demonstrate that our proposed method not only enables feedback and localization tasks to mutually benefit but also shows that incorporating coarse positional data significantly increases the accuracy of both CSI feedback and CSI-based localization.

Radio imaging is rapidly gaining prominence in the design of future communication systems, with the potential to utilize reconfigurable intelligent surfaces (RISs) as imaging apertures. Although the sparsity of targets in three-dimensional (3D) space has led most research to adopt compressed sensing (CS)-based imaging algorithms, these often require substantial computational and memory burdens. Drawing inspiration from conventional Fourier transform (FT)-based imaging methods, our research seeks to accelerate radio imaging in RIS-aided communication systems. To begin, we introduce a two-stage wavenumber domain 3D imaging technique: first, we modify RIS phase shifts to recover the equivalent channel response from the user equipment to the RIS array, subsequently employing traditional FT-based wavenumber domain methods to produce target images. We also determine the diffraction resolution limits of the system through k-space analysis, taking into account factors including system bandwidth, transmission direction, operating frequency, and the angle subtended by the RIS. Addressing the challenge of limited pilots in communication systems, we unveil an innovative algorithm that merges the strengths of both FT- and CS-based techniques by substituting the expansive sensing matrix with FT-based operators. Our simulation outcomes confirm that our proposed FT-based methods achieve high-quality images while demanding few time, memory, and communication resources.

Deep learning has been introduced to improve implicit channel state information (CSI) feedback, and it significantly outperforms codebook-based feedback methods used in existing systems. This study proposes a multi-domain correlation-aided implicit CSI feedback framework that uses deep learning. This framework retains the existing implicit feedback mechanism while introducing the aid of the multi-domain correlation property of CSI matrices to the feedback process for performance improvement. First, a time correlation-aided implicit feedback framework is proposed, where the correlation among adjacent CSI matrices is exploited to improve the CSI reconstruction accuracy. Second, to utilize the correlation between the uplink and downlink channel, the uplink channel magnitude is introduced into the CSI reconstruction process at the base station. Additionally, the framework combines the aid of time and bidirectional channel correlation properties to further enhance performance. Simulation results show that, with the aid of the multi-domain correlation property, the feedback overhead can be reduced by 75% and 85% compared to approaches without correlation utilization and Type II codebook, respectively.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional codebook-based channel state information (CSI) feedback techniques. Confronted with the influx of CSI data from simulations and real-world environments, leveraging neural networks to mine valuable insights poses significant training costs and technical challenges for base station (BS) manufacturers. To address this, we propose a third-party platform serving as a CSI knowledge repository and feedback model hub, reducing training expenses and addressing technical issues for various BS manufacturers. However, tailoring training for each manufacturer’s model may lead to proprietary information leaks and inefficient resource utilization. In response, we present “CSI Meta-knowledge Support”, a cutting-edge CSI feedback network deployment strategy using Learngene, enabling seamless transfer of CSI meta-knowledge across heterogeneous networks. This method captures a Learngene unit enriched with vital CSI meta-knowledge during comprehensive training sessions, serving as a plug-and-play prior to facilitate swift convergence and efficient local fine-tuning for manufacturers. The approach introduces adaptable and scalable CSI feedback network configurations, emphasizing reusability, cost-effectiveness, and resource management while safeguarding intellectual property. Our tests demonstrate enhanced performance, reduced training sample demands, and faster convergence relative to conventional techniques.

Deep learning has brought about a revolution in the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process, and manual design can be prohibitively expensive for customized NNs tailored to different scenarios. To tackle this challenge, this paper proposes the use of neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved, requiring less expert experience and design time, thus lowering the design threshold. The proposed approach leverages implicit scene knowledge and integrates it into the scenario customization process in a data-driven manner, fully exploiting the potential of deep learning in a given scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, further enhancing the proposed scheme. The experimental results demonstrate that the generated architecture, known as Auto-CsiNet, outperforms manually-designed models in terms of reconstruction performance (achieving approximately 14% improvement) and complexity (reducing by approximately 50%), highlighting the effectiveness of the NAS-based automatic scheme. Furthermore, the paper analyzes the impact of the scenario on the NN architecture and capacity.

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cramér-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov’s gradient method. Nesterov’s GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov’s GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.

Semantic communication can significantly reduce transmission payload by sending only semantic information related to the task. However, existing end-to-end trained semantic studies degrade under extreme channel environments, while reconfigurable intelligent surface (RIS) technology offers a potential solution for realizing channel customization. In this work, we propose a reconfigurable RIS-enhanced semantic communication framework called RIS-SC. This framework allows for customization of the channel environment based on the user's requirements for different semantic parts, rather than relying solely on the conventional bit error rate requirement. Using reinforcement learning, the RIS controller interacts with varying channels to meet the user's different requirements. The RIS controller adaptively protects important semantic parts by adjusting the channel conditions. Simulation results demonstrate that the proposed RIS-SC framework can adapt to different channel environments and improve task performance under varying requirements, such as vertical semantic or true image reconstruction.

Deep learning (DL) has been widely utilized for channel estimation and has resulted in significant performance improvements. However, most existing research only performs training and testing in relatively static scenarios, leading to a serious deterioration in dynamic scenarios. In this paper, we propose a robust channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in dynamic scenarios called channel estimator Vision Transformer (CE-ViT) based on attention mechanism. We perform a patch embedding operation to process data in both the time and frequency domains, addressing the limitations of the attention mechanism in extracting 2D correlations. Additionally, we introduce tokens that reflect channel characteristics into the network to enhance the robustness. Experimental results show that CE-ViT outperforms the state-of-the-art DL-based methods. Moreover, the addition of tokens significantly improves the performance of CE-ViT in dynamic channel conditions.

Optimal detection of symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is known to entail exponentially increasing complexity with MIMO dimensions, making it impractical for large-scale systems. Recently, Markov chain Monte Carlo (MCMC) has been combined with the gradient descent (GD) method to create a promising machine learning solution to this issue. This paper proposes a novel algorithm for approaching optimal detection via MCMC random walk accelerated by Nesterov's gradient method, efficiently exploring the discrete search space for MIMO detection. Our proposed method utilizes multiple GDs per random walk and guarantees high sampling efficiency while mitigating the complexity associated with matrix inversions. Simulation results and complexity analysis reveal that the proposed method achieves near-optimal performance and scales effectively to large MIMO dimensions.

Deep learning has been introduced to implicit channel state information (CSI) feedback and considerably outperforms codebook-based feedback methods adopted by existing systems. This work proposes a time correlation-aided deep learning-based implicit CSI feedback framework named Tbi-ImCsiNet. The long short-term memory network is introduced into the implicit CSI compression side and reconstruction side to extract and utilize the time correlation property among CSI matrices and improve the framework performance. Simulation results show that the proposed Tbi-ImCsiNet reduces approximately 58.3% of the feedback overhead compared with the method without time correlation utilization.

Deep learning (DL) has shown great potential in channel state information (CSI) feedback and localization. In this paper, a DL-based integrated CSI feedback and localization framework called FLnet, in which the feedback and localization tasks complement each other, is proposed. Specifically, unlike the existing works that sequentially realize the above two tasks, FLnet jointly designs the autoencoder-based feedback and deep neural networks (DNN)-based localization tasks. The encoder at the user equipment (UE) compresses and quantizes the downlink CSI. Then, the decoder and the DNN at the base station reconstruct the downlink CSI and predict the location of the UE based on the feedback information, respectively. The feedback and localization modules are trained together by an end-to-end approach. Simulation results show that the localization error of FLnet is reduced by 30% compared with that of the separate design while the feedback performance is comparable or even improved.

Deep learning has revolutionized the design of channel state information (CSI) feedback modules in wireless communication. However, designing an optimal neural network (NN) architecture for CSI feedback can be laborious and time-consuming, especially for customized networks targeting different scenarios. To address this challenge, this paper proposes the use of Neural Architecture Search (NAS) to automatically generate scenario-specific CSI feedback neural network architectures. By employing automated machine learning and gradient-based NAS, an efficient and cost-effective architecture design process is achieved with reduced reliance on expert knowledge and design time, thus lowering the design threshold. This approach leverages implicit scenario knowledge and integrates it into the scenario customization process in a data-driven manner, fully harnessing the potential of deep learning in a given scenario. Experimental results demonstrate that the generated architecture called Auto-CsiNet outperforms manually designed models in terms of reconstruction performance (improvement by approximately 14%) and complexity reduction (approximately 50%), highlighting the effectiveness of NAS-based automated solutions.

Extra large-scale multiple-input multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this paper, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view.

With the advances of the Internet of Things and mobile connectivity, location-based services are becoming increasingly popular and continue to enhance our experience. Multiple antennas have been pivotal in providing reliable wireless communications and high-resolution localization. If the antennas of the array are isotropic, then the simplified array manifold determined by the array geometry can be used to estimate the angle of arrival (AOA). However, in the real world, mobile handsets tend to have very limited space, where the practical antennas are equipped on the same ground plane, and the array geometry hardly obeys the rule of half-wavelength spacing. Therefore, a practical antenna couple signals from other antennas, causing a mutual coupling effect. Complex array manifolds are produced on an antenna even if the received signal is propagated through a single path channel. In addition, the irregular radiation pattern of each antenna further impairs the AOA estimation capability. Given the above effects, the simplified array manifold determined by the array geometry can no longer provide precise localization. In this article, we propose a generic array manifold model for both isotropic and practical antennas. We also present an efficient algorithm to enable AOA estimation on practical antennas on the basis of the proposed model and implement it on a 5G phone at a mid-band spectrum with a 100-MHz channel bandwidth. Results reveal the promising performance of the proposed model, with the AOA estimation errors lower than 10° in over 90% of the scenarios.

