Fundamental Limits of MIMO-OTFS and MIMO-OFDM in High-Dynamics ISAC: An Antenna Array Architecture Perspective

This paper investigates the fundamental limits of MIMO-OTFS and MIMO-OFDM integrated sensing and communications (ISAC) systems in high-mobility environments, specifically comparing sparse arrays (SA) against conventional uniform linear arrays (ULA). High-dynamics scenarios, such as V2X and satellite networks, suffer from severe Doppler shifts and rapidly time-varying channels, necessitating robust modulation schemes and efficient array geometries. A unified theoretical analysis of ergodic channel capacity and the Cramér$\unicode{x2013}$Rao bound (CRB) for angle estimation is provided. Utilizing the framework of stochastic majorization, the study reveals that SAs consistently outperform ULAs by creating a more $\unicode{x201C}$uniform$\unicode{x201D}$ spatial eigenvalue distribution, which decorrelates the multipath environment and increases communication capacity. For sensing, the paper proves that the angle CRB is inversely proportional to the array's second-order moment of antenna positions asymptotically, demonstrating that SAs achieve superior accuracy$\unicode{x2014}$improving by up to the square of the number of antennas$\unicode{x2014}$due to their increased physical aperture. Notably, the analysis shows that under relatively ideal conditions, MIMO-OTFS and MIMO-OFDM share similar fundamental limits for both capacity and angle estimation, suggesting that spatial geometry, rather than waveform, is the primary driver of fundamental performance gains in the spatial dimension.

Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

Integrated sensing and communication (ISAC) has emerged as a key technology for 6G systems. To support the development of ISAC systems, accurate channel modeling and simulation for performance evaluation is essential. Recently, 3GPP introduced a standardized ISAC channel model and its associated calibration procedure for this purpose. However, due to the complexity of the modeling methodology and the lack of fully explicit implementation details in the 3GPP reports, different implementations may lead to inconsistent or unsynchronized simulation results. To address this issue, in this work, we implement the 3GPP ISAC channel model simulator specified in TR 38.901 and conduct a comprehensive calibration analysis. We compare the simulation results with the reference results reported by companies in 3GPP and discuss several key implementation details to provide insights into the implementation and calibration of the simulator. To facilitate reproducibility and further research, the developed simulator, together with the relevant datasets and calibration results, has been released as an open-source project on GitHub.

Design of APSK Constellations Approaching the Communication-Sensing Pareto Boundary for ISAC

We propose a semi-analytical amplitude phase shift keying (APSK) signaling framework for integrated sensing and communication (ISAC), focusing on i.i.d. uniform discrete input distributions for practicality and analytical tractability. First, we establish APSK design criteria in which communication performance is measured by the gap to capacity and linked to the minimum Euclidean distance, while sensing performance is characterized by the symbol-energy variance. Based on these criteria, we propose a family of APSK constellations whose key parameters follow explicit scaling laws. Then we prove that this design achieves a constant gap to capacity independent of the signal-to-noise ratio. Building upon this foundation, we further construct a parametric APSK family that bridges the communication-optimal and sensing-optimal designs, with the communication and sensing (C&S) tradeoff controlled by the number of rings and energy allocation among rings. Simulation results show that the proposed APSK achieves C&S performance very close to the Pareto boundary achieved with time-independent, circularly symmetric, and otherwise unconstrained continuous input distributions.

Digital and Hybrid Precoding and RF Chain Selection Designs for Energy Efficient Multi-User MIMO-OFDM ISAC Systems

Using multiple-input multiple-output (MIMO) with orthogonal frequency division multiplexing (OFDM) for integrated sensing and communication (ISAC) has attracted considerable attention in recent years. While most existing works focus on improving MIMO-OFDM ISAC performance, the impact of transmit power and radio-frequency (RF) circuit power consumption on energy efficiency (EE) remains relatively underexplored. To address this gap, this paper investigates joint precoding and RF chain selection for multi-user MIMO-OFDM ISAC systems, and develops energy-efficient designs for both fully digital and hybrid precoding architectures through the joint optimization of precoding and RF-chain activation. Specifically, we first formulate a novel EE maximization problem subject to sensing performance constraints. Then, efficient optimization algorithms are proposed for both architectures, together with analyses of their computational complexity and convergence behavior. Building on the proposed approaches, spectral efficiency-power consumption tradeoff designs are also provided. Simulation results demonstrate that, compared with existing schemes, the proposed approaches achieve significant improvements in the EE-sensing tradeoff for ISAC systems.