We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using various forms of PSI. The hierarchical block orthogonal matching pursuit with PSI (HiBOMP-P) algorithm is designed in a recursive form to reliably recover hierarchically block-sparse signals. We derive exact recovery conditions (ERCs) measured by the mutual incoherence property (MIP), wherein hierarchical MIP concepts are proposed, and further develop reconstructible sparsity levels to reveal sufficient conditions for ERCs. Leveraging these MIP analyses, we present several extended insights, including reliable recovery conditions in noisy scenarios and the optimal hierarchical structure for cases where sparsity is not equal to zero. Our results further confirm that HCS offers improved recovery performance even when the prior information does not overlap with the true support set, whereas existing methods heavily rely on this overlap, thereby compromising performance if it is absent.

Larger antenna arrays, combined with higher transmission frequencies, are prospective in fulfilling the demands of the sixth-generation (6G) communications, enabling a 10-fold increase in overall spectral efficiency. However, such configurations give rise to near-field effects, requiring spherical rather than planar wave modeling. In practical multi-user communications, it is typical that part of the user equippments (UEs) resides in the near-field region, while others are located in the far-field region, thereby leading to a unified near/far-field scenario. Conventional codebooks tailored to either regime alone thus become mismatched, resulting in notable spectral efficiency degradation. In view of this, the ringwise codebook based on the slope-intercept formulation is proposed to address the unified near/far-field communication scenario. Specifically, the slope-intercept domain is first illustrated as the foundation of our codebook design, where the correlation between near/far-field channel steering vectors is exploited by mapping the angle-distance into the slope-intercept parameters. In the slope-intercept domain, the correlation pattern of steering vectors exhibits a dual triangle structure, supported by rigorous analyses on axial symmetry and correlation width, which paves the way for the subsequent codebook development. Secondly, the ringwise codebook is proposed where all UEs coarsely estimate its own slope parameter, based on which ring-wise codebooks composed of orthogonal codewords derived from the slope-intercept domain are constructed for different UEs. The resulting codebooks are then fed back to the base station through specific slope parameters, which are leveraged in the subsequent precoding procedure. Finally, rigorous analysis demonstrates that both the computational complexity and hardware cost of the proposed ringwise codebook design are acceptable, while numerical results validate its effectiveness and feasibility, achieving gains in both spectral efficiency and complexity compared to conventional counterparts.

Channel acquisition for precoding design in massive multiple-input multiple-output (MIMO) systems faces increasingly prominent challenges due to the huge overhead caused by channel feedback and transmission of pilot signals. In response, directly mapping location information to precoding without channel feedback has emerged as a feasible and efficient solution. However, existing deep learning-based methods often struggle to map low-dimensional positional data to high-dimensional precoding vectors accurately. To address this challenge, we propose a spatially adaptive mapping network (SAM-Net) that enhances feature extraction and representation by leveraging transposed convolution and incorporating spatial information for fine-grained adaptive adjustments. While SAM-Net improves mapping performance, its complexity also increases. Therefore, we introduce a lightweight spatially adaptive mapping network (LSAM-Net) that combines average pooling and max pooling to reduce the number of parameters and computational complexity while it maintains near-optimal performance. Evaluation results demonstrate that SAM-Net achieves superior and stable mapping performance, while LSAM-Net offers a more lightweight alternative with minimal loss in performance.

With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates. However, the primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi. In this paper, we address the challenge by designing channel access mechanisms and power control strategies to mitigate interference and ensure fair coexistence. First, we propose a novel collaborative channel access (CCHA) mechanism that integrates channel access with resource allocation through collaborative interactions between base stations (BS) and SL-U users. This mechanism ensures fair coexistence with incumbent systems while improving resource utilization. Second, to further enhance the performance of the coexistence system, we develop a cooperative subgoal-based hierarchical deep reinforcement learning (C-GHDRL) algorithm framework. The framework enables SL-U users to make globally optimal decisions by leveraging cooperative operations between the BS and SL-U users, effectively overcoming the limitations of traditional optimization methods in solving joint optimization problems with nonlinear constraints. Finally, we mathematically model the joint channel access and power control problem and balance the trade-off between fairness and transmission rate in the coexistence system by defining a suitable reward function in the C-GHDRL algorithm. Simulation results demonstrate that the proposed scheme significantly enhances the performance of the coexistence system while ensuring fair coexistence between SL-U and Wi-Fi users.

In frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems, reliable downlink channel estimation is essential but requires huge pilot overhead due to hundreds of antennas at base station (BS). In order to reduce pilot overhead without compromising the channel estimation, compressive sensing (CS) has been widely applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel in angular domain. However, it still suffers from high complexity during the optimization process and the requirement of prior information on the number of spatial paths (PINP). To overcome these challenges, this paper develops a novel hybrid channel estimation scheme by integrating model-driven CS and data-driven deep unrolling techniques. The proposed scheme is composed of a coarse estimation part and a fine correction part, which is implemented in a two-stage manner by exploiting both inter- and intra-frame sparsities of channels in angular domain. Additionally, a threshold function is proposed to eliminate the requirement of the number of spatial paths. Theoretical results are provided to indicate the convergence of both fine correction and coarse estimation. Numerical results demonstrate that our scheme can achieve high accuracy with less pilot overhead and low complexity.

Near-field communication comes to be an indispensable part of the future sixth generation (6G) communications at the arrival of the forth-coming deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. Due to the huge array aperture and high-frequency bands, the electromagnetic radiation field is modeled by the spherical waves instead of the conventional planar waves, leading to severe weak sparsity to angular-domain near-field channel. Therefore, the channel estimation reminiscent of the conventional compression sensing (CS) approaches in the angular domain, judiciously utilized for low pilot overhead, may result in unprecedented challenges. To this end, this paper proposes a brand-new near-field channel estimation scheme by exploiting the naturally occurring useful side information. Specifically, we formulate the dual-band near-field communication model based on the fact that high-frequency systems are likely to be deployed with lower-frequency systems. Representative side information, i.e., the structural characteristic information derived by the sparsity ambiguity and the out-of-band spatial information stemming from the lower-frequency channel, is explored and tailored to materialize exceptional near-field channel estimation. Furthermore, in-depth theoretical analyses are developed to guarantee the minimum estimation error, based on which a suite of algorithms leveraging the elaborating side information are proposed. Numerical simulations demonstrate that the designed algorithms provide more assured results than the off-the-shelf approaches in the context of the dual-band near-field communications in both on- and off-grid scenarios, where the angle of departures/arrivals are discretely or continuously distributed, respectively.

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts involving a holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework for analyzing both noiseless and noisy recovery conditions. Specifically, we present an exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and a signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing the MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the results derived are universal and applicable to all these tensorized variants.

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. In this framework, data from each modality are first given to pretrained single-modality encoders. The encoder outputs are then integrated with a multimodal fusion network, which uses novel merge- and co-attention mechanisms to effectively combine information from the different modalities. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Unlike previous research using only video for pretraining, the i-Code framework can dynamically process single, dual, and triple-modality data during training and inference, flexibly projecting different combinations of modalities into a single representation space. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five multimodal understanding tasks and single-modality benchmarks, improving by as much as 11% and demonstrating the power of integrative multimodal pretraining.

In cognitive radio (CR), compressive spectrum sensing (CSS) has drawn much attention since it enjoys decent performance and facilitates fast implementation. Due to the spectrum's inherent block sparsity and the joint spectrum sampling, CSS is further modeled as a block multiple measurement vector (BMMV) problem, which can be solved by joint block greedy-iterative algorithms such as block simultaneous orthogonal matching pursuit (BSOMP). However, the feasibility of such methods are shadowed by their inflexible sampling rates and dependence on accurate sparsity information. To address this issue, this paper proposes an adaptive-blind block simultaneous orthogonal matching pursuit (AB-BSOMP) algorithm based on the BMMV model. The blind halting criterion for BSOMP is first derived, allowing spectrum recovery to be independent of a priori sparsity information. Furthermore, to guarantee the reliable and adaptive spectrum recovery, a sampling-controlled algorithm (SCA) is developed to calculate an optimal number of measurements dynamically. Finally, AB-BSOMP is proposed by combining the developed blind halting criterion and the SCA. Simulation results demonstrate that the elaborating algorithm performs reliable recovery under various signal-to-noise ratios (SNRs), and reduces computational complexity in high SNR conditions while maintaining exact detection.

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intra-frame sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to grammatical errors, disfluency, and other noises common in spoken communication. These readable issues introduced by speakers and ASR systems will impair the performance of downstream tasks and the understanding of human readers. In this work, we present a task called ASR post-processing for readability (APR) and formulate it as a sequence-to-sequence text generation problem. The APR task aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of speakers. We further study the APR task from the benchmark dataset, evaluation metrics, and baseline models: First, to address the lack of task-specific data, we propose a method to construct a dataset for the APR task by using the data collected for grammatical error correction. Second, we utilize metrics adapted or borrowed from similar tasks to evaluate model performance on the APR task. Lastly, we use several typical or adapted pre-trained models as the baseline models for the APR task. Furthermore, we fine-tune the baseline models on the constructed dataset and compare their performance with a traditional pipeline method in terms of proposed evaluation metrics. Experimental results show that all the fine-tuned baseline models perform better than the traditional pipeline method, and our adapted RoBERTa model outperforms the pipeline method by 4.95 and 6.63 BLEU points on two test sets, respectively. The human evaluation and case study further reveal the ability of the proposed model to improve the readability of ASR transcripts.