Determining the location of Wi-Fi access points (APs) is vital for various Wi-Fi-based applications, such as localization, security, and AP deployment. Considerable effort has been exerted in the field of AP localization. In contrast to studies that require additional robots with specialized antenna arrays, we present EasyAPPos, a lightweight, always-on, and user-centered AP positioning solution that utilizes widely available mobile phones. We focus on addressing three challenges in AP positioning. First, the patch antenna on a mobile phone has a limited angular range due to its size, but our approach proposes a method for utilizing human natural rotation to enhance angular diversity. Second, our angle-based algorithm does not require synchronous clocks between the mobile device and the APs, in contrast to existing algorithms that require this synchrony to transform propagation delays into positions. Nevertheless, our algorithm can still utilize asynchronous delay information. Third, the low bandwidth of Wi-Fi beacon frames, which only provide limited capacity to counteract the effects of multipath, is addressed by performing AP positioning under challenging conditions. We validate EasyAPPos through simulations and experiments, which demonstrate its ability to achieve decimeter-level positioning accuracy even under harsh conditions.

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this study, we propose multi-timescale channel customization for RIS-assisted multiple-input multiple-output systems to facilitate transmission design. Specifically, we customize a high-rank channel for spatial multiplexing (SM) transmission and a highly correlated rank-1 channel for beamforming (BF) transmission by designing the phase shifters of the RIS with statistical channel state information in the angle-coherent time to improve spectral efficiency (SE). We derive closed-form expressions for the approximation and upper bound of the ergodic SE and compare them to investigate the relative SE performance of SM and BF transmissions. In terms of reliability enhancement, we customize a fast-changing channel in the symbol timescale to achieve more diversity gain for SM and BF transmissions. Extensive numerical results demonstrate that flexible customization of channel characteristics for a specific transmission scheme can achieve a tradeoff between SE and bit error ratio performance.

Video conferencing has become a popular mode of meeting despite consuming considerable communication resources. Conventional video compression causes resolution reduction under a limited bandwidth. Semantic video conferencing (SVC) maintains a high resolution by transmitting some keypoints to represent the motions because the background is almost static, and the speakers do not change often. However, the study on the influence of transmission errors on keypoints is limited. In this paper, an SVC network based on keypoint transmission is established, which dramatically reduces transmission resources while only losing detailed expressions. Transmission errors in SVC only lead to a changed expression, whereas those in the conventional methods directly destroy pixels. However, the conventional error detector, such as cyclic redundancy check, cannot reflect the degree of expression changes. To overcome this issue, an incremental redundancy hybrid automatic repeat-request framework for varying channels (SVC-HARQ) incorporating a novel semantic error detector is developed. SVC-HARQ has flexibility in bit consumption and achieves a good performance. In addition, SVC-channel state information (CSI) is designed for CSI feedback to allocate the keypoint transmission and enhance the performance dramatically. Simulation shows that the proposed wireless semantic communication system can remarkably improve transmission efficiency.

The use of a hybrid analog-digital architecture that connects one RF chain to multiple antennas through phase shifters is an energy-efficient solution for multiuser multiple-input multiple-output (MU-MIMO) systems. However, designing the hybrid precoder is challenging due to its nonconvex objective functions and constraints. Existing algorithms struggle with high computational complexity or poor performance, which often result from slow or no convergence. This study proposes a solution that leverages model-driven deep learning (DL) to maximize the spectral efficiency of MU-MIMO systems through hybrid precoding. The optimization problem is first transformed into a weighted minimum mean square error optimization. Then, it is combined with manifold optimization and DL to improve performance and simplify the process. The algorithm is designed to be robust in changing environments and utilizes DL to address imperfect channel state information. Simulation results show that the proposed method outperforms existing algorithms, is robust in changing system parameters, and can even outperforms fully digital precoding with the same number of antennas.

For frequency division duplex (FDD) systems, downlink channel state information (CSI) feedback is essential. Deep learning-based auto-encoder (AE) structures have shown promise in reducing feedback overhead. However, designing a super-large AE network to handle the CSI of all scenarios is not practical. A more practical approach is to divide the CSI dataset by region/scenario and use multiple simple AE networks. However, this method requires high memory capacity, making it unsuitable for low-end user equipment (UE). In this paper, we propose a new UE-friendly framework based on multi-tasking mode. Our framework, called single-encoder-to-multiple-decoders (S-to-M), uses multi-task-learning to design multiple independent AEs into a joint architecture with a shared encoder that corresponds to multiple task-specific decoders. We also integrate GateNet as a classifier to enable the base station to autonomously select the right task-specific decoder for the subregion. Our experiments on a simulated multi-scenario CSI dataset show that our proposed S-to-M framework outperforms other benchmark modes by significantly reducing model complexity and UE memory consumption.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem in millimeter wave (mmWave) communications. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Considering that the currently available RIS hardware cannot arbitrarily switch between the active (reflection with configurable phase response) and deactivate (absorption) modes, we suggest a new beam training method for RIS-assisted mmWave communication systems in this study. First, a predefined hierarchical codebook is created using the pattern synthesis method. Then, we provide a novel hierarchical beam training method using two multi-mainlobe codewords in each layer of the hierarchical codebook for beam sweeping. Combining the results of the beam identification in all the layers will yield the ultimate ideal beam direction. Theoretical analyses demonstrate that the suggested approach can effectively reduce training overhead while ensuring successful beam alignment. Simulation results show that the practical codebook can be created successfully, and the suggested method can achieve accurate beam alignment with reduced training overhead.

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein’s unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have a greater potential to assist in localization due to their ability to maintain high RCSs and create strong NLOS links. A one-stage linear weighted least squares estimator is proposed to simultaneously determine user equipment (UE) locations, velocities, and scatterer (or RIS) locations using line-of-sight (LOS) and NLOS paths. The estimator supports environment sensing and UE localization even using only NLOS paths. A second-stage estimator is also introduced to improve environment sensing accuracy by considering the nonlinear relationship between UE and scatterer locations. Simulation results demonstrate the effectiveness of the proposed estimators in rich scattering environments and the benefits of using NLOS paths for improving UE location accuracy and assisting in environment sensing. The effects of RIS number, size, and deployment on localization performance are also analyzed.

To address the inherent channel deficiency in strong line-of-sight (LoS) millimeter-wave (mmWave) wireless systems, distributed reconfigurable intelligent surfaces (RISs) are introduced in this study to customize the wireless channel. Capitalizing on the ability of the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. The number of elements needed for each RIS to combat the path loss is also considered in this research. Numerical results show that the effective channel rank can be flexibly and accurately customized according to the required number of data streams. Moreover, utilizing our proposal, every corner in the interested coverage can build the well-conditioned channel with small truncated condition number.

The downlink channel state information (CSI) feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems with deep learning. The key idea of this work is to hide downlink CSI within the transmitted messages (e.g., images) with no transmission resource occupation and few effects on the message semantic. We propose a novel neural network framework, in which the user extracts and hides the CSI features within the images by networks, and the base station recovers the CSI from the transmitted images. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the transmitted images, including the image quality and classification accuracy.

Channel state information (CSI) plays a crucial role in the capacity of multiple-input and multiple-output systems, but CSI feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems through deep learning. The key idea is to hide/superimpose CSI in transmitted messages (e.g., images) with no transmission resource occupation and few effects on message semantics. Concretely, we introduce a novel neural network framework in which the user extracts and hides CSI features in images, and the base station recovers the CSI from the transmitted images. However, the essential source coding (e.g., JPEG compression) before data transmission causes two problems in the proposed EliCsiNet framework when applied in practical systems. First, the compression inevitably disturbs the information of the hidden CSI in images and affects the CSI reconstruction accuracy. Therefore, a two-stage separable training strategy, which includes coding-free end-to-end and coding-aware decoder-only training, is adopted to reduce these effects. Second, the bit length of the images coded via JPEG is unpredictable and uncontrollable, and CSI superimposition may lead to an increase in the bit length of the coded images. To avoid this issue, we divide a full image into several sub-blocks and select the one with the smallest length increment. Image entropy is also introduced to accelerate block selection. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the features properties of transmitted images, including image quality and bit length.

Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication. In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in whichmutual enhancementbetween the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% – 40.0% of overhead compared with the Type I codebook under different antenna configurations. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with the Type II codebook when ignoring and considering extracting subband correlation, respectively.

The autoencoder empowered by artificial intelligence has shown considerable potential in solving channel state information (CSI) feedback problems in frequency-division duplexing systems. However, this method needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. This paper proposes an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to feedback process through neural networks (NNs) at the base station. Only an NN-based refining operation is added after the common standardized feedback approach. The NNs learn to automatically extract environment features and utilize the channel statistics through large volumes of recorded data. The NNs also use the partial correlation between bidirectional channels to further improve feedback performance. In addition, to deal with downlink channel estimation errors, we propose two strategies to reduce their effects using an NN-based denoise module. The proposed framework can be easily embedded in most existing codebook-based feedback methods, such as random vector quantization. Two channel datasets generated by QuaDRiGa and measured in practical systems are adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline codebook because of more accurate feedback.

