Radar Operating Metrics and Network Throughput for Integrated Sensing and Communications in Millimeter-wave Urban Environments

Millimeter wave integrated sensing and communication (ISAC) systems are being researched for next-generation intelligent transportation systems. Here, radar and communication functionalities share a common spectrum and hardware resources in a time-multiplexed manner. The objective of the radar is to first scan the angular search space and detect and localize mobile users/targets in the presence of discrete clutter scatterers. Subsequently, this information is used to direct highly directional beams toward these mobile users for communication service. The choice of radar parameters such as the radar duty cycle and the corresponding beamwidth are critical for realizing high communication throughput. In this work, we use the stochastic geometry-based mathematical framework to analyze the radar operating metrics as a function of diverse radar, target, and clutter parameters and subsequently use these results to study the network throughput of the ISAC system. The results are validated through Monte Carlo simulations.

Reconfigurable Low-Complexity Architecture for High Resolution Doppler Velocity Estimation in Integrated Sensing and Communication System

In millimeter wave integrated sensing and communication (ISAC) systems for intelligent transportation, radar and communication share spectrum and hardware in a time division manner. Radar rapidly detects and localizes mobile users (MUs), after which communication proceeds through narrow beams identified by radar. Achieving fine Doppler resolution for MU clutter discrimination requires long coherent processing intervals, reducing communication time and throughput. To address this, we propose a reconfigurable architecture for Doppler estimation realized on a system on chip using hardware software codesign. The architecture supports algorithm level reconfiguration, dynamically switching between low-complexity, high-speed FFT-based coarse estimation and high complexity ESPRIT based fine estimation. We introduce modifications to ESPRIT that achieve 6.7 times faster execution while reducing memory and multiplier usage by 79% and 63%, respectively, compared to state of the art approaches, without compromising accuracy. Additionally, the reconfigurable architecture can switch to lower slow time packets under high SNR conditions, improving latency further by 2 times with no loss in performance.

Beam Alignment in Multipath Environments for Integrated Sensing and Communication using Bandit Learning

Prior works have explored multi-armed bandit (MAB) algorithms for the selection of optimal beams for millimeter-wave (mmW) communications between base station and mobile users. However, when the number of beams is large, the existing MAB algorithms are characterized by long exploration times, resulting in poor overall communication throughput. In this work, we propose augmenting the upper confidence bound (UCB) based MAB with integrated sensing and communication (ISAC) to address this limitation. The premise of the work is that the radar and communication functionalities share the same field-of-view and that communication mobile users are detected by the radar as mobile targets. The radar information is used for significantly reducing the number of candidate beams for the UCB, resulting in an overall reduction in the exploration time. Further, the radar information is used to estimate the realignment time in quasi-stationary scenarios. We have realized the MAB and radar signal processing algorithms on the system on chip (SoC) via hardware-software co-design (HSCD) and fixed-point analysis. We demonstrate the significant gain in execution time using accelerators. The simulations consider complex propagation channels involving direct and multipath, with simple and extended radar targets in the presence of significant static clutter. The resulting experiments show that the proposed ISAC-based MAB achieves a 35% reduction in the overall exploration time and 1.4 factor higher throughput as compared to the conventional MAB that is based only on communications.

Riemannian Manifold Optimization for Advanced Wireless Communications: Fundamentals and Applications

Next-generation wireless communications promise transformative technologies such as massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and fluid antenna systems (FAS). However, deploying these technologies is hindered by large-scale optimization problems with nonconvex constraints. Conventional Euclidean-space methods rely on approximations or relaxations, which degrade performance and incur substantial computational costs. Riemannian manifold optimization (RMO) offers a powerful alternative that directly operates on the manifold defined by the geometric constraints. This approach inherently satisfies the constraints at every optimization step, thereby avoiding the performance degradation and substantial computational costs. In this paper, we first elaborate on the principles of RMO, including the fundamental concepts, tools, and methods, emphasizing its effectiveness for nonconvex problems. We then introduce its applications in advanced wireless communications, showing how constrained problems are reformulated on their natural manifolds and solved using tailored RMO algorithms. Furthermore, we present a case study on secure beamforming in an FAS-assisted non-orthogonal multiple access (NOMA) system, demonstrating RMO's superiority over conventional methods in terms of both performance and computational efficiency.

Optimal Transmit Beamforming for MIMO ISAC with Unknown Target and User Locations

This paper studies a challenging scenario in a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system where the locations of the sensing target and the communication user are both unknown and random, while only their probability distribution information is known. In this case, how to fully utilize the spatial resources by designing the transmit beamforming such that both sensing and communication can achieve satisfactory performance statistically is a difficult problem, which motivates the study in this paper. Moreover, we aim to reveal if it is desirable to have similar probability distributions for the target and user locations in terms of the ISAC performance. Firstly, based on only probability distribution information, we establish communication and sensing performance metrics via deriving the expected rate or posterior Cramér-Rao bound (PCRB). Then, we formulate the transmit beamforming optimization problem to minimize the PCRB subject to the expected rate constraint, for which the optimal solution is derived. It is unveiled that the rank of the optimal transmit covariance matrix is upper bounded by the summation of MIMO communication channel matrices for all possible user locations. Furthermore, due to the need to cater to multiple target/user locations, we investigate whether dynamically employing different beamforming designs over different time slots improves the performance. It is proven that using a static beamforming strategy is sufficient for achieving the optimal performance. Numerical results validate our analysis, show that ISAC performance improves as the target/user location distributions become similar, and provide useful insights on the BS-user/-target association strategy.