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to disfluency, filter words, and other errata common in spoken communication. Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline. In this work, we propose an ASR post-processing model that aims to transform the incorrect and noisy ASR output into a readable text for humans and downstream tasks. We leverage the Metadata Extraction (MDE) corpus to construct a task-specific dataset for our study. Since the dataset is small, we propose a novel data augmentation method and use a two-stage training strategy to fine-tune the RoBERTa pre-trained model. On the constructed test set, our model outperforms a production two-step pipeline-based post-processing method by a large margin of 13.26 on readability-aware WER (RA-WER) and 17.53 on BLEU metrics. Human evaluation also demonstrates that our method can generate more human-readable transcripts than the baseline method.

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the most important methodologies for the machine-type communications (MTC). In the field of MTC, compressed sensing based multi-user detection (CS-MUD) has been recognized as an excellent candidate for joint user activity and data detection, since many users sporadically transmit short-size data packets at low rates. This article focuses on the CS-MUD problem in the up-link grant-free NOMA scenario, where users are (in)-active randomly in each time slot yet with high temporal correlation. First, we investigate the CS framework to fully extract the underlying side information in the temporal correlation and propose a novel CS-MUD algorithm. Then, to mitigate the performance degradation due to the imperfect channel estimation in practice, the proposed algorithm is further extended by utilizing the perturbed CS, where the impact of channel estimation errors is modeled as certain perturbation in the measurement matrix. Different from most of the state-of-the-art CS-MUD algorithms, both proposed algorithms can apply even in the absence of prior knowledge on the number of active users. Simulation results indicate that the proposed algorithms achieve better performance than the existing CS-MUD methods. Their convergence and complexity issues are also discussed theoretically and numerically.

Spectrum sensing is an important topic in cognitive radio networks. The traditional schemes suffer from high time consumption, hardware loss and computational complexity. To overcome the above shortcomings, compressed sensing is integrated into cognitive radio networks. In this paper, we propose two compressed spectrum sensing algorithms called Logit Weighted Block Orthogonal Matching Pursuit (LW-BOMP) and Logit Weighted Block Orthogonal Matching Pursuit with Joint Judgement (LW-BOMP-J), which exploit the block sparsity and prior information. The first algorithm integrates support probabilities into the matching pursuit procedure, and the second algorithm extends the first one to the scenario with inaccurate block support probabilities. The performance of the algorithms is simulated and compared with conventional algorithm. The results show that the proposed algorithms are more promising in reconstructing original spectrum and LW-BOMP-J is better than LW-BOMP under the inaccurate prior information condition.

We present a gonio-plenoptic imaging system that realizes a single-shot shape measurement for specular surfaces. The system is comprised of a collimated illumination source and a plenoptic camera. Unlike a conventional plenoptic camera, our system captures the BRDF variation of the object surface in a single image in addition to the light field information from the scene, which allows us to recover very fine 3D structures of the surface. The shape of the surface is reconstructed based on the reflectance property of the material rather than the parallax between different views. Since only a single-shot is required to reconstruct the whole surface, our system is able to capture dynamic surface deformation in a video mode. We also describe a novel calibration technique that maps the light field viewing directions from the object space to subpixels on the sensor plane. The proposed system is evaluated using a concave mirror with known curvature, and is compared to a parabolic mirror scanning system as well as a multi-illumination photometric stereo approach based on simulations and experiments.

Heterogeneous networks (HetNets) have been considered as a promising technique for improving spectral efficiency, but the energy efficiency influenced by the fluctuation of user numbers should not be neglected to achieve green communications. We introduce the energy-efficient switch-off mode algorithm for pico cells to reduce power consumption in the self-organizing network (SON). Once the switch-off mode is initiated in some picked pico cells, the users connecting to these cells would face the problem of redistribution. In this paper, we introduce the energy saving (ES) strategy based on the prediction of load capability to select pico cells to enter switchoff mode. Furthermore, network flow based load balancing (LB) is proposed to provide solution of uneven load status caused by ES. System level simulations are conducted to exhibit the performance enhancement that the proposed algorithm can reduce energy consumption as well as improve the balanced degree of resource allocation significantly.