Structured Latent Dynamics in Wireless CSI via Homomorphic World Models

We introduce a self-supervised framework for learning predictive and structured representations of wireless channels by modeling the temporal evolution of channel state information (CSI) in a compact latent space. Our method casts the problem as a world modeling task and leverages the Joint Embedding Predictive Architecture (JEPA) to learn action-conditioned latent dynamics from CSI trajectories. To promote geometric consistency and compositionality, we parameterize transitions using homomorphic updates derived from Lie algebra, yielding a structured latent space that reflects spatial layout and user motion. Evaluations on the DICHASUS dataset show that our approach outperforms strong baselines in preserving topology and forecasting future embeddings across unseen environments. The resulting latent space enables metrically faithful channel charts, offering a scalable foundation for downstream applications such as mobility-aware scheduling, localization, and wireless scene understanding.

Design of Uplink ISAC Systems with Cooperative Sensing: Power Control and Receive Beamforming

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD).

Hybrid Radar Fusion with Quantization: CRB-Rate Trade-offs and ADC Dynamic Range

Recent advancements have underscored the relevance of low-resolution analog-to-digital converters (ADCs) in integrated sensing and communication (ISAC) systems. Nevertheless, their specific impact on hybrid radar fusion (HRF) remains largely unexplored. In HRF systems, where uplink (UL) paths carry direct and reflected signals in the same frequency band, the reflected signal is often significantly weaker, making HRF performance particularly sensitive to ADC resolution. To study this effect, we use the quantized Cramér-Rao bound (CRB) to measure sensing accuracy. This work derives an upper bound on the quantized CRB for angle of arrival (AoA) estimation and explores CRB-rate trade-offs through two formulated optimization problems. Simulation results indicate that HRF becomes infeasible when the dynamic range of the received signal exceeds the dynamic range supported by the ADC, which is inherently limited by its resolution. Furthermore, the UL communication rate does not increase significantly when the ADC resolution is raised beyond a certain threshold. These observations highlight a fundamental trade-off between sensing and communication performance: while HRF performance benefits from higher ADC resolutions, the corresponding gains in communication rate plateau. This trade-off is effectively characterized using CRB-rate boundaries derived through simulation.

Flexible Intelligent Metasurface for Downlink Communications under Statistical CSI

Flexible intelligent metasurface (FIM) is a recently developed, groundbreaking hardware technology with promising potential for 6G wireless systems. Unlike conventional rigid antenna array (RAA)-based transmitters, FIM-assisted transmitters can dynamically alter their physical surface through morphing, offering new degrees of freedom to enhance system performance. In this letter, we depart from prior works that rely on instantaneous channel state information (CSI) and instead address the problem of average sum spectral efficiency maximization under statistical CSI in a FIM-assisted downlink multiuser multiple-input single-output setting. To this end, we first derive the spatial correlation matrix for the FIM-aided transmitter and then propose an iterative FIM optimization algorithm based on the gradient projection method. Simulation results show that with statistical CSI, the FIM-aided system provides a significant performance gain over its RAA-based counterpart in scenarios with strong spatial channel correlation, whereas the gain diminishes when the channels are weakly correlated.

From OFDM to AFDM: Enabling Adaptive Integrated Sensing and Communication in High-Mobility Scenarios

Integrated sensing and communication (ISAC) is a key feature of next-generation wireless networks, enabling a wide range of emerging applications such as vehicle-to-everything (V2X) and unmanned aerial vehicles (UAVs), which operate in high-mobility scenarios. Notably, the wireless channels within these applications typically exhibit severe delay and Doppler spreads. The latter causes serious communication performance degradation in the Orthogonal Frequency-Division Multiplexing (OFDM) waveform that is widely adopted in current wireless networks. To address this challenge, the recently proposed Doppler-resilient affine frequency division multiplexing (AFDM) waveform, which uses flexible chirp signals as subcarriers, shows great potential for achieving adaptive ISAC in high-mobility scenarios. This article provides a comprehensive overview of AFDM-ISAC. We begin by presenting the fundamentals of AFDM-ISAC, highlighting its inherent frequency-modulated continuous-wave (FMCW)-like characteristics. Then, we explore its ISAC performance limits by analyzing its diversity order, ambiguity function (AF), and Cramer-Rao Bound (CRB). Finally, we present several effective sensing algorithms and opportunities for AFDM-ISAC, with the aim of sparking new ideas in this emerging field.

3GPP-Compliant Radar Cross Section Characterization of Indoor Factory Targets

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25-28 GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (<1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. The robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas UAVs show clear size-dependent reflectivity patterns in AGVs. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.

Towards ISAC RIS-Enabled Passive Radar Target Localization

Incorporating integrated sensing and communication capabilities into forthcoming 6G wireless networks is crucial for achieving seamless synchronization between the digital and physical worlds. The following paper focuses on a scenario where a passive radar (PR) is subject to weak line-of-sight signals of opportunity, emanating from an access point and subsequently reflecting off targets, ultimately reaching the PR. Furthermore, a normalized least mean squares method is presented for jointly detecting the number of targets and estimating target angles of arrival (AoAs). The algorithm iteratively adjusts the steering vector estimates to minimize a suitable error cost function, while the target AoAs are identified via a peak-finding search conducted on the resulted power spectrum. Simulation results show the capabilities of the proposed localization method, as well as a 14 dB dynamic range reduction that can be achieved at the PR.

Designing Waveforms with Adjustable PAPR for Integrated Sensing and Communication

This paper presents a new optimization framework dedicated for integrated sensing and communication (ISAC) waveform design. In particular, the problem aims at maximizing the total achievable sum-rate, through multi-user interference minimization, while preserving a certain level of similarity to a given desired radar waveform. Aiming towards feasible and practical PHY architectures, we also offer the flexibility of tuning the peak-to-average power ratio to a desired level. Towards this design, a non-convex optimization problem is formulated, and an alternating direction method of multipliers based solution is derived to converge towards the superiority of the final ISAC waveform. Finally, simulation results validate the proposed ISAC waveform design, as compared to state-of-the-art solutions.

Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges

Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. $7.125$-$24.25$ $\GHz$, by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this \textcolor{black}{never-before} used spectrum by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity (e.g. data rate) tradeoffs, while also examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output, compact and digital architectures, and evaluate the potential of multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.

Statistical and Deterministic RCS Characterization for ISAC Channel Modeling

In this study, we perform a statistical analysis of the radar cross section (RCS) for various test targets in an indoor factory at \(25\)-\(28\) GHz, with the goal of formulating parameters that may be used for target identification and other sensing applications for future wireless systems. The analysis is conducted based on measurements in monostatic and bistatic configurations for bistatic angles of \(20^\circ\), \(40^\circ\), and \(60^\circ\), which are functions of transmitter-receiver (T-R) and target positions, via accurate \(3\)dB beamwidth of \(10^\circ\) in both azimuth and elevation planes. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. We utilize parametric statistical distributions to fit the measured RCS data. The analysis reveals that the \textit{lognormal and gamma distributions} are effective in modeling the RCS of the test targets over different reflecting points of the target itself, i.e. when target is in motion. Additionally, we provide a framework for evaluating the deterministic bistatic RCS of a rectangular sheet of laminated wood, due to its widespread use in indoor hotspot environments. Novel deterministic and statistical RCS models are evaluated, incorporating dependencies on the bistatic angle, T-R distance (\(2\)m -\(10\)m) and the target. The results demonstrate that some proposed RCS models accurately fit the measured data, highlighting their applicability in bistatic configurations.