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overhead. In this work, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce such overhead. We consider the entire downlink CSI acquisition process, including the downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Second, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Finally, we combine the two modules and compare two popular uplink-aided downlink channel acquisition frameworks. One framework estimates and subsequently feeds back the channel at the user equipment. In the other framework, the user equipment directly feeds back the received pilot signals to the base station. Results reveal that with the help of the uplink channel, directly feeding back pilot signals can save approximately 20% of feedback bits. This work thus provides a guideline for future research.

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user equipment) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including bitstream generation, multirate feedback, imperfect feedback, NN complexity, training dataset collection, online training, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

Recently, semantic communication has been brought to the forefront because deep learning (DL)-based methods, such as Transformer, have achieved great success in semantic extraction. Although semantic communication has been successfully applied in sentence transmission to reduce semantic errors, the existing architecture is usually fixed in terms of codeword length and inefficient and inflexible for varying sentence lengths. In this study, we exploit hybrid automatic repeat request (HARQ) to reduce the semantic transmission error further. We combine semantic coding (SC) with Reed-Solomon (RS) channel coding and HARQ (called SC-RS-HARQ). SC-RS-HARQ exploits the superiority of SC and the reliability of conventional methods successfully. Although SC-RS-HARQ can be easily applied in existing HARQ systems, we also develop an end-to-end architecture called SCHARQ to pursue enhanced performance. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for semantic sentence transmission and the sentence error rate. We also attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve wrong sentences with similar semantic information and conserve transmission resources.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.

In frequency division duplexing systems, the base station (BS) acquires downlink channel state information (CSI) via channel feedback, which has not been adequately investigated in the presence of RIS. In this study, we examine the limited channel feedback scheme by proposing a novel cascaded codebook and an adaptive bit partitioning strategy. The RIS segments the channel between the BS and mobile station into two sub-channels, each with line-of-sight (LoS) and non-LoS (NLoS) paths. To quantize the path gains, the cascaded codebook is proposed to be synthesized by two sub-codebooks whose codeword is cascaded by LoS and NLoS components. This enables the proposed cascaded codebook to cater the different distributions of LoS and NLoS path gains by flexibly using different feedback bits to design the codeword structure. On the basis of the proposed cascaded codebook, we derive an upper bound on ergodic rate loss with maximum ratio transmission and show that the rate loss can be cut down by optimizing the feedback bit allocation during codebook generation. To minimize the upper bound, we propose a bit partitioning strategy that is adaptive to diverse environment and system parameters. Extensive simulations are presented to show the superiority and robustness of the cascaded codebook and the efficiency of the adaptive bit partitioning scheme.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage thresholding algorithm-based channel estimator (LISTA-CE) based on deep learning. The proposed channel estimator can learn to transform the beam-frequency mmWave channel into the domain with sparse features through training data. The transform domain enables us to adopt a simple denoiser with few trainable parameters. We further enhance the adaptivity of the estimator by introducing hypernetwork to automatically generate learnable parameters for LISTA-CE online. Simulation results show that the proposed approach can significantly outperform the state-of-the-art deep learning-based algorithms with lower complexity and fewer parameters and adapt to new scenarios rapidly.

In frequency-division duplexing systems, the downlink channel state information (CSI) should be fed back through an uplink transmission to reap the benefits of the massive multiple-input and multiple-output system, thereby leading to a large feedback overhead. The autoencoder-based architecture empowered by artificial intelligence has shown considerable potential in solving the CSI feedback problem. This method, however, needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. In this paper, we propose an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to the feedback process through neural networks (NNs) at the base station. The NNs learn to automatically extract the environment features and utilize the channel statistics through large volumes of recorded data. The channel dataset, which is generated by QuaDRiGa software, is adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline feedback codebook because of the more accurate CSI feedback.

In massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) is required by the base station (BS) to achieve high-performance gains. In frequency division duplexing (FDD) systems, the downlink CSI matrix should be sent back to the BS; unfortunately, the computational and overhead cost of this task is inherently high. Recently, deep learning has been increasingly applied in the space of CSI feedback. However, neural networks entail extra memory and computational requirements, which undermines the deployment of CSI feedback neural networks at the user equipment (UE) side. The conventional lightweight methods such as pruning and quantization requires heavy workload of experiments and difficulty of individually designing training methods for each neural network (NN). In this paper, a novel network lightweight method utilizing knowledge distillation as a training method is introduced to lighten the computation burden of the encoder at the UEs. Knowledge distillation (KD) aims at transferring knowledge from a complex network to a simple network and improving the performance of the simple network close to the complex network. Our numerical experiments demonstrate that the performance of the proposed network can be improved with KD.

Dual-polarized antenna arrays are widely used to reduce the array aperture and expand the channel capacity in multiple-input multiple-output (MIMO) systems. However, a challenge for doubling the number of antennas is how to reconstruct the large-dimensional channel with low complexity. In this paper, we prove the similarity of the delays, AOAs, and the number of the paths between channels in different polarizations, which is the property of polarization-independency, and verify it through both theoretical analysis and experiments. On basis of the similarity among polarizations, we propose a dual-polarized channel extrapolation scheme with low pilot cost and low computational complexity. Simulations and experiments are conducted to examine the performance of the proposed extrapolation scheme. Results show that the proposed dual-polarized channel extrapolation scheme is feasible in practice and can achieve a good channel reconstruction performance with reduced computational complexity.

The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, the user in frequency-division duplexing (FDD) systems has to keep on feeding back the CSI to the BS, thereby occupying large uplink transmission resources. Recently, deep learning (DL) has achieved great success in the CSI feedback. However, the existing works just focus on improving the feedback accuracy and ignore the effects on the following modules, e.g., beamforming (BF). In this paper, we propose a DL-based CSI feedback framework for BF design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the BF performance gain rather than the feedback accuracy. We apply it to two representative scenarios: single- and multi-cell systems. The CsiFBnet-s in the single-cell system is based on the autoencoder architecture, where the encoder at the user compresses the CSI and the decoder at the BS generates the BF vector. The CsiFBnet-m in the multi-cell system has to feed back two kinds of CSI: the desired and the interfering CSI. The entire neural networks are trained by an unsupervised learning strategy. Simulation results show the great performance improvement and complexity reduction of the CsiFBnet compared with the conventional DL-based CSI feedback methods.

Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI, more specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.

The stringent requirements on reliability and processing delay in the fifth-generation (5G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.

Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme data speeds and capacity. However, mmWave comes with challenges related to significantly high path loss and susceptibility to blockage. Particularly, when mmWave communication is applied to a mobile terminal device, communication can be frequently broken because of rampant hand blockage. Although a number of mobile phone companies have suggested configuring multiple sets of antenna modules at different locations on a mobile phone to circumvent this problem, identifying an optimal antenna module and a beam pair by simultaneously opening multiple sets of antenna modules causes the problem of excessive power consumption and device costs. In this study, a fast antenna and beam switching method termed Fast-ABS is proposed. In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules. As such, unmasked antenna modules and their corresponding beam pairs can be rapidly selected for switching to avoid the problem of poor quality or disconnection of communications caused by hand blockage. Thorough analysis and extensive simulations, which include the derivation of relevant Cramér-Rao lower bounds, show that the performance of Fast-ABS is close to that of an oracle solution that can instantaneously identify the best beam of other antenna modules even in complex multipath scenarios. Furthermore, Fast-ABS is implemented on a software defined radio and integrated into a 5G New Radio physical layer. Over-the-air experiments reveal that Fast-ABS can achieve efficient and seamless connectivity despite hand blockage.

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an extremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramér-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximuma posterioridecoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

Reconfigurable intelligent surface (RIS) is an emerging technology to enhance wireless communication in terms of energy cost and system performance by equipping a considerable quantity of nearly passive reflecting elements. This study focuses on a downlink RIS-assisted multiple-input multiple-output (MIMO) wireless communication system that comprises three communication links of Rician channel, including base station (BS) to RIS, RIS to user, and BS to user. The objective is to design an optimal transmit covariance matrix at BS and diagonal phase-shifting matrix at RIS to maximize the achievable ergodic rate by exploiting the statistical channel state information at BS. Therefore, a large-system approximation of the achievable ergodic rate is derived using the replica method in large dimension random matrix theory. This large-system approximation enables the identification of asymptotic-optimal transmit covariance and diagonal phase-shifting matrices using an alternating optimization algorithm. Simulation results show that the large-system results are consistent with the achievable ergodic rate calculated by Monte-Carlo averaging. The results verify that the proposed algorithm can significantly enhance the RIS-assisted MIMO system performance.

Signal recovery through coarse quantization of a linear transform output has many applications in engineering, such as channel estimation and signal detection in massive MIMO systems. A recently proposed scheme, known as generalized expectation consistent signal recovery (GEC-SR), can achieve Bayesian inference and exhibit better robustness than many existing methods. However, recovering signals with large transform matrices continue to present a computational burden for GEC-SR. In this study, we develop a novel decentralized architecture by leveraging the core framework of GEC-SR called "deGEC-SR." deGEC-SR offers excellent performance as GEC-SR and runs tens of times faster than GEC-SR. We derive the theoretical state evolution of deGEC-SR and demonstrate its accuracy using numerical results.

