Enabling Indoor Multi-Person Tracking With 6G mmWave ISAC Systems

Integrated sensing and communication (ISAC) has emerged as a key technology for 6G wireless networks. In this paper, wireless sensing for the indoor multi-person tracking is explored with 6G mmWave ISAC systems. To limit the sensing overhead, a sparse deployment of sensing reference signals (RS) is applied in the orthogonal frequency-division multiplexing (OFDM) frame, where the channel state information (CSI) at the sensing RS is extracted for the multi-person tracking. To enable a robust tracking of multiple persons in a complex indoor environment, three key mechanisms are proposed: 1) a modified moving target indicator (MTI) scheme is proposed to remove the static environmental clutter under a sparse RS time spacing; 2) an effective target identification mechanism is developed to exclude false target points; 3) the Kalman filter with a penalty association algorithm is designed to associate the detected points with the right tracks, especially handling the crossover case of two tracks. With the above mechanisms, multiple persons can be effectively tracked with an extremely low sensing overhead. An mmWave bistatic ISAC prototype system at 26 GHz with 500 MHz bandwidth has been developed to validate our design, where the overhead of the sensing RS is less than 0.005\%. Experimental results demonstrate that our proposed design achieves a median position error of 12 cm for multi-person tracking with path-crossing in the indoor environment with a single receiver.

Waveform Design for 6G ISAC Systems Under Full-Duplex Residual Self-Interference

In this paper, the waveform design for 6G integrated sensing and communication (ISAC) systems is investigated, with a particular focus on the practical limitations imposed by imperfect full-duplex radios. Under such imperfections, continuous communication waveforms, such as OFDM, suffer from severe full-duplex residual self-interference (RSI) for radar sensing, which significantly restricts the long-range sensing capabilities required by emerging low-altitude wireless networks (LAWN). To address this challenge, we propose a novel time-division ISAC waveform that integrates a specially developed dual-power phase-coded pulse for sensing into the communication frame under full-duplex RSI. Specifically, the dual-power sensing pulse consists of a high-power sequence followed by a low-power sequence, effectively exploiting imperfect full-duplex operations to achieve reliable long-range sensing while eliminating the detection blind range inherent to conventional half-duplex pulse radars. Furthermore, a complementary and inverse-phase sequence group is designed to ensure perfect autocorrelation and robust cross-correlation sidelobe suppression, so as to enhance multi-target detection capability. As for sensing signal processing, a parameterized mismatched filter is developed and optimized to maximize the detection performance, tailored to the proposed pulse structure. In addition, we design a hierarchical one-dimensional CFAR-CA detector that can exploit the perfect range-domain autocorrelation characteristics of the proposed waveform to further improve the detection performance. Extensive simulations demonstrate that the proposed design significantly improves the maximum detection range and multi-target detection capability compared to existing OFDM and LFM pulse baselines, while effectively covering the blind range for targets with small RCS.

SSB-Based Sensing-Assisted Robust Beamforming for High-Mobility UAV Communications in LAWN

High-mobility uncrewed aerial vehicle (UAV) communications in low-altitude wireless networks (LAWN) demand reliable beamforming, while conventional feedback-based schemes suffer from excessive overhead and severe misalignment under rapid trajectory variations. To address this challenge, this paper proposes an SSB-based sensing-assisted predictive robust beamforming framework that replaces explicit channel state information (CSI) feedback with sensing-driven state estimation and uncertainty-aware optimization. Leveraging the periodic 'always-on' synchronization signal block (SSB), a hierarchical sensing algorithm tailored for hybrid digital-analog uniform planar arrays is developed, combining 2D range-velocity profiling and augmented beamspace multiple signal classification (MUSIC). By integrating a locally-focused analog receive beamformer, the proposed sensing design can ensure energy accumulates across different radio-frequency (RF) chains while resolving angular ambiguity. An extended Kalman filter (EKF) is further employed to track UAV states between sparse synchronization-signal (SS) bursts, and a covariance correction is introduced to characterize maneuver-induced prediction uncertainties. Based on the derived statistical distributions of range and angular parameters, the communication channel is modeled through predictive correlation matrices rather than instantaneous CSI, leading to a multi-user robust beamforming formulation that maximizes average network sum-rate under uncertainty. The resulting nonconvex problem is efficiently solved via successive convex approximation and alternating minimization. Simulation results demonstrate that the proposed framework significantly enhances spectral efficiency and link stability compared with feedback-based beamforming and non-robust beamforming design, particularly in high-mobility and large-SSB-interval scenarios.

Low-Complexity Soft-Feedback Detector for AFDM Systems

Affine frequency division multiplexing (AFDM), an emerging multi-carrier modulation scheme, has garnered significant attention due to its resilience to Doppler shifts and capability to achieve full diversity in doubly dispersive channels. However, existing data detection algorithms for AFDM systems face a significant trade-off between computational complexity and accuracy. In this paper, a novel low-complexity data detection scheme, termed the soft-feedback detector (SFD), is proposed. Particularly, building upon a maximum ratio combining (MRC) estimator framework, the SFD leverages the a priori symbol distribution to mitigate error propagation during iterative detection. Specifically, soft-decision feedback is incorporated as extrinsic information derived from the log-likelihood ratios of the transmitted symbols. As a result, the proposed detector significantly enhances detection accuracy while maintaining low computational complexity. Simulation results demonstrate that the SFD consistently outperforms benchmark decision-feedback detectors. In particular, compared with the conventional MRC detector, the proposed scheme achieves approximately a 3 dB signal-to-noise ratio (SNR) gain at the bit error rate (BER) of $10^{-3}$.

