Accurate wireless positioning of mobile agents is challenging in non-line-of-sight (NLOS) propagation environments due to unknown range or angle biases. In this paper, we develop a cooperative localization algorithm for mixed line-of-sight (LOS)/NLOS environments where the NLOS effect is mitigated by exploiting the geometric relationship of the range biases. In particular, we cast the localization problem as a detection-aided optimization program, in which all the distance measurements are initially treated as NLOS links with unknown nonnegative biases, followed by iterative agent position estimation and LOS identification. Moreover, the maximum-likelihood estimator for the agent positions and NLOS biases is relaxed into a semidefinite program where the geometric relationship of the biases is introduced as constraints. We also characterize the cooperation gain for LOS identification, and derive the constrained Cramér-Rao bound to show the localization accuracy improvement by the geometric constraints. Finally, numerical results validate the superior performance of the proposed algorithm compared with other competitive methods.

Integrated sensing and communication (ISAC), which enables hardware, resources (e.g., spectra), and waveforms sharing, is becoming a key feature in future-generation communication systems. This paper investigates performance characterization and waveform design for ISAC systems when the underlying true communication channels are not accurately known. With uncertainty in a nominal communication channel, the nominal Pareto frontier of the sensing and communication performances cannot represent the true performance trade-off of a real-world operating ISAC system. Therefore, this paper portrays the robust (i.e., conservative) Pareto frontier considering the uncertainty in the communication channel. To be specific, the lower bound of the true (but unknown) Pareto frontier is investigated, technically by studying robust waveform design problems that find the best waveforms under the worst-case channels. The robust waveform design problems examined in this paper are shown to be non-convex and high-dimensional, which cannot be solved using existing optimization techniques. As such, we propose a computationally efficient solution framework to approximately solve them. Simulation results show that by solving the robust waveform design problems, the lower bound of the true but unknown Pareto frontier, which characterizes the sensing-communication performance trade-off under communication channel uncertainty, can be obtained.

Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.

This paper develops a new mathematical framework for denoising in blind two-dimensional (2D) super-resolution upon using the atomic norm. The framework denoises a signal that consists of a weighted sum of an unknown number of time-delayed and frequency-shifted unknown waveforms from its noisy measurements. Moreover, the framework also provides an approach for estimating the unknown parameters in the signal. We prove that when the number of the observed samples satisfies certain lower bound that is a function of the system parameters, we can estimate the noise-free signal, with very high accuracy, upon solving a regularized least-squares atomic norm minimization problem. We derive the theoretical mean-squared error of the estimator, and we show that it depends on the noise variance, the number of unknown waveforms, the number of samples, and the dimension of the low-dimensional space where the unknown waveforms lie. Finally, we verify the theoretical findings of the paper by using extensive simulation experiments.

The knowledge of CSI at the BS (CSIT) is required to achieve the high spectrum efficiency promised by massive MIMO. In Frequency-Division Duplex (FDD) Massive MIMO systems, the CSIT is obtained via downlink channel estimation and uplink channel feedback, However, the acquisition of CSIT is a very challenging problem in practical FDD massive MIMO systems with a large number of antennas. Recently, compressive sensing has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, standard sparse recovery algorithms have stringent requirement on the channel sparsity level for robust channel recovery and this severely limits the operating regime of the solution. To overcome this issue, we propose a joint burst LASSO algorithm to exploit additional joint burst-sparse structure in multi-user (MU) massive MIMO channels. Simulations show that the joint burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.

Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care.