Millimeter-wave (mmWave) communication has become a promising option for meeting the multi-fold increase in demand for mobile data in the fifth-generation (5G) mobile broadband. However, when mmWave is applied to a mobile terminal device, communication can be frequently broken due to rampant hand blockage. Although this problem can be overcome by configuring multiple sets of subarrays at different locations, developing a fast and efficient operation that can find the best subarray and beam direction with power, complexity, and latency constraints is extremely challenging. In this study, we propose a fast antenna and beam switching method termed `Fast-ABS' that uses only one antenna module for the reception to predict the best beam of other subarrays. Through extensive simulations, we demonstrate that Fast-ABS achieves efficient and seamless connectivity under hand blockage. In addition, we implement Fast-ABS on software radios and integrate it into the 5G New Radio physical layer. Our experiments show that the performance of the proposed beam switching method is close to that of an “Oracle” solution that can instantaneously identify the best beam of other subarrays even in complex non-line-of-sight scenarios.

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

The spatial reciprocity enables the downlink channel reconstruction in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems by obtaining the frequency-independent parameters in the uplink. However, the algorithms to estimate these parameters are typically complex and time-consuming. In this paper, we regard the channel as an image and utilize you only look once (YOLO), an advanced deep learning-based object detection network, to locate the bright spots in the channel image, then the frequency-independent parameters can be estimated rapidly. Superior to the traditional algorithm that iteratively extracts the paths, YOLO can detect all the path simultaneously. Experimental results show that YOLO can greatly deplete the running time to obtain the frequency-independent parameters and reconstruct the FDD massive MIMO downlink channel with satisfactory accuracy.

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

Millimeter wave (mmWave) band, or high frequencies such as THz, has large undeveloped band of spectrum. However, wireless channels over the mmWave band usually have one or two paths only due to the severe attenuation. The channel property restricts its development in the multiple-input multiple-output (MIMO) system, which can improve throughput by increasing the spectral efficiency. Recent development in reconfigurable intelligent surface (RIS) provides new opportunities to mmWave communications. In this study, we propose a mmWave system, which used low-precision analog-to-digital converters (ADCs), with the aid of several RIS arrays. Moreover, each RIS array has many reflectors with discrete phase shift. By employing the linear spatial processing, these arrays form a synthetic channel with increased spatial diversity and power gain, which can support MIMO transmission. We develop a MIMO detector according to the characteristics of the synthetic channel. RIS arrays can provide spatial diversity to support MIMO transmission, however, different number, antenna configuration, and deployment of RIS arrays affect the bit error rate (BER) performance. We present state evolution (SE) equations to evaluate the BER of the proposed MIMO detector in the different cases. The BER performance of indoor system is studied extensively through leveraging by the SE equations. We reveal numerous insights about the RIS effects and discuss the appropriate system settings. In addition, our results demonstrate that the low-cost hardware, such as the 3-bit ADCs of the receiver side and the 2-bit uniform discrete phase shift of the RIS arrays, only moderately degenerate the system performance.

Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. Decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximuma posteriori(MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational complexity and excess of baseband data to be transferred and processed, prohibit the use of centralized processing. A promising solution is to distribute baseband data from disjoint subsets of antennas into parallel processing procedures coordinated by a central processing unit. This solution is called subarray-based architecture. In this work, we extend the application of expectation propagation (EP) principle, which effectively balances performance and practical feasibility in conventional centralized MIMO detector design, to fit the subarray-based architecture. Analytical results confirm the convergence of the proposed iterative procedure and that the proposed detector asymptotically approximates Bayesian optimal performance under certain conditions. The proposed subarray-based EP detector is reduced to centralized EP detector when only one subarray exists. In addition, we propose additional strategies for further reducing the complexity and overhead of the information exchange between parallel subarrays and the central processing unit to facilitate the practical implementation of the proposed detector. Simulation results demonstrate that the proposed detector achieves numerical stability within few iterations and outperforms its counterparts.

Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system. Since mm-wave channels are sparse in angular dimensions, exploiting the structure could reduce the number of measurements while achieving acceptable performance at the same time. Motivated by the variational Bayesian line spectral estimation (VALSE) algorithm which treats the angles as random parameters, in contrast to previous works which confine the estimate to the set of grid angle points and induce grid mismatch, this paper proposes the grid-less quantized variational Bayesian channel estimation (GL-QVBCE) algorithm for antenna array systems with low resolution ADCs. Numerical results show the near optimal performance of GL-QVBCE by comparing with the Cramèr Rao bound (CRB) and the state-of-art methods.

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAM-P, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.

Compressive scanning (CS) has exhibited its potential in improving the path tracking efficiency of millimeter wave (mmWave) systems. However, its practical performance is significantly degenerated by hardware imperfections, such as carrier frequency offset (CFO). Conventional CFO estimation methods that compare the phases of two measurements cannot be applied in CS straightforwardly because the two successive beacons are different. To overcome these problems, we propose a novel CFO-robust compressive path-tracking algorithm by introducing a two-stage CFO estimation procedure before performing coherent CS detection. Unlike conventional CFO estimates, the CFO estimate in the proposed algorithm can be obtained from the signal strength value of the received signal at the cost of a small amount of additional computation complexity. Numerical results demonstrate the superiority of the proposed algorithm in both single-path and multipath scenarios.

In this letter, we investigate the symbol detection of an uplink massive multiple-input multiple-output system impaired by phase noise at the transmitter and receiver sides. We propose a low-complexity iterative algorithm using approximate Bayesian inference based on the framework of generalized expectation consistent signal recovery to recover the symbol vector from nonlinear noisy measurements. Numerical results show that the proposed algorithm outperforms the existing algorithm and approaches the symbol error rate limit of a genie detector in high signal-to-noise ratio (SNR) regime, while the performance loss is very small in medium SNR. In particular, the complexity of proposed algorithm is quadratic, which makes it particularly suitable for large systems.

We propose a low-complexity and high-performance detector for multi-input multi-output circular filter bank multicarrier (C-FBMC) system on the basis of an orthogonal approximate message passing algorithm. Low complexity and high performance are realized through the use of a special structure of the modulation matrix and block diagonalization of the equivalent channel matrix in the frequency domain. After exploring the property of the C-FBMC modulation matrix in the frequency domain, we design an algorithm to replace the direct inversion of the matrix, thus reducing complexity considerably. The complexity per iteration of the proposed method is O(KMT log2M), where K, M, and T denote the number of subcarriers, subsymbols, and transmit antennas, respectively. Numerical simulations show that the proposed iterative algorithm is superior to conventional detectors in terms of performance and computational complexity.

Massive multiple-input multiple-output systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI.

The use of low-resolution analog-to-digital converters can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes achieving reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this paper, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework, and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

In this paper, we study the design of secure communication for time-division duplex multi-cell multi-user massive multiple-input-multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission of the users. We exploit both the received pilot's and the received data signals for uplink channel estimation. We show analytically that when both the number of transmit antennas and the length of the data vector tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station, provided their signal powers are different. This finding reveals that decreasing (instead of increasing) the desired user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this observation, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. For the special case of a single-cell single-user system with independent and identically distributed fading, the obtained expression reveals that the secrecy rate scales logarithmically with the number of transmit antennas. This is the same scaling law as for the achievable rate of a single-user massive MIMO system in the absence of eavesdroppers. The numerical results indicate that the proposed scheme achieves significant secrecy rate gains compared with alternative approaches based on matched filter precoding with artificial noise generation and null space transmission.

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. First, we estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high-estimation accuracy. The simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting the promising application of the proposed scheme in large-scale antenna array systems.

This paper aims to provide a partial discrete Fourier transform (DFT) pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as an MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. The simulation results confirm the theoretical accuracy of our analysis.

This paper faces to the frequency-division-duplex (FDD) three-dimensional (3D) multi-antenna system and proposes a novel downlink channel reconstruction scheme which costs only a small amount of overhead. Uniform planar array (UPA) is employed at the base station (BS) to exploit the 3D space. In the uplink, we first estimate the frequency-independent parameters (delay, vertical and horizontal angles of arrival) by introducing and extending the Newtonized orthogonal matching pursuit (NOMP) algorithm. Both the orthogonal matching pursuit dictionary and the Newton refinement step (NRS) are redesigned according to the spatial propagation model of the signal for the NOMP algorithm. Then, we calculate the downlink gains and feed them back to the BS. The costs for downlink pilots and uplink feedback can be very small. Finally, downlink channel is reconstructed by using the frequency-independent parameters and the downlink gains. Simulation results show that using more antennas helps improve the mean-squared error (MSE) performance of the proposed scheme and can even achieve a better MSE performance than using the linear minimum mean-square error method.

In this paper, we study the design of secure communication for time division duplexing multi-cell multi-user massive multiple-input multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission phase of the desired users. We exploit both the received pilots and data signals for uplink channel estimation. We show analytically that when the number of transmit antennas and the length of the data vector both tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station if their signal powers are different. This finding reveals that decreasing (instead of increasing) the desire user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this result, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. Numerical results indicate that under strong active attacks, the proposed design achieves significant secrecy rate gains compared to the conventional design employing matched filter precoding and artificial noise generation.

Generalized approximate message passing (GAMP) is an efficient algorithm for the estimation of independent identically distributed random signals under generalized linear model. The sum-product GAMP has long been recognized as an approximate implementation of the sum-product loopy belief propagation. In this letter, we propose to view the message passing in a new perspective of expectation propagation (EP). Comparing with the previous methods that were based on Taylor expansions, the proposed EP method could unify the derivations for the real and the complex GAMP, with a difference only in the setup of Gaussian densities.

This paper considers the beamspace channel estimation problem in three-dimensional (3D) lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a two-dimensional (2D) natural image; that is, the channel is sparse and the changes between most of adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse noninformative parameter estimator-based cosparse analysis approximate message passing for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation-maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also introduce a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-to-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this paper, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2. IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.

