Radio map estimation (RME) is crucial for effective planning and optimization of wireless networks. Traditional approaches such as interpolation excel at capturing local smoothness in densely populated data but struggle with sparse or irregular data. Conversely, matrix completion (MC) approaches utilize global structures but require huge number of samples and may produce non-smooth estimates. To integrate these strengths, we propose a convex optimization approach for RME (IIMC-RME) that merges interpolation with MC. This approach formulates the RME task as a low-rank MC problem constrained by interpolated results. Additionally, we have developed a convergent algorithm utilizing the alternating direction method of multipliers (ADMM) to efficiently solve the IIMC-RME problem. Experimental evaluations on both synthetic and real-world datasets have shown that IIMC-RME surpasses existing approaches, thereby achieving superior accuracy in RME.

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

In this letter, we look into the emitter localization task within the Direct Position Determination (DPD) paradigm. This paradigm is by essence a largest eigenvalue problem which treats the channel attenuation variables as free parameters. We consider the channel fading physical rule on electromagnetic signal propagation and reformulate the traditional DPD problem with a channel contextual prior. Thereafter, we develop iterative optimization algorithms based on the majorization-minimization (MM) framework. Numerical results show that the proposed algorithms outperform the traditional DPD estimators with better localization performance.

In this paper, we reflect on an interesting question whether direction of arrival (DOA) detection algorithms can be deceived. When the transmit signals are independent, prevalent DOA algorithms suffice to find the correct arriving directions from measurement statistics at the receiver end. But when the signals are coherent, the expected spectrum peaks may not show up and the detection performance deteriorates to a great extent. Instead of addressing the coherence issue, we take advantage of this phenomenon and fake a peak at a desired DOA in an attempt to launch a deception attack. The deception is realized by constructing a specified steering vector through a composite of candidate vectors in the dictionary. Thereafter, we apply the alternating direction method of multipliers (ADMM) framework to develop an efficient solution algorithm. Numerical simulations show that the proposed algorithm gives satisfactory DOA deception performance.

This paper proposes a 24-29.5 GHz$2\times 2$transmitter (TX) and receiver (RX) front-end chipset with streamlined intermediate frequency (IF) interfaces for digital beamforming (DBF) systems. An ultra-compact 90° coupler employing a three-dimensional (3-D) coupling structure achieves a 70% reduction in size compared to typical Lange couplers while maintaining excellent performance. This 3-D 90° coupler enables the synthesis/distribution of IF I-Q signals within the transceiver (TRX), enhancing the feasibility of mmWave DBF by reducing the demand for baseband channels and facilitating scalability for multi-beam arrays. To ensure a low-level error vector magnitude (EVM) in the TRX, the chipset employs joint package design for optimal power and noise performance. It utilizes an image-reject mixer to suppress RX image noise and introduces a DC-offset in the single-sideband up-converter for LO leakage regulation. Additionally, a frequency doubler chain is incorporated to enhance phase noise performance. Fabricated using 0.13-$\mu $m SiGe BiCMOS technology and packaged in WLCSP process, the TX achieves an average output 1-dB compression point ($OP_{1dB}$) of 20 dBm per channel. The RX chip features a minimum noise figure (NF) of 3.4 dB. Over-the-air (OTA) measurements conducted over a 1.1-m distance reveal a transmission data rate of up to 8 Gb/s using 16-QAM modulation at 25 GHz. Furthermore, when employing a 400-MHz 64-QAM 5G New Radio (NR) modulation, the system maintains EVM levels below -33 dB at 25 GHz and below -30 dB over 24-29.5 GHz.

Non-orthogonal multiple access (NOMA) is recognized as a pivotal technology for enhancing spectral efficiency and facilitating massive connectivity in wireless communications. In practice, users are often divided into groups that occupy different resource blocks, while the users in the same group share the same resource through power-domain multiplexing, leading to multi-group NOMA (MG-NOMA). Hence, the full benefit of MG-NOMA relies on optimum power allocation, which, unfortunately, leads to difficult optimization problems that have not been well solved so far. This work investigates the optimal power allocation to achieve the maximum spectral efficiency and energy efficiency with quality-of-service (QoS) requirements and arbitrary user weights in MG-NOMA. We show that the complicated MG-NOMA power allocation problems can be decomposed into two layers, the lower layer for power allocation within each group and the upper layer for the power budget allocation among groups. More importantly, we reveal that, although quite difficult, the two-layer problems both contain the hidden convexity, indicating the achievability of the globally optimal solution. Then, we derive the optimal power allocation in either closed or semi-closed form for the maximum spectral efficiency and energy efficiency in MG-NOMA, respectively. The simulation results demonstrate the superiority of the proposed optimal power allocation schemes.

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.

Decentralized computation has received considerable research interest lately, due to its wide applications in information processing systems. However, one key requirement to establish convergence for almost all decentralized algorithms, for convex and non-convex problems alike, is that the loss function has Lipschitz-continuous gradient (LipGrad). This is a strong assumption, which does not hold for many practical problems, such as matrix/tensor factorization, neural network training, etc. On the contrary, in the centralized setting, one can utilize techniques such as the Bregman proximal gradient (BPG) method to deal with the lack of LipGrad. This work fills the gap between centralized and decentralized cases by developing a novel smoothed decentralized BPG algorithm to deal with a class of nonconvex decentralized problem, where the local problems do not have LipGrad objective functions. By leveraging the recent notion of relative smoothness and primal-dual error bounds, we show that the proposed algorithm achieves a certain ε-stationary solution by using $\mathcal{O}\left( {{\varepsilon ^{ - 2}}} \right)$ iterations, matching the rate of the centralized Bregman proximal gradient method. To our knowledge, this is the first decentralized algorithm that matches the centralized convergence rate bounds under the class of considered problems. Our numerical results on the decentralized quadratic regression example demonstrate the effectiveness of proposed algorithm.

