Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning

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.Comment: 14 pages, 9 figures, 2 tables. This paper has been accepted for publication by the IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessibl

Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method

A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

The generalized Radon-Fourier transform (GRFT) has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL) which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO) has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method

Event-Centric Query Expansion in Web Search

In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches. In this work, we present Event-Centric Query Expansion (EQE), a novel QE system that addresses these issues by mining the best expansion from a significant amount of potential events rapidly and accurately. This system consists of four stages, i.e., event collection, event reformulation, semantic retrieval and online ranking. Specifically, we first collect and filter news headlines from websites. Then we propose a generation model that incorporates contrastive learning and prompt-tuning techniques to reformulate these headlines to concise candidates. Additionally, we fine-tune a dual-tower semantic model to function as an encoder for event retrieval and explore a two-stage contrastive training approach to enhance the accuracy of event retrieval. Finally, we rank the retrieved events and select the optimal one as QE, which is then used to improve the retrieval of event-related documents. Through offline analysis and online A/B testing, we observe that the EQE system significantly improves many metrics compared to the baseline. The system has been deployed in Tencent QQ Browser Search and served hundreds of millions of users. The dataset and baseline codes are available at https://open-event-hub.github.io/eqe .Comment: ACL 2023 Industry Trac

Low distortion reversible database watermarking based on hybrid intelligent algorithm

In many fields, such as medicine and the computer industry, databases are vital in the process of information sharing. However, databases face the risk of being stolen or misused, leading to security threats such as copyright disputes and privacy breaches. Reversible watermarking techniques ensure the ownership of shared relational databases, protect the rights of data owners and enable the recovery of original data. However, most of the methods modify the original data to a large extent and cannot achieve a good balance between protection against malicious attacks and data recovery. In this paper, we proposed a robust and reversible database watermarking technique using a hash function to group digital relational databases, setting the data distortion and watermarking capacity of the band weight function, adjusting the weight of the function to determine the watermarking capacity and the level of data distortion, using firefly algorithms (FA) and simulated annealing algorithms (SA) to improve the efficiency of the search for the location of the watermark embedded and, finally, using the differential expansion of the way to embed the watermark. The experimental results prove that the method maintains the data quality and has good robustness against malicious attacks

Timing of Maximal Weight Reduction Following Bariatric Surgery: A Study in Chinese Patients

Introduction: Bariatric surgery is a well-received treatment for obesity with maximal weight loss at 12–36 months postoperatively. We investigated the effect of early bariatric surgery on weight reduction of Chinese patients in accordance with their preoperation characteristics. Materials and Methods: Altogether, 409 patients with obesity from a prospective cohort in a single bariatric center were enrolled retrospectively and evaluated for up to 4 years. Measurements obtained included surgery type, duration of diabetic condition, besides the usual body mass index data tuple. Weight reduction was expressed as percent total weight loss (%TWL) and percent excess weight loss (%EWL). Results: RYGB or SG were performed laparoscopically without mortality or complications. BMI generally plateaued at 12 months, having decreased at a mean of 8.78 kg/m2. Successful weight loss of \u3e 25% TWL was achieved by 35.16, 49.03, 39.22, 27.74, 20.83% of patients at 6, 12, 24, 36, and 48 months after surgery. Overall, 52.91% of our patients had lost 100% of their excess weight at 12 months, although there was a rather wide range among individuals. Similar variability was revealed in women of child-bearing age. Conclusion: Chinese patients undergoing bariatric surgery tend to achieve maximal weight loss and stabilization between 12 and 24 months postoperatively, instead of at \u3e 2 years. The finding of the shorter stabilization interval has importance to earlier intervention of weight loss related conditions and women\u27s conception planning