The ongoing convergence of spectrum and hardware requirements for wireless sensing and communication applications has fueled the integrated sensing and communication (ISAC) paradigm in next-generation networks. Neural-network-based ISAC leverages data-driven learning techniques to add sensing capabilities to existing communication infrastructure. This paper presents a novel signal-processing framework for such neural ISAC systems based on the multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) downlink. Our approach enables generalized sensing functionality without modifying the MIMO-OFDM communication link. Specifically, our neural ISAC pipeline measures the backscattered communication signals to generate discrete map representations of spatial occupancy, formulated as multiclass or multilabel classification problems, which can then be utilized by specialized downstream tasks. To improve sensing performance in closed or cluttered environments, our neural ISAC pipeline relies on features specifically designed to mitigate strong reflective paths. Extensive simulations using ray-tracing models demonstrate that our neural ISAC framework reliably reconstructs scene maps without altering the MIMO-OFDM communication pipeline or reducing data rates.

We introduce a novel class of regularization functions, called Cauchy–Schwarz (CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems. To demonstrate the versatility of CS regularizers, we derive regularization functions that promote discrete-valued vectors, eigenvectors of a given matrix, and orthogonal matrices. The resulting CS regularizers are simple, differentiable, and can be free of spurious stationary points, making them suitable for gradient-based solvers and large-scale optimization problems. In addition, CS regularizers automatically adapt to the appropriate scale, which is, for example, beneficial when discretizing the weights of neural networks. To demonstrate the efficacy of CS regularizers, we provide results for solving underdetermined systems of linear equations and weight quantization in neural networks. Furthermore, we discuss specializations, variations, and generalizations, which lead to an even broader class of new and possibly more powerful regularizers.

Source localization is essential for a wide range of applications. The efficiency of source localization in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). In this paper, we investigate the information inequality of RIS-aided localization systems. We propose a general signal model for RIS-aided localization valid for both near-field and far-field scenarios. Based on the proposed model, we perform Fisher information analyses of localization performance in networks with RISs. Numerical results show that optimal-configured RISs can improve the localization accuracy significantly.

The wideband system plays a important role in high-accuracy location awareness. In beyond 5G networks, reconfigurable intelligent surfaces (RISs) are proposed to control the complex wireless environments. However, existing works related to RISs mainly focus on narrow-band systems due to the frequency-selectivity of conventional metasurfaces. This paper first presents the general signal model of wideband systems with RISs, and then performs Fisher information analysis to determine the theoretical limits of wideband localization with RISs. Furthermore, special scenarios including complete coupling scenarios and complete decoupling scenarios are discussed and evaluated. The simulation result shows that RISs in complete coupling scenarios provided more improvement, in terms of squared position error bound (SPEB), compared with that in complete decoupling scenarios and random configuration.

Achieving accurate location-awareness in wireless networks requires integrated sensing and communication (ISAC), where optimization, signal processing, and data fusion are performed under a common framework. The efficiency of ISAC in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). This paper introduces the concept of continuous intelligent surface (CIS) and establishes the fundamental limits of RIS-aided ISAC systems, specifically, an RIS-aided localization and communication system. In particular, this paper considers two types of RISs, namely CISs and discrete intelligent surfaces (DISs). First, this paper proposes a general signal model for RIS-aided localization and communication valid for both near-field and far-field scenarios, and then theoretical limits on the localization and communication performance are derived. Based on the proposed model, Fisher information analyses of the localization performance in networks with RISs are performed. Numerical results show that RISs with optimized phase responses can improve the received signal-to-noise ratio (SNR) and spectral efficiency of communication, and the localization accuracy significantly.