- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Optimization of Non-Orthogonal Multiple Access based Visible Light Communication Systems
摘要： In Visible Light Communication (VLC), the data is transmitted by modulating the Light Emitting Diode (LED). The data-rate is throttled by the narrow modulation bandwidth of LEDs which results as a barrier in attaining high transmission rates. Non-Orthogonal Multiple Access (NOMA) is a new Multiple Access (MA) scheme envisioned to improve the system capacity. In addition to MA schemes, optimization techniques are applied to further improve the data-rate. In this letter, convex optimization is applied on NOMA-based VLC system for downlinks. The proposed optimization system is analyzed in terms of the Bit Error Rate (BER) and the sum-rate.
关键词： Sum-rate maximization,Visible Light Communication,Non-Orthogonal Multiple Access (NOMA),Convex Optimization,5G
更新于2025-09-23 15:23:52
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Compensation of Radome Effects in Small Airborne Monopulse Arrays by Convex Optimization
摘要： The quality of small array antennas in airborne monopulse systems can be significantly reduced by the radome. We therefore present a convex optimization approach to minimize radome effects in monopulse arrays. This is achieved by using active element patterns in the optimization to determine the excitation weights. Simulation results for a BoR array with 48 elements and an extended hemispherical radome are presented. We demonstrate that it is possible to reduce the side-lobe level by 3.5 dB by taking radome effects into account in the optimization. This approach also results in an increased gain, particularly at large scan angles. Furthermore, the presented approach allows the monopulse slope to be indirectly specified as a design parameter. It is shown that the trade-off between the monopulse slope coefficient and the side-lobe level is approximately linear.
关键词： radomes,airborne ESM,monopulse DOA estimation,convex optimization,phased array antennas
更新于2025-09-23 15:19:57
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Multiple Input Single Output Phase Retrieval
摘要： In this paper, we consider the problem of recovering the phase information of multiple sources from a mixed phaseless short-time Fourier transform measurement, which is called multiple input single output (MISO) phase retrieval problem. It is an inherently ill-posed problem due to the lack of the phase and mixing information, and the existing phase retrieval algorithms are not explicitly designed for this case. To address the MISO phase retrieval problem, a least-squares method coupled with an independent component analysis (ICA) algorithm is proposed for the case of sufficiently long window length. When these conditions are not met, an integrated algorithm is presented, which combines a gradient descent algorithm by minimizing a non-convex loss function with an ICA algorithm. Experimental evaluation has been conducted to show that under appropriate conditions the proposed algorithms can explicitly recover the signals, the phases of the signals, and the mixing matrix. In addition, the algorithm is robust to noise.
关键词： Short-time Fourier transform (STFT),Multiple input single output (MISO),Independent component analysis (ICA),Non-convex optimization,Phase retrieval
更新于2025-09-19 17:15:36
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A Convex Approach to Superresolution and Regularization of Lines in Images
摘要： We present a new convex formulation for the problem of recovering lines in degraded images. Following the recent paradigm of superresolution, we formulate a dedicated atomic norm penalty and we solve this optimization problem by means of a primal-dual algorithm. This parsimonious model enables the reconstruction of lines from lowpass measurements, even in presence of a large amount of noise or blur. Furthermore, a Prony method performed on rows and columns of the restored image, provides a spectral estimation of the line parameters, with subpixel accuracy.
关键词： sparse recovery,convex optimization,splitting method,superresolution,spectral estimation,line detection
更新于2025-09-19 17:15:36
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Optimization-based AMP for Phase Retrieval: The Impact of Initialization and ?2-regularization
摘要： We consider an (cid:96)2-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting m, n → ∞, m/n → δ and obtain sharp performance bounds, where m is the number of measurements and n is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only m = (cid:0) 64 π2 ? 4(cid:1) n ≈ 2.5n measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding (cid:96)2 regularization to the non-convex loss function can be beneficial. (ii) Spectral initialization has marginal impact on the performance of the algorithm. The sharp analyses in this paper, not only enable us to compare the performance of our method with other phase recovery schemes, but also shed light on designing better iterative algorithms for other non-convex optimization problems.
关键词： (cid:96)2-regularization,spectral initialization,Phase retrieval,approximate message passing,non-convex optimization
更新于2025-09-19 17:15:36
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Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
摘要： We propose a Bayesian uncertainty quanti?cation method for large-scale imaging inverse problems. Our method applies to all Bayesian models that are log-concave, where maximum a posteriori (MAP) estimation is a convex optimization problem. The method is a framework to analyze the con?dence in speci?c structures observed in MAP estimates (e.g., lesions in medical imaging, celestial sources in astronomical imaging), to enable using them as evidence to inform decisions and conclusions. Precisely, following Bayesian decision theory, we seek to assert the structures under scrutiny by performing a Bayesian hypothesis test that proceeds as follows: ?rst, it postulates that the structures are not present in the true image, and then seeks to use the data and prior knowledge to reject this null hypothesis with high probability. Computing such tests for imaging problems is generally very di?cult because of the high dimensionality involved. A main feature of this work is to leverage probability concentration phenomena and the underlying convex geometry to formulate the Bayesian hypothesis test as a convex problem, which we then e?ciently solve by using scalable optimization algorithms. This allows scaling to high-resolution and high-sensitivity imaging problems that are computationally una?ordable for other Bayesian computation approaches. We illustrate our methodology, dubbed BUQO (Bayesian Uncertainty Quanti?cation by Optimization), on a range of challenging Fourier imaging problems arising in astronomy and medicine. MATLAB code for the proposed uncertainty quanti?cation method is available on GitHub.