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

In this paper, we study the classical Logistic Regression (LR) problem in machine learning. Traditionally, the solving algorithms are based on either the first- or second-order approximation of the objective. For instance, the Fixed-Hessian Newton (FHN) method approximates the true Hessian with a constant estimate. In contrast, our design additionally exploits the third-order information. Applying the majorization–minimization (MM) framework, we construct a novel majorizing function based on the third-order Taylor expansion and the minimization solution is in closed-form with perseverance of the true gradient and Hessian structures. In analysis, we prove the convergence rate of the proposed algorithm. The enhanced numerical performance can be verified through simulation results.

This article presents a fully packaged 94-GHz 16-channel local oscillator (LO) phase-shifting transmitter (TX) and a single-channel receiver (RX). The implementation is accomplished using a hybrid integration scheme, combining high-output-power 100 nm GaAs pHEMT front-end chips and highly-integrated 130 nm SiGe BiCMOS beamformer chips. High-accuracy LO phase shifting is achieved with the utilization of a commercial SiGe-based four-channel beamformer chips, offering 7-bit phase control at 24–28 GHz. A 26-to-78 GHz tripler chain using power-enhancing and harmonic-suppression techniques, a 16-to-94 GHz bi-directional mixer, and a 94-GHz power amplifier are designed in the transmitter front-end chip based on the GaAs process. The GaAs transmitter front-ends are wire-bonded to microstrip lines and then converted to low-loss substrate integrated waveguides (SIWs), which directly feed a high-gain TEM horn antenna array. The inter-element spacing of the transmitter array is optimized to 1.6 mm ($0.5 \lambda _{0}$@94 GHz) for a wide scanning range. The 16-channel transmitter achieves a wide scanning range of ±50° and a peak effective isotropic radiated power (EIRP) of 43.6 dBm at 94 GHz. The GaAs receiver chip is packaged with the WR10 waveguide RF interface and connected to a horn antenna. The packaged GaAs receiver module achieves a conversion gain (CG) of 25 dB and a noise figure (NF) of 5.8 dB. Additionally, the 16T1R over-the-air (OTA) measurement supports 5G New Radio 400-MHz 64-QAM signal between 88 and 94 GHz over a 5-meter ±48° scanning range.

The direction of arrival (DOA) estimation of the signal is an important task in radio signal positioning. Various methods have been investigated to cope with the DOA task. However, since the imperfect interference factors are often present in practical antenna arrays, the performance of DOA estimation is often significantly degraded. Besides, few methods deal with the DOA estimation for signals of multiple frequencies. In this paper, we consider the problem of two-dimensional DOA estimation in the presence of imperfect factors, and propose a novel approach where the convolutional attention network is used for DOA estimation. The frequency information is introduced as a token added to the network, which improves the network robustness while taking into account the case of the signal of multiple frequencies. Besides, we extend the mean square error (MSE) to the design of a new loss function for training to improve the accuracy of the model. The advantages of the proposed DOA estimation scheme are demonstrated through numerical experiments.

The vector autoregressive (VAR) models provide a significant tool for multivariate time series analysis. Most existing works on VAR modeling are based on the multivariate Gaussian distribution. However, heavy-tailed distributions are suggested more reasonable for capturing the real-world phenomena, like the presence of outliers and a stronger possibility of extreme values. Furthermore, missing values in observed data is a real problem, which typically happens during the data observation or recording process. In this paper, we propose an algorithmic framework to estimate the parameters of a VAR model with heavy-tailed Student’s t distributed innovations from incomplete data based on the stochastic approximation expectation maximization (SAEM) algorithm coupled with a Markov Chain Monte Carlo (MCMC) procedure. Extensive experiments with synthetic data corroborate our claims.

The heuristic 1/N (equally weighted) portfolio and long-only quintile portfolio are both popular simple strategies in financial investment. In the 1/N portfolio, a fraction of 1/N of wealth is allocated to each of the N available assets. In the long-only quintile portfolio, first the assets are sorted according to some factors, e.g., expected returns, and then the strategy equally longs the top 20% (i.e., top quintile). Although they have been criticized for naiveness when proposed by practitioners, they have shown great advantage over some sophisticated portfolios. They are becoming more and more popular in practical investment due to their stable performance and easy deployment. In this paper, we formulate a mathematically meaningful robust maximum return portfolio design and show that it reduces to the two heuristic portfolios under different level of estimation error in the mean returns. A variance-adjusted uncertainty set is also proposed to derive an inverse-volatility portfolio, which shows consistent advantage over the heuristic portfolios in backtesting with real market data.

Minimax multiple-input multiple-output (MIMO) transmit beampattern matching is a fundamental and important problem in many MIMO systems. The problem is formulated to minimize the maximum beampattern matching error as well as suppress the cross-correlation beampatterns while taking different practical waveform constraints into consideration. Due to the high nonconvexity of the problem, the traditional way for problem solving is a two-stage approach, where a waveform covariance matrix is firstly designed and then the waveforms are synthesized from the covariance matrix under a specific constraint. This approach is usually very time consuming and only results in suboptimal solutions. In this paper, a novel and unified one-stage approach is proposed to solve the minimax beampattern matching problem which is capable of considering multiple waveform constraints. Superior performance of the proposed approach over the classical approach is verified through numerical simulations.