A receiver architecture with low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays has drawn considerable interest in the millimeter wave (mm-wave) system. Although architecture with pure one-bit ADCs has low power cost, such a system presents many challenges for synchronization, channel estimation, and power level estimation. Research has been conducted recently on the so-called mixed one-bit system, in which most antennas are equipped with one-bit ADCs, and a few have high-resolution ADCs. Despite the advantages of this system, studies on how to efficiently use high-resolution outputs to aid the one-bit system are lacking. This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs. The channel gain can be extracted from high-resolution ADCs, and the channel angle can be extracted by the combination of additional one-bit ADCs. The proposed estimator leverages the sparsity feature of the channel in mm-wave. In contrast to previous works that used compressive sensing techniques, which confine the estimate to the set of grid angle points and induce estimation bias, the proposed estimator is gridless and treats the angle as a continuous parameter. The simulation results demonstrate that the proposed method yields significantly lower mean square errors than the conventional maximum likelihood estimator. In addition, this paper investigates ways to further improve the channel estimate by arranging the locations of high-resolution ADCs. Several useful observations on system design are obtained through our analysis.

In this paper, we present the link-level simulation of a time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) system with multi-antenna user equipments (UEs). The system model of the proposed massive MIMO system is first given, based on which the general settings of our link-level simulation are presented, including the essential parameters, the LTE-like frame structure, and the discontinuous pilot allocation for all UEs. Block diagrams are then illustrated, along with the link-level data transmission procedures of both the uplink and the downlink. Thereinto, the low-complexity but well-performance channel estimation algorithm and the MIMO detector for the uplink are introduced, and two block diagonalization (BD) precoding schemes for the downlink are also illustrated. Finally, numerical results in terms of the bit error rate (BER) and the throughput are investigated and analyzed. Running time for these two proposed precoding schemes is also given for comparison of computational complexity.

This paper proposes a low-cost distributed massive multiple-input multiple-output (MIMO) system, which employs the mixed analog-to-digital converter (ADC) remote radio heads (RRHs). In particular, the RRHs with low-resolution ADCs connect with the baseband unit through wireless fronthaul while the RRHs with full-resolution ADCs connect with the baseband unit through fiber. After estimating the channel state information between RRHs and users, we derive the closed-form expressions for achievable downlink rate and energy efficiency. Based on these analytical results, we find that our distributed architecture can obtain remarkable gains compared with the centralized layout. Moreover, compared with the conventional distributed network with pure expensive full-resolution ADCs, our mixed-ADC architecture can achieve the rate requirement in a more energy-efficient and low-cost way, especially for the low rate demands. Additionally, we also present the optimal RRH assignment proportion that can maximize the energy efficiency under a fixed total number of RRHs, which can be used as guidelines for practical network configuration.

In this paper, we propose a generalized expectation consistent signal recovery algorithm to estimate the signal x from the nonlinear measurements of a linear transform output z = Ax. This estimation problem has been encountered in many applications, such as communications with front-end impairments, compressed sensing, and phase retrieval. The proposed algorithm extends the prior art called generalized turbo signal recovery from a partial discrete Fourier transform matrix A to a class of general matrices. Numerical results show the excellent agreement of the proposed algorithm with the theoretical Bayesian-optimal estimator derived using the replica method.

Orthogonal frequency division multiplexing (OFDM) has been widely used in communication systems operating in the millimeter wave (mmWave) band to combat frequency-selective fading and achieve multi-Gbps transmissions, such as the IEEE 802.15.3c and the IEEE 802.11ad. For mmWave systems with ultra high sampling rate requirements, the use of low-resolution analog-to-digital converters (ADCs) (i.e., 1-3 bits) ensures an acceptable level of power consumption and system costs. However, orthogonality among subchannels in the OFDM system cannot be maintained because of the severe nonlinearity caused by low-resolution ADC, which renders the design of data detector challenging. In this paper, we develop an efficient algorithm for optimal data detection in the mmWave OFDM system with low-resolution ADCs. The analytical performance of the proposed detector is derived and verified to achieve the fundamental limit of the Bayesian optimal design. On the basis of the derived analytical expression, we further propose a power allocation (PA) scheme that seeks to minimize the average symbol error rate. In addition to the optimal data detector, we also develop a feasible channel estimation method, which can provide high-quality channel state information without significant pilot overhead. Simulation results confirm the accuracy of our analysis and illustrate that the performance of the proposed detector in conjunction with the proposed PA scheme is close to the optimal performance of the OFDM system with infinite-resolution ADC.

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance.

This paper investigates the design of precoders for single-user multiple-input multiple-output (MIMO) channels, and, in particular, for finite-alphabet signals. Based on an asymptotic expression for the mutual information of channels exhibiting line-of-sight components and rather general antenna correlations, precoding structures that decompose the general channel into a set of parallel subchannel pairs are proposed. Then, a low-complexity iterative algorithm is devised to maximize the sum mutual information of all pairs. The proposed algorithm significantly reduces the computational load of existing approaches with only minimal loss in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unmanageable with existing solutions. Most importantly, the proposed solution does not require instantaneous channel state information (CSI) at the transmitter, but only statistical CSI.

In this paper, we consider a heterogeneous network in which a macro-cell tier is overlaid with a very dense tier of small cells. The macro-cell base station (BS) that applies a massive MIMO scheme not only serves the macro user equipment but also provides a wireless backhual for small-cell access points (APs). These APs serve their associated small-cell user equipment. A reverse time division duplex transmission protocol is utilized. To avoid interference toward the APs in the uplink (UL), regularized zero-forcing precoding combined with a projection technique is utilized at the BS in the downlink (DL). We derive deterministic expressions for ergodic UL and DL sum rates (SRs) under the assumption that perfect channel state information is available and use these results to optimize the spectrum division between radio access links and the wireless backhaul. Simulation results suggest that the deterministic SR approximations are accurate and that system performance can be significantly improved through the optimization of spectrum division.

In this paper, we investigate the design of multiple-input multiple-output single-user precoders for finite-alphabet signals under the premise of statistical channel-state information at the transmitter. Based on an asymptotic expression for the mutual information of channels exhibiting antenna correlations, we propose a low-complexity iterative algorithm that radically reduces the computational load of existing approaches by orders of magnitude with only minimal losses in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unwieldy in existing solutions.

In this study, we propose a generalized turbo signal recovery algorithm to estimate a signal from quantized measurements, in which the sensing matrix is a row-orthogonal matrix, such as the partial discrete Fourier transform matrix. The state evolution of the proposed algorithm is derived and is shown to be consistent with that obtained with the replica method. Numerical experiments illustrate the excellent agreement of the proposed algorithm with theoretical state evolution.

© 2015 IEEE. In this paper, we study the multiple-input multiple-output wiretap channel for simultaneous wireless information and power transfer, in which there is a base station (BS), an information-decoding (ID) user, and an energy-harvesting (EH) user. The messages intended to the ID user is required to be kept confidential to the EH user. Our objective is to design the optimal transmit covariance matrix at the BS for maximizing the ergodic secrecy rate subject to the harvested energy requirement for the EH user exploiting only statistical channel state information at the BS. To this end, we begin by deriving an approximation for the ergodic secrecy rate using large-dimensional random matrix theory and the method of Taylor series expansion. This approximation enables us to derive the asymptotic-optimal transmit covariance matrix that achieves the tradeoff for ergodic secrecy rate and harvested energy. The simulation results are provided to verify the accuracy of the approximation and show that a bigger rate-energy region can be achieved when the Rician factor increases or the path loss exponent decreases. We also show that when the transmit correlation increases or the distance between the eavesdropper and the BS decreases, the harvested energy will be increased, while the achieved ergodic secrecy rate decreases.

The hardware cost and power consumption of a massive multiple-input multiple-output (MIMO) system can be remarkably reduced by using a very low-resolution analog-to-digital converter (ADC) unit in each antenna. However, such a pure low-resolution ADC architecture complicates parameter estimation problems. These issues can be resolved and the potential of a pure low-resolution ADC architecture can be achieved by applying a mixed ADC architecture, whose antennas are equipped with low-precision ADCs, while few antennas are composed of high-precision ADCs. In this paper, a unified framework is presented to develop a family of detectors on a massive MIMO uplink system through probabilistic Bayesian inference. Our basic setup comprises an optimal detector, which is developed to provide a minimum mean-squared-error estimate on data symbols. Considering that highly nonlinear steps are involved in quantization, we also investigate the potential for complexity reduction on an optimal detector by postulating a common pseudo-quantization noise model. We provide asymptotic performance expressions, including mean squared error and bit error rate for optimal and suboptimal MIMO detectors. These expressions can be evaluated rapidly and efficiently. Thus, they can be used for system design optimization.

This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multipleoutput (MIMO) system by using Bayesian inference. The Bayesoptimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information of the interfering signals. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

The use of low precision (e.g., 1 - 3 bits) analog-to-digital converters (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be very large just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the MIMO system with low precision ADCs is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the MIMO system.

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

In this paper, we investigate the design of linear precoders for the multiple-input-multiple-output (MIMO) multiple access channel (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large system limit) expression for the weighted sum rate (WSR) of the MIMO MAC with finite alphabet inputs and Weichselberger's MIMO channel model. Subsequently, we obtain the optimal structures of the linear precoders of the users maximizing the asymptotic WSR and an iterative algorithm for determining the precoders. We show that the complexity of the proposed precoder design is significantly lower than that of MIMO MAC precoders designed for finite alphabet inputs and instantaneous CSI. Simulation results for finite alphabet signaling indicate that the proposed precoder achieves significant performance gains over existing precoder designs.