Experiment Study on Reference-Path-Aided System Calibration for mmWave Bistatic ISAC Systems

Integrated sensing and communications (ISAC) has been regarded as a key enabling technology for next-generation wireless networks. Compared to monostatic ISAC, bistatic ISAC can eliminate the critical challenge of self-interference cancellation and is well compatible with the existing network infrastructures. However, the synchronization between the transmitter and the sensing receiver becomes a crucial problem. The extracted channel state information (CSI) for sensing under communication synchronization contains different types of system errors, such as the sampling time offset (STO), carrier frequency offset (CFO), and random phase shift, which can severely degrade sensing performance or even render sensing infeasible. To address this problem, a reference-path-aided system calibration scheme is designed for mmWave bistatic ISAC systems, where the line-of-sight (LoS) path can be blocked. By exploiting the delay-angle sparsity feature in mmWave ISAC systems, the reference path, which can be either a LoS or a non-LoS (NLoS) path, is first identified. By leveraging the fact that all the paths suffer the same system errors, the channel parameter extracted from the reference path is utilized to compensate for the system errors in all other paths. A mmWave ISAC system is developed to validate our design. Experimental results demonstrate that the proposed scheme can support precise estimation of Doppler shift and delay, maintaining time-synchronization errors within 1 nanosecond.

Sensing-Assisted Channel Estimation for OFDM ISAC Systems: Framework, Algorithm, and Analysis

Integrated sensing and communication (ISAC) has garnered significant attention in recent years. In this paper, we delve into the topic of sensing-assisted communication within ISAC systems. More specifically, a novel sensing-assisted channel estimation scheme is proposed for bistatic orthogonal-frequency-division-multiplexing (OFDM) ISAC systems. A framework of sensing-assisted channel estimator is first developed, integrating a tailored low-complexity sensing algorithm to facilitate real-time channel estimation and decoding. To address the potential sensing errors caused by low-complexity sensing algorithms, a sensing-assisted linear minimum mean square error (LMMSE) estimation algorithm is then developed. This algorithm incorporates tolerance factors designed to account for deviations between estimated and true channel parameters, enabling the construction of robust correlation matrices for LMMSE estimation. Additionally, we establish a systematic mechanism for determining these tolerance factors. A comprehensive analysis of the normalized mean square error (NMSE) performance and computational complexity is finally conducted, providing valuable insights into the selection of the estimator's parameters. The effectiveness of our proposed scheme is validated by extensive simulations. Compared to existing methods, our proposed scheme demonstrates superior performance, particularly in high signal-to-noise ratio (SNR) regions or with large bandwidths, while maintaining low computational complexity.

Channel Modeling Framework for Both Communications and Bistatic Sensing Under 3GPP Standard

Integrated sensing and communications (ISAC) is considered a promising technology in the B5G/6G networks. The channel model is essential for an ISAC system to evaluate the communication and sensing performance. Most existing channel modeling studies focus on the monostatic ISAC channel. In this paper, the channel modeling framework for bistatic ISAC is considered. The proposed channel modeling framework extends the current 3GPP channel modeling framework and ensures the compatibility with the communication channel model. To support the bistatic sensing function, several key features for sensing are added. First, more clusters with weaker power are generated and retained to characterize the potential sensing targets. Second, the target model can be either deterministic or statistical, based on different sensing scenarios. Furthermore, for the statistical case, different reflection models are employed in the generation of rays, taking into account spatial coherence. The effectiveness of the proposed bistatic ISAC channel model framework is validated by both ray tracing simulations and experiment studies. The compatibility with the 3GPP communication channel model and how to use this framework for sensing evaluation are also demonstrated.

Interference Management for Full-Duplex ISAC in B5G/6G Networks: Architectures, Challenges, and Solutions

Integrated sensing and communications (ISAC) has been visioned as a key technique for B5G/6G networks. To support monostatic sensing, a full-duplex radio is indispensable to extract echo signals from targets. Such a radio can also greatly improve network capacity via full-duplex communications. However, full-duplex radios in existing ISAC designs are mainly focused on wireless sensing, while the ability of full-duplex communications is usually ignored. In this article, we provide an overview of full-duplex ISAC (FD-ISAC), where a full-duplex radio is used for both wireless sensing and full-duplex communications in B5G/6G networks, with a focus on the fundamental interference management problem in such networks. First, different ISAC architectures are introduced, considering different full-duplex communication modes and wireless sensing modes. Next, the challenging issues of link-level interference and network-level interference are analyzed, illustrating a critical demand on interference management for FD-ISAC. Potential solutions to interference management are then reviewed from the perspective of radio architecture design, beamforming, mode selection, and resource allocation. The corresponding open problems are also highlighted.