In this article, we have provided general, comprehensive coverage of the SDR technique, from its practical deployments and scope applicability to key theoretical results. We also showcased several representative applications, namely MIMO detection, B¿ shimming in MRI, sensor network localization. Another important application, downlink transmit beamforming, is described [1]. Due space limitations, are unable cover many other beautiful applications although done our best illustrate intuitive...

In this paper, we study a probabilistically robust transmit optimization problem under imperfect channel state information (CSI) at the transmitter and multiuser multiple-input single-output (MISO) downlink scenario. The main issue is to keep probability of each user's achievable rate outage as caused by CSI uncertainties below given threshold. As well known, such constraints present significant analytical computational challenge. Indeed, they do not admit simple closed-form expressions are...

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new for each task. Currently, many research works propose only fine-tune small portion parameters while keeping most shared across different These methods achieve surprisingly good performance and are shown more stable than their corresponding fully fine-tuned counterparts. such kind still not...

In this paper, we develop a novel robust optimization approach to source localization using time-difference-of-arrival (TDOA) measurements that are collected under non-line-of-sight (NLOS) conditions. A key feature of our is it does not require knowledge the distribution or statistics NLOS errors, which often difficult obtain in practice. Instead, only assumes errors have bounded supports. Based on assumption, formulate TDOA-based problem as least squares (RLS) problem, location estimate...

In cognitive radio (CR) networks with multiple-input multiple-output (MIMO) links, secondary users (SUs) can exploit "spectrum holes" in the space domain to access spectrum allocated a primary system. However, they need suppress interference caused (PUs), as system should be transparent this paper, we study optimal secondary-link beamforming pattern that balances between SU's throughput and it causes PUs. particular, aim maximize of SU, while keeping temperature at receivers below certain...

We consider optimization problems over the Stiefel manifold whose objective function is summation of a smooth and nonsmooth function. Existing methods for solving this kind problem can be classified into three categories. Algorithms in first category rely on information subgradients thus tend to converge slowly practice. second are proximal point algorithms, which involve subproblems that as difficult original problem. third based operator-splitting techniques, but they usually lack rigorous...

Consider the following problem: A multi-antenna base station (BS) sends multiple symbol streams to single-antenna users via precoding. However, unlike conventional multiuser precoding, transmitted signals are subjected binary, unit-modulus, or even discrete unit-modulus constraints. Such constraints arise in one-bit and constant-envelope (CE) massive MIMO scenarios, wherein high-resolution digital-to-analog converters (DACs) replaced by DACs phase shifters, respectively, for cutting down...

Consider transceiver designs in a multiuser multi-input single-output (MISO) downlink channel, where the users are to receive same data stream simultaneously. This problem, known as physical-layer multicasting, has drawn much interest. Presently, popularized approach is transmit beamforming, which beamforming optimization handled by rank-one approximation method called semidefinite relaxation (SDR). SDR-based been shown be promising for small or moderate number of users. paper describes two...

Adaptive OFDMA has recently been recognized as a promising technique for providing high spectral efficiency in future broadband wireless systems. The research over the last decade on adaptive systems focused adapting allocation of radio resources, such subcarriers and power, to instantaneous channel conditions all users. However, "fast" adaptation requires computational complexity excessive signaling overhead. This hinders deployment worldwide. paper proposes slow scheme, which subcarrier is...

Recently, robust transmit beamforming has drawn considerable attention because it can provide guaranteed receiver performance in the presence of channel state information (CSI) errors. Assuming complex Gaussian distributed CSI errors, this paper investigates design problem that minimizes transmission power subject to probabilistic signal-to-interference-plus-noise ratio (SINR) constraints. The SINR constraints general have no closed-form expression and are difficult handle. Based on a...

An estimation problem of fundamental interest is that phase (or angular) synchronization, in which the goal to recover a collection phases angles) using noisy measurements relative angle offsets). It known Gaussian noise setting, maximum likelihood estimator (MLE) an optimal solution nonconvex quadratic optimization and can be found with high probability semidefinite programming (SDP), provided power not too large. In this paper, we study convergence performance recently proposed...

In this paper, we study the problem of recovering a low-rank matrix from number random linear measurements that are corrupted by outliers taking arbitrary values. We consider nonsmooth nonconvex formulation problem, in which explicitly enforce property solution using factored representation variable and employ an $\ell_1$-loss function to robustify against outliers. show even when constant fraction (which can be up almost half) arbitrarily corrupted, as long certain measurement operators...

The successful deployment and operation of location-aware networks, which have recently found many applications, depends crucially on the accurate localization nodes. Currently, a powerful approach to is that convex relaxation. In typical application this approach, problem first formulated as rank-constrained semidefinite program (SDP), where rank corresponds target dimension in nodes should be localized. Then, non-convex constraint either dropped or replaced by surrogate, thus resulting...

Classifying binary imbalanced streaming data is a significant task in both machine learning and mining. Previously, online area under the receiver operating characteristic (ROC) curve (AUC) maximization has been proposed to seek linear classifier. However, it not well suited for handling nonlinearity heterogeneity of data. In this paper, we propose kernelized (KOIL) algorithm, which produces nonlinear classifier by maximizing AUC score while minimizing functional regularizer. We address four...