In this paper, we consider a cognitive radio (CR) network in which a secondary multiantenna base station (BS) attempts to communicate with multiple secondary users (SUs) using the radio frequency spectrum that is originally allocated to multiple primary users (PUs). Here, we employ partially-projected regularized zero-forcing (PP-RZF) precoding to control the amount of interference at the PUs and to minimize inter-SUs interference. The PP-RZF precoding partially projects the channels of the SUs into the null space of the channels from the secondary BS to the PUs. The regularization parameter and the projection control parameter are used to balance the transmissions to the PUs and the SUs. However, the search for the optimal parameters, which can maximize the ergodic sum-rate of the CR network, is a demanding process because it involves Monte-Carlo averaging. Then, we derive a deterministic expression for the ergodic sum-rate achieved by the PP-RZF precoding using recent advancements in large dimensional random matrix theory. The deterministic equivalent enables us to efficiently determine the two critical parameters in the PP-RZF precoding because no Monte-Carlo averaging is required. Several insights are also obtained through the analysis.

In this paper, we investigate the design of linear precoders for multiple-input multiple-output (MIMO) multiple access channels (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large-system limit) weighted sum rate (WSR) expression for the MIMO MAC with finite alphabet inputs and general jointly-correlated fading. Subsequently, we obtain necessary conditions for linear precoders maximizing the asymptotic WSR and propose an iterative algorithm for determining the precoders of all users. In the proposed algorithm, the search space of each user for designing the precoding matrices is its own modulation set. This significantly reduces the dimension of the search space for finding the precoding matrices of all users compared to the conventional precoding design for the MIMO MAC with finite alphabet inputs, where the search space is the combination of the modulation sets of all users. As a result, the proposed algorithm decreases the computational complexity for MIMO MAC precoding design with finite alphabet inputs by several orders of magnitude. Simulation results for finite alphabet signalling indicate that the proposed iterative algorithm achieves significant performance gains over existing precoder designs, including the precoder design based on the Gaussian input assumption, in terms of both the sum rate and the coded bit error rate.

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size N is higher than the observation dimension M. Moreover, this estimator performs poorly when the sample size is not sufficiently large. To address this problem, we propose a new shrinkage LMMSE estimator. The proposed estimator performs efficiently over a wide range of observation dimensions and sample sizes. In contrast to existing methods, the proposed estimator can be applied if M ≥ N. Even if M <; N, the proposed estimator performs more efficiently than existing estimators.

Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution.

In this paper, we analyze the ergodic sum-rate of a multi-cell downlink system with base station (BS) cooperation using regularized zero-forcing (RZF) precoding. Our model assumes that the channels between BSs and users have independent spatial correlations and imperfect channel state information at the transmitter (CSIT) is available. Our derivations are based on large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the BS and users approach to infinity with some fixed ratios. In particular, a deterministic equivalent expression of the ergodic sum-rate is obtained and is instrumental in getting insight about the joint operations of BSs, which leads to an efficient method to find the asymptotic-optimal regularization parameter for the RZF. In another application, we use the deterministic channel rate to study the optimal feedback bit allocation among the BSs for maximizing the ergodic sum-rate, subject to a total number of feedback bits constraint. By inspecting the properties of the allocation, we further propose a scheme to greatly reduce the search space for optimization. Simulation results demonstrate that the ergodic sum-rates achievable by a subspace search provides comparable results to those by an exhaustive search under various typical settings.

In this paper, we investigate the asymptotic mutual information expression and the capacity-achieving input covariance matrices for the coordinated multi-point multiple-input multiple-output (MIMO) antenna channels. In particular, it is considered that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our derivations are based on novel techniques from large dimensional random matrix theory. In contrast to previous studies, we consider a general model in which the correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian distributed. We show that the asymptotic capacity is invariant to all types of fading distribution. As such, the asymptotic mutual information expression is robust and has wide applicability.

In this paper, we consider the multi-cell downlink system with multiple base stations (BSs) and multiple single antenna users employing the BS cooperation. The channels between BSs and users have independent spatial correlations. All BSs jointly implement the regularized zero-forcing based on imperfect channel estimation. By utilizing the large dimensional random matrix theory, we first obtain the limiting distribution of the eigenvalues for a class of random Hermitian matrix. Based on this result, we derive a deterministic equivalent of ergodic sum rate for the multi-cell downlink system and obtain the optimal regularization parameter in the special case employing maximize the deterministic equivalent of ergodic sum rate. Numerical results show that the deterministic equivalent are accurate even for finite number of antenna and the channel estimation parameter almost do not affect the accuracy of the approximation.

In this paper, we focus on a general coordinated multi-point (CoMP) multiple input multiple-output (MIMO) system consisting of multiple users and multiple base stations (BSs) equipped with multiple antennas, respectively. An asymptotic ergodic mutual information expression and the capacity-achieving input covariance matrices for the system are derived employing novel techniques from large dimensional random matrix theory (RMT). The asymptotic regime is based on the assumption that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our contributions are to extend the previous results to the general channel model with two-sided spatial correlation and line-of-sight (LOS), in which the transmit and receive correlation matrices are both generally nonnegative definite and the channel entries are non-Gaussian distributed. Simulations show that the asymptotic capacity is accurate even for finite number of antenna and invariant to all types of fading distribution.

In this paper, we address statistically robust transmit design problems that maximize the average mutual information of amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-hop relay channels having perfect channel state information at the receiver (CSIR) and statistical channel state information at the transmitter (CSIT). In the selected scenario, the source, relay, and destination terminals are equipped with correlated antennas where a direct link between the source and the destination terminals can be found. Moreover, the statistical CSIT consists of the channel means and covariance matrices of various links. The design problem is taken into account for regimes in large systems because there are no closed-form expressions of the average mutual information in the MIMO relay channel. The contribution of this study includes the derivation of asymptotic mutual information expressions for the MIMO relay channel in the large system limit. Additionally, an efficient optimization algorithm based on the asymptotic mutual information is proposed in order to find the asymptotically optimal source covariance matrix and the relay amplifying matrix. Numerical simulation results show that the new approach achieves indistinguishable performance compared to those of the maximization approaches in finite-dimensional systems, even for a small number of antennas at each link. The impact of the CSI of various links on the throughput of the MIMO relay channel is studied using the new approach.

In this paper, we propose a concept of parasitic communication system via relaying. The proposed system is suitable for improving the throughput of some users who are far from the base station or have numerous obstructions in their communication links. In the parasitic communication system, the users communicate to the base station by following the original setting of the standards. Only a slight modification at the retransmission mode is required. Therefore, the proposed approach is backward compatible to several existing systems.

In this paper, we study the capacity-achieving input covariance matrices for the jointly-correlated (or the Weichsel-berger) Rician fading multiple-input multiple-output (MIMO) antenna channel when perfect channel state information (CSI) is known at the receiver while only statistical CSI at the transmitter is available. Unlike the Kronecker model, such jointly-correlated MIMO channel accounts for the correlation coupled between the two ends and has been shown to be most accurate for representing real channels. Our contribution includes the expression for the asymptotic mutual information for the jointly-correlated Rician fading MIMO channel in the large-system regime in which the numbers of antennas at the transmitter and receiver go to infinity with a fixed ratio. Based on this expression, an efficient algorithm is also proposed to obtain the capacity-achieving input covariance matrix. Simulation results demonstrate that even for a moderate number of antennas at each end, the proposed scheme provides undistinguishable results as those obtained by the highly-complex stochastic programming (or Monte-Carlo based) approach.

In this paper, we study the capacity-achieving input covariance matrices for the multiuser multiple-input multiple-output (MIMO) uplink channel under jointly-correlated Rician fading when perfect channel state information (CSI) is known at the receiver, or CSIR while only statistical CSI at the transmitter, or CSIT, is available. The jointly-correlated MIMO channel (or the Weichselberger model) accounts for the correlation at two link ends and is shown to be highly accurate to model real channels. Classically, numerical techniques together with Monte-Carlo methods (named stochastic programming) are used to resolve the problem concerned but at a high computational cost. To tackle this, we derive the asymptotic sum-rate of the multiuser (MU) MIMO uplink channel in the large-system regime where the numbers of antennas at the transmitters and the receiver go to infinity with constant ratios. Several insights are gained from the analytic asymptotic sum-rate expression, based on which an efficient optimization algorithm is further proposed to obtain the capacity-achieving input covariance matrices. Simulation results demonstrate that even for a moderate number of antennas at each link, the new approach provides indistinguishable results as those obtained by the complex stochastic programming approach.

This paper studies the asymptotic properties of amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-hop relay channels, where the source terminal (ST), relay terminal (RT) and destination terminal (DT) are equipped with a number of correlated antennas and there is a direct link between the ST and DT. The analysis is based on the Kronecker correlated fading model where the correlation properties of the MIMO channel are modeled at the transmitter and receiver ends separately. Our first main contribution is the derivation of the asymptotic mutual information expressions for the MIMO-AF relay channel for any given signaling input distributions (not necessarily Gaussian) in the large-system regime, which is particularly relevant if practical modulation schemes are used. In addition to the formula that uses joint optimal decoding (JOD) at the DT, we also obtain the asymptotic result for a more practical system which performs separate detection and decoding (SDD) (i.e., spatial detection followed by a temporal error-correction decoder). Another major contribution of this paper is an efficient algorithm that can compute the optimal source covariance matrix and the relay precoding matrix to maximize the asymptotic mutual information of the MIMO-AF relay system if channel covariance information (CCI) is known at the ST and RT.

