IEEE Transactions on Signal Processing

We present a new analytical framework for the uplink data detection in massive multiple-input multiple-output systems with 1-bit analog-to-digital converters (ADCs). We first characterize the expected values of the soft-estimated symbols (after the linear receiver and prior to the data detection), which are affected by 1-bit quantization during both the channel estimation and the uplink data tran…

A new, practical algorithm, fast Sketched Columnbased Matrix Approximation (fSCMA), is proposed for low–rank matrix approximation. fSCMA leverages randomly, but fully sampled columns combined with structural side information, to achieve efficient and accurate approximations. The algorithm leverages both matrix sketching and side information to reduce complexity. A theoretical spectral bound on th…

This paper addresses the challenges caused by employing phased-array-based intelligent reflecting surfaces (IRSs) for wideband beamforming applications at mmWave frequencies. When a large IRS is used in conjunction with a wideband signal, a phenomenon known as the beam-split (B-SP) effect arises wherein the IRS fails to focus its reflected energy toward a single user equipment (UE) across the ent…

Rydberg Atomic REceivers (RAREs) have demonstrated remarkable capabilities for radio-frequency signal measurement, enabling advanced quantum wireless sensing. Existing RARE-based sensing systems popularly adopt the heterodyne detection methodology, which requires an additional reference source to serve as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/199…

We present theoretical analysis of Sharpness-Aware Minimization (SAM) applied to training loss minimization with neural networks and smooth activations. Unlike prior works on stationary points measure in standard non-convex smooth optimization settings with noise assumptions, we leverage intrinsic properties of neural network loss landscapes to establish a convergence rate of Õ<inline-formula xml…

Variational Mode Decomposition (VMD) requires manual specification of mode numbers and center frequencies, limiting its practical applicability for non-stationary signals. This paper introduces Density-based Adaptive Mode Decomposition (DAMD), which addresses these limitations through three key innovations. First, we establish mathematical equivalence between VMD optimization and density-based cl…

To understand the convergence behavior of the Push–Pull method for decentralized optimization with stochastic gradients (Stochastic Push–Pull), this paper presents a comprehensive analysis. Specifically, we first clarify the algorithm’s underlying assumptions, particularly those regarding the network structure and weight matrices. Then, to establish the convergence rate under smooth nonconvex obj…

In this paper, we propose an integrated sensing and communication (ISAC) system aided by the movable-antenna (MA) array, which can improve the communication and sensing performance via flexible antenna movement over conventional fixed-position antenna (FPA) array. First, we consider the downlink multiuser communication, where each user is randomly distributed within a given three-dimensional zone…

State-space models are pivotal for dynamic system analysis but often struggle with outlier data that deviates from Gaussian distributions, frequently exhibiting skewness and heavy tails. This paper introduces a robust extension utilizing the asymmetric Laplace distribution, specifically tailored to capture these complex characteristics. We propose an efficient variational Bayes algorithm and a no…

We introduce a unified framework for analyzing utility regions of wireless networks, with a focus on signal-to-interference- plus-noise-ratio (SINR) and achievable rate regions. The framework provides valuable insights into interference patterns of modern network architectures, including extremely large MIMO and cell-less networks. A central contribution is a simple characterization of feasible u…

This paper addresses the jamming detection problem in colored noise environments. Low-power attacks are particularly dangerous due to their stealthiness and persistent harmful effects. The fluctuations caused by colored noise backgrounds further exacerbate this issue, making it easier for these attacks to remain concealed within the noise. We apply a source enumerator, namely the second-order-dif…

The wide-band cyclic cross-correlation function (WB-CCCF) is introduced for finite average-power signals. It is the cyclic cross-correlation of a signal and a time-scaled version of another signal. Input-output relationships in terms of WBCCCFs are derived for the wide-band Doppler channel, that is, the channel that introduces a complex gain, a delay, a frequency shift, and a time-scale factor on…

Extremely large antenna arrays (ELAA) are regarded as a promising technology for supporting sixth-generation (6G) networks. However, the large number of antennas significantly increases the computational complexity in precoding design, even for linearly regularized zero-forcing (RZF) precoding. To address this issue, a series of low-complexity iterative precoding algorithms are investigated. The …

This paper seeks an efficient algorithm for stochastic precoding to maximize the long-term average weighted sum rates throughout a multiple-input multiple-output (MIMO) network. Unlike many existing works that assume a particular probability distribution model for fading channels (which is typically Gaussian), our approach merely relies on the first and second moments of fading channels. For the …

Federated learning enables decentralized model training across multiple clients without exchanging raw data, making it a crucial paradigm for privacy-preserving machine learning. However, its deployment in adversarial and dynamic environments remains fundamentally challenging, particularly under Byzantine attacks. Existing online Byzantine-robust methods face two critical limitations: (i) a stron…

We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, i…

The recovery of block-sparse signals with unknown structural patterns remains a fundamental challenge in structured sparse signal reconstruction. By proposing a variance transformation framework, this paper unifies existing pattern-based block sparse Bayesian learning methods, and introduces a novel space power prior based on undirected graph models to adaptively capture the unknown patterns of b…

This paper introduces novel Bellman mappings (BMaps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected graph/network with arbitrary topology—but without a centralized node, that is, a node capable of aggregating all data and performing computations. Each agent constructs a nonparametric B-Map from its private data, operat…

In this paper, we propose SubLoRA, a rank determination method for Low-Rank Adaptation (LoRA) based on submodular function maximization. In contrast to prior approaches, such as AdaLoRA, that rely on first-order (linearized) approximations of the loss function, SubLoRA utilizes second-order information to capture the potentially complex loss landscape by incorporating the Hessian matrix. We show …