Single-pixel imaging via compressed sensing can reconstruct high-quality images from a few linear random measurements of an object known a priori to be sparse or compressive, by using a point/bucket ...detector without spatial resolution. Nevertheless, random measurements still have blindness, limiting the sampling ratios and leading to a harsh trade-off between the acquisition time and the spatial resolution. Here, we present a new compressive imaging approach by using a strategy we call cake-cutting, which can optimally reorder the deterministic Hadamard basis. The proposed method is capable of recovering images of large pixel-size with dramatically reduced sampling ratios, realizing super sub-Nyquist sampling and significantly decreasing the acquisition time. Furthermore, such kind of sorting strategy can be easily combined with the structured characteristic of the Hadamard matrix to accelerate the computational process and to simultaneously reduce the memory consumption of the matrix storage. With the help of differential modulation/measurement technology, we demonstrate this method with a single-photon single-pixel camera under the ulta-weak light condition and retrieve clear images through partially obscuring scenes. Thus, this method complements the present single-pixel imaging approaches and can be applied to many fields.
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.
In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in ...wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.
Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along ...desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this paper, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight upper bound of the ergodic spectral efficiency and investigate the effect of the phase shifts on the ergodic spectral efficiency in different propagation scenarios. In particular, we propose an optimal phase shift design based on the upper bound of the ergodic spectral efficiency and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable spectral efficiency degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.
Developing Type‐I photosensitizers is considered as an efficient approach to overcome the deficiency of traditional photodynamic therapy (PDT) for hypoxic tumors. However, it remains a challenge to ...design photosensitizers for generating reactive oxygen species by the Type‐I process. Herein, we report a series of α,β‐linked BODIPY dimers and a trimer that exclusively generate superoxide radical (O2−.) by the Type‐I process upon light irradiation. The triplet formation originates from an effective excited‐state relaxation from the initially populated singlet (S1) to triplet (T1) states via an intermediate triplet (T2) state. The low reduction potential and ultralong lifetime of the T1 state facilitate the efficient generation of O2−. by inter‐molecular charge transfer to molecular oxygen. The energy gap of T1‐S0 is smaller than that between 3O2 and 1O2 thereby precluding the generation of singlet oxygen by the Type‐II process. The trimer exhibits superior PDT performance under the hypoxic environment.
Heavy‐atom‐free boron dipyrromethene (BODIPY)‐based photosensitizers generate ROS exclusively by the Type‐I process upon near‐infrared light illumination for tumor ablation.
Reliability of information can directly affect the accuracy of decision-making. Compared with classical fuzzy sets, Z-number takes into account the uncertainty in information generation process and ...reliability of information. Z-number can also be an intuitive and effective description form of decision information. Existing research on the distance measure of Z-number is rare, and most of the research works cannot reflect well the advantages of Z-information and the characteristics of Z-number. Given this research gap, this paper simultaneously considered the randomness and fuzziness of Z-number and defined the comprehensive weighted distance measure of Z-number. To extend classic VlseKriterijum-ska Optimizacija I Kompromisno Resenje (VIKOR) method to the Z-information environment, we suggested the Z-VIKOR method based on the proposed distance measure. This method is convenient and effective for the direct computation of Z-numbers. We also provided an example of multicriteria decision-making for selecting regional circular economy development plan to illustrate the feasibility and validity of our proposed method. We then verified the applicability and superiority of our method through comparative analyses with other existing methods.
Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex networks. ...However, the huge number of antennas poses a challenge to the conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this letter, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves tradeoff between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet-LSTM outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input ...multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.
This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require ...minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.
Simple and practical noble‐metal‐free catalyzed hydrogen production from sustainable resources, such as renewable formic acid, is highly desirable. Herein, the development of an efficient ...photocatalytic hydrogen production from aqueous solution of formic acid using in situ generated Ni/CdS photocatalytic system was described. CdS−Cys (Cys=l‐cysteine) quantum dots (QDs) acting as photocatalyst with Ni(OAc)2 as H2 production catalyst precursor, a 94 % yield was obtained within 5 h under visible light irradiation at 50 °C. The average rate of H2 production reached up to 282 μmol mg−1 h−1 with 99.8 % H2 selectivity. Mechanistic studies indicate cooperation of dynamic quenching and static quenching of CdS−Cys QDs by Ni(OAc)2. Especially, Ni0, generated in the dynamic quenching, accelerated the electron transfer by acting as an electron outlet and enhancing the stability of CdS to slow down the photocorrosion distinctly, delivering efficient H2 production with high selectivity. Our study will inspire exploration of various efficient non‐noble‐metal catalysts for practical H2 production from bio‐based formic acid.
Efficient photocatalytic hydrogen production from an aqueous solution of formic acid was achieved using an in situ generated Ni/CdS photocatalytic system. The dynamic quenching and static quenching of CdS−Cys (Cys=l‐cysteine) quantum dots by Ni(OAc)2 accelerate the electron transfer and enhance the stability of CdS, leading to enhanced activity and selectivity.
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