This paper considers the relay subset selection problem in a two-way amplify-and-forward relay network, where each user is equipped with multiple antennas and each relay is equipped with a single antenna. In a two-way channel, two users exchange data with each other using a relay subset. With the optimal relay subset selection scheme, the system can reduce the hardware burden while preserving the diversity benefit. However, the optimal relay subset selection algorithm requires an exhaustive search of all possible combinations to determine the optimum subset in order to achieve the maximum sum-rate. Thus, this results in high computational complexity. In order to reduce the computational load while still maximizing the achievable rate, a cross-entropy (CE) method is introduced to search for the optimal relay subset. Simulation results indicate that the proposed CE method can guarantee a result that is within 98% of the optimum sum-rate obtained by the exhaustive search method with low computational complexity.

In this paper, we study the transmit precoding problem for multiple-input multiple-output (MIMO) multiple-access (MA) cellular systems employing cooperative base stations (BSs) and amplify-and-forward (AF) relaying where the antennas at the user equipments (UEs), relay terminals (RTs), and BSs are correlated. It is assumed that BSs have perfect channel state information (CSI) while user equipments (UEs) and RTs only have the channel covariance information (CCI). We derive the asymptotic sum-rate expressions for the MIMO-MA cellular systems for any given signaling input distributions in the large-system limit. Using the asymptotic results, we propose an efficient algorithm to determine the asymptotic optimum transmit matrices for the UEs and RTs that can maximize the sum-rate of the MIMO-MA cellular systems if CCI is exploited at the UEs and RTs.

Cooperation and relaying are emerging technologies as flexible solutions for improving the capacity of cellular systems. Motivated by this, we investigate the asymptotic achievable sum-rate for multiple-input multiple-output (MIMO) multiple-access cellular systems in the uplink employing cooperative base stations (BSs) and amplify-and-forward (AF) relaying. In our model, the antennas at the user equipments (UEs), relay terminals, and BSs are correlated and there are direct links between UEs and BSs. Using the replica method, we derive the large-system sum-rate for the uplink system using joint optimal decoding (JOD) as well as separate detection and decoding (SDD) and show that each UE's performance can be characterized by a vector Gaussian channel with an appropriate vector input, an effective channel gain, and a vector Gaussian noise. Based on the asymptotic result, we also devise an efficient algorithm to determine the asymptotic optimum transmit matrices for the UEs and relays that can maximize the sum-rate of the MIMO multiple-access cellular network if the spatial channel covariance information (CCI) is available and exploited at the UEs and the relay terminals.

This paper considers the use of the sign-selection technique to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. In the sign-selection technique, a set of subcarrier signs is selected to significantly reduce the PAPR statistics for OFDM signals. However, the considerable computational complexity for an exhaustive search over all combinations of 2Ldifferent sign patterns is a potential problem for practical implementation, whereLis the number of subcarriers. To reduce the computational complexity while still improving the PAPR statistics, we introduce the quantum-inspired evolutionary algorithm (QEA), an effective algorithm that solves various combinatorial optimization problems, to determine a good set of subcarrier signs. The computer simulation results show that as compared to the conventional selected mapping (SLM) scheme and the cross-entropy (CE) method, the proposed QEA obtains the desirable PAPR reduction with low computational complexity.

This letter considers the use of the tone injection (TI) scheme to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. TI is a distortionless technique that can reduce PAPR significantly without data rate loss and does not require the extra side information. However, the optimal TI scheme requires an exhaustive search over all combinations of possible permutations of the expanded constellation, which is a potential problem for practical applications. To reduce the computational complexity while still improving PAPR statistics, this letter first formulates the PAPR reduction with TI scheme as a particular combinatorial optimization problem. Next, it proposes the application of the cross-entropy (CE) method to solve the problem. Computer simulation results show that the proposed CE method obtains the desired PAPR reduction with low computational complexity.

Using the replica method, originally developed in statistical physics, we derive the asymptotic mutual information (MI) of a spatially correlated Rician multiple-input multiple-output (MIMO) multiple-access (MA) channel for any given signaling input distributions (not necessarily Gaussian) in the large-system regime where the numbers of antennas at the transmitters and the receiver go to infinity with a constant ratio. In addition to the result that uses joint optimum decoding (JOD) at the central receiver, we also obtain asymptotic characteristics of a more practical system which performs separate detection and decoding (SDD) (i.e., spatial detection followed by a temporal error-correction decoder). Simulation results reveal that the asymptotic results provide promising estimates for the average performance metrics (e.g., the error probability and the MI) even with only a few antenna elements at the transceivers.

This paper aims to analyze the throughput of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network. Some recent research studies showed that, in the concerned scenario, the joint processing of all the received signals by access point (AP) cooperation can dramatically enhance the system throughput because of the macrodiversity. However, in practice, the channel estimation quality for the paths from the macrodiversity providers is usually poor. Unfortunately, existing literature on the performance of the joint processing in the MIMO cellular networks is deficient in channel estimation errors. To fill this literature gap, this paper provides an analytical framework for calculating the large-system throughput of a MIMO cellular network with channel estimation errors. Unlike most large-system results, analytical results of this study can be applied in the scenarios with any given input distributions (not necessarily Gaussian) from transmitters. In addition, several issues including 1) how much of the coherence interval should be spent on training and 2) the impact of AP cooperation are highlighted by the analytical results.

Inspired by the success of the low-density parity-check (LDPC) codes in the field of error-control coding, in this paper we propose transforming the downlink multiuser multiple-input multiple-output scheduling problem into an LDPC-like problem using the normal graph. Based on the normal graph framework, soft information, which indicates the probability that each user will be scheduled to transmit packets at the access point through a specified angle-frequency sub-channel, is exchanged among the local processors to iteratively optimize the multiuser transmission schedule. Computer simulations show that the proposed algorithm can efficiently schedule simultaneous multiuser transmission which then increases the overall channel utilization and reduces the average packet delay.

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i.e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

This paper studies the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where spatial correlations at both the transmitters (i.e., mobile stations) and the central receiver (i.e., base station) exist, and more than one multiantenna sets are employed at the receiver to provide macrodiversity. The sense of asymptotic behavior we consider is the large-system limit in which the numbers of antennas at each mobile station and antenna set go to infinity with their ratios fixed. Using the replica method, we derive analytical solutions to the asymptotic spectral efficiency (SE) of the MIMO-MA systems for any given input distributions (not necessarily Gaussian) from the mobile transmitters. Our results can be regarded as the generalization of many previously known results for degenerate cases. Another contribution of this paper is an efficient algorithm to determine the asymptotic optimum transmit-signal covariance matrices that can maximize the SE of the MIMO-MA network within the period in which the spatial channel covariance information (CCI) can be considered static, and assuming that only CCI is available at the transmitters

While the capacity of a single-user, point-to-point, multiple-input multiple-output (MIMO) channel has been well known, the achievable capacity of a MIMO channel in the presence of other co-channel users is much less understood. One such important scenario is the multiple-access (MA) channel where communication occurs from many uncoordinated mobile users to a common base station receiver (i.e., multipoint-to-point). Unlike previous studies whose emphases were on the idealized spatially uncorrelated channels with Gaussian signaling inputs from users, this paper derives a general analytical expression for the asymptotic (in the sense of large-system limit) sum-rate of a MIMO-MA system where the transmitters and the receiver can have different spatial correlations, and the users' inputs are not necessarily Gaussian. In addition to the sum-rate formula that assumes optimal joint decoding at the base station, we also derive the asymptotic sum-rate of a more practical system which performs separate decoding (multiuser detection followed by a bank of temporal error-correction decoders). Our analytic formulae are important in that they reveal the sum-rate one's system can achieve given the spatial correlation structures at the transmitters and receiver, and the input signal distributions. For special cases that users are homogeneous or users have Gaussian inputs, our results degenerate to previously published results. Furthermore, through computer simulations, we see that the proposed asymptotic solution gives good estimates for the ergodic sum-rate of the systems even with only a few antenna elements at each transmitter and receiver

For antenna-array-based multiple-input multiple- output orthogonal-frequency-division-multiplexing (MIMO-OFDM) wireless systems, gain in channel throughput reduced through sufficient feedback of the channel state information (CSI) is significant, particularly when the number of transmit antennas is larger than the number of receive antennas. In this letter, we demonstrate that, in such scenarios, (1) the CSI of each OFDM sub-carrier can be parameterized into a short bit stream by a proposed low-complexity QR decomposition on the corresponding MIMO channel matrix, (2) the overall CSI can be reliably represented by a proposed parameter interpolation on the above bit streams of only a fraction of sub-carriers, and (3) a MIMO-OFDM system with a low-rate CSI feedback parameterized above can provide a channel throughput comparable to the channel capacity.

With extrinsic information transfer (EXIT) chart code design tool, people can more easily design a capacity-approaching low-density parity-check (LDPC) codes for antenna-array-based multiple-input multiple-output (MIMO) systems. However, extremely time-consuming Monte-Carlo simulations are required to evaluate the statistics used in the EXIT charts, which makes capacity-approaching LDPC code design almost impossible for large-scale MIMO systems. By making use of large-system performance analysis technique, we propose a time-efficient code design approach which eliminates the necessity of time-consuming Monte Carlo simulations and then makes capacity-approaching LDPC code design possible for large-scale MIMO systems. Numerical experiments show that the so-constructed LDPC codes attain comparable bit error rate performance with existing well-designed LDPC codes. In addition, we demonstrate that redesigning LDPC code is very helpful only when the ratio of number of transmit and that of receive antennas of a MIMO system varies.

