Markku Juntti

Integrated sensing and communications (ISAC) has emerged as a promising paradigm to unify wireless communications and radar sensing, enabling efficient spectrum and hardware utilization. A core challenge for realizing the gains of ISAC stems from dual-functional beamforming design where the key performance metrics, such as communications sum rate and sensing Cramér–Rao lower bound (CRLB), present highly non-convex formulations. In this paper, we propose a low-complexity structured approach to ISAC beamforming optimization to simultaneously enhance spectral efficiency and sensing accuracy. Specifically, we develop a successive convex approximation (SCA)-based algorithm that transforms the original non-convex problem into a sequence of convex subproblems ensuring convergence to a stationary point. Furthermore, leveraging the proposed SCA framework and Lagrange duality, we derive the optimal beamforming structure for CRLB minimization in ISAC systems. Our analysis reveals the maximum reduction in radar streams without affecting the sensing performance. This finding enables a dimensionality reduction that enhances computational efficiency. Numerical simulations validate that our approach achieves comparable or superior performance to the considered benchmarks while requiring significantly lower computational complexity.

The D-band frequencies (110–170 GHz) are expected to play a key role in the development of sixth-generation (6G) and beyond wireless systems, as the growing demand for bandwidth and capacity continues to drive innovation in intelligent transport systems (ITS). This is largely due to the high bandwidth availability and lower atmospheric loss in this band compared to other frequencies above 100 GHz. This paper focuses on deriving a channel frequency response for D-band communications using a geometric-based stochastic channel model (GBSM), specifically considering measurements conducted in both indoor and out door environments for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This study analyzes the statistics of normalized path gain (NPG), normalized path delay (NPD), number of paths (NoP), and azimuth angular spread. The goal is to provide a foundational channel model for the development of signal processing algorithms and antenna performance evaluation for D-band systems. We evaluate the proposed model against an existing channel model and empirical data, focusing on effective channel rank, path loss, and maximum excess delay (MED).

Light detection and ranging (LiDAR) systems are a key technology for sensing in terms of ranging and velocity of a mobile target for various environments, such as terrestrial, satellite, underwater, etc. However, the turbulence-induced fading and pointing errors are inherent detrimental effects in laser communications. In this paper, we study both the disorientation and misalignment errors of LiDAR systems over Gamma-Gamma turbulence fading channels. The analysis puts emphasis on either time of flight (ToF) or frequency-modulated continuous wave (FMCW) LiDAR ranging and velocity precision and provides the theoretical frameworks for deriving the aforementioned key performance indicators. In more detail, we extract closed-form Cramér-Rao Lower Bounds (CRLBs) for ranging and velocity precision and validation via Monte Carlo simulations. As a final step, we extract the operational bounds of both ToF and FMCW LiDARs due to the joint effect of disorientation and misalignment.

In this paper, we develop a tractable analytical framework for a three-dimensional (3D) indoor terahertz (THz) communication system to theoretically assess the impact of the pointing error on its coverage performance. Specifically, we model the locations of access points (APs) using a Poisson point process, human blockages as random cylinder processes, and wall blockages through a Boolean straight line process. A pointing error refers to beamforming gain and direction mismatch between the transmitter and receiver. We characterize it based on the inaccuracy of location estimate. We then analyze the impact of this pointing error on the received signal power and derive a tractable expression for the coverage probability, incorporating the multi-cluster fluctuating two-ray distribution to accurately model small-scale fading in THz communications. Aided by simulation results, we corroborate our analysis and demonstrate that the pointing error has a pronounced impact on the coverage probability. Specifically, we find that merely increasing the antenna array size is insufficient to improve the coverage probability and mitigate the detrimental impact of the pointing error, highlighting the necessity of advanced estimation techniques in THz communication systems.

This work provides a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication system in the presence of phase errors. We first derive the exact expressions for the first and second moments of the maximum signal-to-noise ratio at the receiver. We then use these moments to obtain highly accurate and insightful expressions for the outage probability, average symbol error probability (SEP), and ergodic rate (ER) for various phase error distributions, leveraging the moment-matching approach. Furthermore, we present closed-form asymptotic expressions for these performance metrics. The derived expressions are then leveraged to gain important insights into the impact of phase errors on the scaling laws of diversity order, coding gain, and ER. The numerical results show that the presence of phase errors can significantly degrade the system’s diversity order and coding gain. Specifically, we show that, in the absence of phase errors, the diversity order scales linearly with the number of RIS elements, while the diversity order reduces to unity in the presence of uniformly distributed phase errors. Finally, the analytical findings are demonstrated through extensive simulation results.

Symbol-level precoding (SLP) is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SLP-DFRC signal design problem which optimizes the radar performance under communication performance constraints. We show that a common phase modulation applied to the transmit signals from an antenna array does not affect the performance of different radar sensing metrics, including beampattern similarity, signal-to-interference-plus-noise ratio (SINR), and Cramér-Rao lower bound (CRLB). We refer to this as symmetric-rotation invariance, upon which we develop low-complexity yet efficient DFRC signal design algorithms. More specifically, we propose a symmetric non-convexity (SNC)-based DFRC algorithm that relies on the non-convexity of the radar sensing metrics to identify a set of radar-only solutions. Based on these solutions, we further exploit the symmetry property of the radar sensing metrics to efficiently design the DFRC signal. We show that the proposed SNC-based algorithm is versatile in the sense that it can be applied to the DFRC signal optimization of all three sensing metrics mentioned above (beampattern, SINR, and CRLB). In addition, since the radar sensing metrics are independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, thereby reducing the computational complexity. We also develop an accelerated SNC-based algorithm that further reduces the complexity. Finally, we numerically demonstrate the superiority of the proposed algorithms compared to existing methods in terms of sensing and communication performance as well as computational requirements.