IEEE 802.11ad-Aided 5-D Sensing with a UAV Swarm in Urban Environment

Aerial base stations mounted on unmanned aerial vehicles (UAVs) support next-generation wireless networks in challenging environments such as urban areas, disaster zones, and remote locations. Further, UAV swarms overcome the challenges of limited battery life and other operational constraints of a single UAV. However, tracking mobile users on the ground by each UAV and the corresponding synchronization between the UAVs is a significant issue that must be addressed before this framework can be deployed in reality. Incorporating additional sensing capabilities to facilitate this additional requirement would introduce significant overhead in terms of hardware, cost, and power to each UAV. Instead, we propose an integrated sensing and communications-enabled swarm UAV system, based on the millimeter-wave IEEE 802.11ad protocol. Further, we show that our proposed system is capable of five-dimensional (5-D) ground target sensing (range, Doppler velocity, azimuth, elevation, and polarization) in an urban environment. Numerical experiments using realistic models demonstrate and validate the performance of 5-D sensing using our proposed 802-11ad-aided UAV system.

AFDM: Evolving OFDM Towards 6G+

As the standardization of sixth generation (6G) wireless systems accelerates, there is a growing consensus in favor of evolutionary waveforms that offer new features while maximizing compatibility with orthogonal frequency division multiplexing (OFDM), which underpins the 4G and 5G systems. This article presents affine frequency division multiplexing (AFDM) as a premier candidate for 6G, offering intrinsic robustness for both high-mobility communications and integrated sensing and communication (ISAC) in doubly dispersive channels, while maintaining a high degree of synergy with the legacy OFDM. To this end, we provide a comprehensive analysis of AFDM, starting with a generalized fractional-delay-fractional-Doppler (FDFD) channel model that accounts for practical pulse shaping filters and inter-sample coupling. We then detail the AFDM transceiver architecture, demonstrating that it reuses nearly the entire OFDM pipeline and requires only lightweight digital pre- and post-processing. We also analyze the impact of hardware impairments, such as phase noise and carrier frequency offset, and explore advanced functionalities enabled by the chirp-parameter domain, including index modulation and physical-layer security. By evaluating the reusability across the radio-frequency, physical, and higher layers, the article demonstrates that AFDM provides a low-risk, feature-rich, and efficient path toward achieving high-fidelity communications in the later versions of 6G and beyond (6G+).

MI-ISAC: Magneto-Inductive Integrated Sensing and Communication in the Reactive Near-Field for RF-Denied Environments

Radio-frequency integrated sensing and communication (RF-ISAC) is ineffective inunderground, underwater, and in-body environments where conductive media attenuate electromagnetic waves by tens of dB per meter. This article presents magneto-inductive ISAC (MI-ISAC), a paradigm that exploits the reactive near-field quasi-static coupling inherent to MI links, enabling a fundamentally different approach to ISAC in these RF-denied environments. Five foundational results are established: (i)~tri-axial coils are necessary and sufficient for identifiable joint range-and-angle estimation; (ii)~coupling strength changes sharply with range, enabling theoretical sub-millimeter accuracy at typical MI distances despite kHz-level bandwidth; (iii)~time-of-flight is ineffective under such narrow bandwidth, but the coupling gradient provides approximately six orders of magnitude finer resolution; (iv)~MI-ISAC can provide 4--10+\,dB sensing gain over time-division baselines; and (v)~the MI-MIMO channel is geometry-invariant and well-conditioned across all orientations. Applications and a research roadmap are discussed.

Message Passing based Parameter Estimation in Cooperative MIMO-OFDM ISAC Systems

In integrated sensing and communication (ISAC) networks, multiple base stations (BSs) collaboratively sense a common target, leveraging diversity from multiple observation perspectives and joint signal processing to enhance sensing performance. This paper introduces a novel message-passing (MP)-based parameter estimation framework for collaborative MIMO-OFDM ISAC systems, which jointly estimates the target's position and velocity. First, a signal propagation model is established based on geometric relationships, and a factor graph is constructed to represent the unknown parameters. The sum-product algorithm (SPA) is then applied to this factor graph to jointly estimate the multi-dimensional parameter vector. To reduce communication overhead and computational complexity, we employ a hierarchical message-passing scheme with Gaussian approximation. By adopting parameterized message distributions and layered processing, the proposed method significantly reduces both computational complexity and inter-BS communication overhead. Simulation results demonstrate the effectiveness of the proposed MP-based parameter estimation algorithm and highlight the benefits of multi-perspective observations and joint signal processing for cooperative sensing in MIMO-OFDM ISAC systems.

Optimal Low-Dimensional Structures of ISAC Beamforming: Theory and Efficient Algorithms

Transmit beamforming design is a fundamental problem in integrated sensing and communication (ISAC) systems. Numerous methods have been proposed to jointly optimize key performance metrics such as the signal-to-interference-plus-noise ratio and Cramér-Rao bound. However, the computational complexity of these methods often grows rapidly with the number of transmit antennas at the base station (BS). To tackle this challenge, we prove a fundamental structural property of the ISAC beamforming problem, i.e., there exists an optimal solution exhibiting a low-dimensional structure. This leads to an equivalent reformulation of the problem with dimension related to the number of users rather than the number of BS antennas, thereby enabling the development of low-complexity algorithms. When applying the interior-point method to the reformulated problem, we achieve up to six orders of magnitude in complexity reduction when the number of antennas exceeds the number of users by an order of magnitude. To further reduce the complexity, we develop a balanced augmented Lagrangian method to solve the reformulated problem. The proposed algorithm maintains optimality while achieving a computational complexity that scales quartically with the number of users. Our simulation results demonstrate that the proposed R-BAL method can achieve a speedup of more than 10000$\times$ over the conventional IPM in massive MIMO scenarios.