In this paper, we are interested in performance analyses of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where Wyner's linear cellular array model is adopted. Notwithstanding some recent results have shown that, in the Wyner's model, the joint processing of the entire received signal with access point (AP) cooperation can dramatically enhance the system throughput because of the macrodiversity. However, in practice, the channel estimation quality from the providers of the macrodiversity is usually poor. Furthermore, existing literatures do not take channel estimation error into account. The channel estimation error provides us the motive to derive analytical solutions to the large-system throughput (or channel sum-rate) of the MIMO cellular network with imperfect channel estimations. Unlike the most large-system results, our derived results can not only be applied in the scenarios with any given input distributions (not necessarily Gaussian) from the mobile transmitters, but also clarify the performance of the network which was originally involved. Several issues we address including (1) how much of coherence interval should be spent for training and (2) the impact of AP cooperation is highlighted through the analytical results.

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearl's belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.

With a unified approach, this paper investigates the asymptotic performance or, equivalently, the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations. Using the replica method originally developed for statistical physics, we provide analytical solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint decoding or various suboptimum spatial equalizers followed by a bank of temporal decoders. Important physical meanings revealed though the analytical solutions to those more practical combinations, such as how the input-output mutual information is affected by the channel spatial correlations, are highlighted along our derivation. Moreover, we provide a novel waterfilling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter.

By considering the large-system regimes, we analyze the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network in the presence of spatial correlation at the transmitters (i.e., mobile stations) and the receiver (i.e., base station) with particular emphasis on the receive antennas utilizing pattern diversity. Using the replica method originally developed in statistical physics, we are able to derive analytical solutions to the spectral efficiency of the MIMO-MA systems for any given arbitrary input signal distributions at the transmitters assuming the information-theoretic optimum decoding at the receivers. Our results are general in that they encompass lots of previously published results for degenerate cases. In addition, based on the asymptotic solution, we propose a computation-efficient algorithm to determine the asymptotic optimum transmit signal covariance matrices that can maximize the spectral efficiency of the MA network when only the slow-varying channel spatial covariance information is available at the transmitters.

Most researches into multiple-input multiple-output (MIMO) antenna systems have aimed to determine the capacity-achieving input covariance given certain degree of channel state information (CSI) at the transmitter side. In practice, however, it is much preferred to find the input signal covariance that requires the least average transmit power to support a given rate based on only long-term channel information. In this paper, we analyze the characteristics of the optimal (in the sense of power-saving) input covariance for spatially correlated MIMO channels where only the long-term (slow-varying) channel spatial covariance information is available at the transmitter. Sufficient and necessary conditions of the optimal input covariance are derived. By considering the large-system regimes, we devise an efficient iterative algorithm to compute the asymptotic optimal power-saving input covariance. Remarkably, simulation results will demonstrate that the asymptotic solution is very effective in that it gives promising results even for MIMO systems with only a few antennas at the transmitter and the receiver.

Recently, several published papers have been drawing attention to multicarrier code division multiple access (MCCDMA) systems, especially their applications in the field of broad-band wireless communications. In such applications, existing suboptimal multiuser detectors (MUDs) usually do not achieve acceptable performance. Unfortunately, it is NP-hard to implement an optimum MUD algorithm. In this paper, to make the optimum MUD feasible, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems based on Pearl's belief propagation (BP) scheme. The BP-based MUD algorithm can be implemented in a practical time scale. The analytical dynamic biterror-rate (BER) performance of the BP-based iterative detector can be traced accurately and its BER in the stationary state is shown to achieve the performance obtained by the optimum MUD algorithm.

Asymptotic Analysis of MIMO Wireless Systems With Spatial Correlation at the Receiver With a unified approach, this paper investigates the asymptotic performance, or equivalently, the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations. Using the replica method originally developed for statistical physics, we provide analytical solutions to the input–output mutual information of MIMO systems at the transmit side, with arbitrary inputs, and at the receive side, with either the optimum space–time joint decoding, or various suboptimum spatial equalizers followed by a bank of temporal decoders. Important physical meanings revealed though the analytical solutions to those more practical combinations, such as how the input–output mutual information is affected by the channel spatial correlations, are highlighted along our derivation. Moreover, we provide a novel waterfilling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter.

Exploiting the Fourier basis structure both in the space and the time domains, we develop a low-complexity multiuser space-time coding scheme, multiuser (MU) angle-frequency coding scheme (MU-AFCS), to properly schedule the data streams of each user with respect to its corresponding angle-frequency channel structure for downlink wireless systems. With the proposed approach, a large amount of space resource left over by one user, in clustered multipath wireless channels, can be easily identified and used by the others without serious signal collision in the space domain. In doing so, low channel capacity resulting from poor channel structures in systems, allowing only single-user transmission at a time, can be greatly boosted. The key advantage of this approach is that only limited feedback of channel state information to the transmitter is required while multiuser macro-diversity is sufficiently exploited. In addition, the complexity of the proposed approach is much lower than that of the existing ones serving similar purposes. Through theoretical analyses and computer simulations, we demonstrate that the MU-AFCS can significantly increase the channel capacity as compared to the traditional orthogonal resource division MU multiple-input multiple-output (MIMO) systems.

We use the replica method originally developed in statistical physics to investigate the asymptotic sum-rate of a Gaussian antenna-array-based multiple-input multiple-output (MIMO) multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. The asymptotic solution is not only rigorously valid for systems with large array sizes, but it also produces highly accurate ergodic results for systems with only a few antenna elements at each transmitter and receiver. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.

We use the replica method originally developed in statistical physics to investigate the asymptotic sum-rate of a Gaussian antenna-array-based multiple-input multiple-output (MIMO) multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. The asymptotic solution is not only rigorously valid for systems with large array sizes, hut it also produces highly accurate ergodic results for systems with only a few antenna elements at each transmitter and receiver. This otters the asymptotic solution important practical values in analyzing and designing a MIMO multiple access system that makes best use of the wireless channel structure. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.

In a unified approach, this paper investigates the asymptotic performance, or equivalently the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations at both the transmitter and the receiver. Using the replica method originally developed for statistical physics, we provide closed-form solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with either Gaussian inputs or various nonGaussian arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint-decoding, or various suboptimum spatial equalizers followed by a bank of temporal decoders. Among all these combinations, in Part I, we focus on the performance analysis of the MIMO systems with the optimum space-time joint-decoding, i.e., the information-theoretic optimum decoding. Based on the framework developed in this paper, some known performance results are recovered and some new performance results are discovered. Furthermore, we provide a novel water-filling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter. In Part II, we focus on the performance analysis of the MIMO systems with various suboptimum spatial equalizers, each followed by a bank of temporal decoders.

For pt.I, see ibid., p.3504-8 (2004). In Part I of this paper, we analyzed the asymptotic mutual information of the joint-decoding MIMO systems. Here in Part II, we focus on the performance analysis of the MIMO systems with various separate decoding schemes, i.e., with various suboptimum spatial equalizers followed by a bank of temporal decoders. Among the suboptimum spatial equalizers, under the proposed framework, we cover both 1) linear spatial equalizers such as linear MMSE spatial equalizer, linear zero-forcing spatial equalizer, and matched-filter equalizer, and 2) nonlinear spatial equalizers such as individually optimal spatial equalizer and marginal optimal spatial equalizer. Closed-form solutions are derived for all the possible combinations of 1) various transmit-side arbitrary input distributions and 2) the receive-side decoding schemes listed above. Important physical meanings revealed though the closed-form solutions to those more practical combinations, as how the input-output mutual information is affected by the transmit-side and the receive-side channel spatial correlations, are highlighted along our derivation.

This paper derives a general explicit expression for the asymptotic (in the sense of large-system limit) spectral efficiency of a multiple-input multiple-output multiple-access channel (MIMO-MAC) where the transmitters and the receiver may have different spatial correlations, and the users' inputs are not necessarily Gaussian. In addition to the spectral efficiency formula that assumes optimal joint-decoding at the base station, we also derive the asymptotic spectral efficiency of a more practical system which performs separate-decoding (multiuser detection followed by a bank of temporal error-correction decoders).

Systems that employ multiple antennas in both the transmitter and the receiver of a wireless system have been shown to promise extraordinary spectral efficiency. With full channel knowledge at the transmitter and receiver, Raleigh and Cioffi (1998) proposed a spatio-temporal coding scheme, discrete matrix multitone (DMMT), to achieve asymptotically optimum multiple-input-multiple-output (MIMO) channel capacity. The DMMT can be regarded as an extension of the discrete multitone for a digital subscriber lines (DSL) system to the MIMO wireless application. However, the DMMT is basically impracticable in nonstationary wireless environments due to its high-computational complexity. Exploring second-order statistics, we develop an efficient adaptive blind coding scheme for a high-capacity time-division duplexing (TDD) system with slow time-varying frequency-selective MIMO channels. With this method, neither a training sequence nor feedback of channel information is required in the proposed blind approach. Besides, the computational complexity of the proposed scheme is significantly lower than that of the coding scheme described by Raleigh and Cioffi. Simulation results show that the proposed architecture works efficiently in indoor wireless local area network applications.