关键词： Bayesian inference,inverse problems,image processing,hypothesis testing,uncertainty quanti?cation,convex optimization
更新于2025-09-19 17:15:36
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Low Complexity Dimensioning of Sustainable Solar-enabled Systems: A Case of Base Station
摘要： Solar-enabled systems are becoming popular for provisioning pollution-free and cost-effective energy solution. Dimensioning of a solar-enabled system requires estimation of appropriate size of photovoltaic (PV) panel as well as storage capacity while satisfying a given energy outage constraint. Dimensioning has strong impact on the user’s quality of experience and network operator’s interest in terms of energy outage and revenue. In this paper, dimensioning problem of solar-enabled communication nodes is analyzed in order to reduce the computation overhead, where stand-alone solar-enabled base station (SS-BS) is considered as a case study. For this purpose, hourly solar data of last ten years has been taken into consideration for analysis. First, the power consumption model of BS is revised to save energy and increase revenue. Using the hourly solar data and power consumption profile, the lower bounds on panel size and storage capacity are obtained using Gaussian mixture model, which provides a reduced search space for cost-optimal system dimensioning. Then, the cost function and energy outage probability are modeled as functions of panel size and number of battery units using curve fitting technique. The cost function is proven to be quasiconvex, whereas energy outage probability is proven to be convex function of panel size and number of battery units. These properties transform the cost-optimal dimensioning problem into a convex optimization framework, which ensures a global optimal solution. Finally, a Computationally-efficient Energy outage aware Cost-optimal Dimensioning Algorithm (CECoDA) is proposed to estimate the system dimension without requiring exhaustive search. The proposed framework is tested and validated on solar data of several cities; for illustration purpose, four cities, New Delhi, Itanagar, Las Vegas, and Kansas, located at diverse geographical regions, are considered. It is demonstrated that, the presented optimization framework determines the system dimension accurately, while reducing the computational overhead up to 94% and the associated energy requirement for computation with respect to the exhaustive search method used in the existing approaches. The proposed framework CECoDA takes advantage of the location-dependent unique solar profile, thereby achieving cost-efficient solar-enabled system design in significantly less time.
关键词： computation efficiency,cost-optimal system dimensioning,Sustainable solar-enabled system,solar energy harvesting,energy outage,Gaussian mixture model,convex optimization,curve fitting
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) - Xi'an (2018.9.13-2018.9.16)] 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) - Adaptive Optimization with Nested Prior Navigation for Blind Image Deblurring
摘要： Image deblurring is a typical inverse problem and has many applications in vision and multimedia areas. It involves the estimation of blur kernel and sharp image given only a blurry observation. Due to the fundamentally ill-posed nature, most existing works design different priors within the maximum a posteriori (MAP) framework to regularize their solution space. However, due to the unknown image distribution, complex kernel structure and non-uniform noises, it is indeed challenging to explicitly design a fixed prior for blurry images in real-world scenarios. Different from these conventional strategies to integrate sophisticated priors into optimization model, this work only formulates the necessary constraints on latent image and blur kernel as a lightweight MAP model. Then we develop an inexact projected gradient scheme to incorporate flexible sparse structure control for MAP inference. We demonstrate that this adaptive scheme can successfully avoid degenerate solutions and is universally suitable for different blurry scenarios, such as low-illumination, face and text. Extensive experiments on both synthetic data and real-world images demonstrate the effectiveness and robustness of the proposed method against other state-of-the-art methods.
关键词： maximum a posteriori,Image deblurring,adaptive prior,non-convex optimization
更新于2025-09-10 09:29:36
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Griffin-Lim Like Phase Recovery via Alternating Direction Method of Multipliers
摘要： Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing. When only an amplitude spectrogram is available and no explicit information is given for the phases, the Grif?n–Lim algorithm (GLA) is one of the most utilized methods for phase recovery. However, GLA often requires many iterations and results in low perceptual quality in some cases. In this paper, we propose two novel algorithms based on GLA and the alternating direction method of multipliers (ADMM) for better recovery with fewer iteration. Some interpretation of the existing methods and their relation to the proposed method are also provided. Evaluations are performed with both objective measure and subjective test.
关键词： STFT-based speech synthesis,spectrogram consistency,short-time Fourier transform (STFT),Non-convex optimization,phaseless spectrogram inversion
更新于2025-09-09 09:28:46
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Directional Sinogram Inpainting for Limited Angle Tomography
摘要： In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microscopy and Mammography, physical limitations on acquisition lead to regions of data which cannot be sampled. Depending on the severity of the restriction, reconstructions can contain severe, characteristic, artefacts. Our model aims to address these artefacts by inpainting the missing data simultaneously with the reconstruction. Numerically, this problem naturally evolves to require the minimisation of a non-convex and non-smooth functional so we review recent work in this topic and extend results to fit an alternating (block) descent framework. We perform numerical experiments on two synthetic datasets and one Electron Microscopy dataset. Our results show consistently that the joint inpainting and reconstruction framework can recover cleaner and more accurate structural information than the current state of the art methods.
关键词： Joint Model,Electron Microscopy,Limited Angle Tomography,Non-convex Optimization,Directional Sinogram Inpainting
更新于2025-09-09 09:28:46