This paper proposes a novel multi-user multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO-OFDM) based integrated sensing and communication (ISAC) framework. By partitioning the time-frequency resource grid into sub-blocks, the architecture enables 4D sensing (range, velocity, azimuth, elevation) and facilitates radar data cube formation for high-speed standard radar processing. A key innovation is a radar supplement signal featuring double-orthogonality to both the communication channel and signal. Unlike conventional null-space projection (NSP) methods, which transmit radar signals solely through the channel’s null-space, our approach explicitly eliminates the cross-correlation between sensing and communication signals by exploiting the communication signal’s null-space. This minimizes radar estimation error while strictly preserving communication performance. Furthermore, we derive an optimal power allocation strategy and incorporate stabilization techniques, such as block selection, to ensure robust sensing in practical environments. Simulation results demonstrate that the proposed framework achieves superior radar detection performance with an average precision (AP) of 0.90, significantly outperforming both the separate resource allocation (SRA) method, which assigns distinct resources to communication and radar, and schemes that neglect orthogonality with respect to the communication signal. These gains are achieved while maintaining spectral efficiency comparable to a communication-only baseline, effectively validating the efficacy of the stabilized ISAC architecture.
Kyung In Lee, Ju Hyeon Kim, Dong In Kim et al.· IEEE Transactions on Wireles...· 0 citations
Wireless localization is expected to play a key role in future communication systems by providing location-aware services and supporting efficient network operation. However, existing deep learning (DL)-based localization methods often suffer from limited generalization when the deployment environment changes, since they tend to learn environment-specific propagation patterns. To address this issue, this article proposes an environment-aware generalized wireless localization framework that jointly exploits wireless channel, base station (BS) geometric information, and environmental information. Irregular city structures are represented by voxel-based occupancy maps, enabling explicit modeling of environmental factors that affect radio propagation. A transformer-based architecture is developed to comprehensively process wireless channel, geometric information of network nodes, and environmental information, thereby capturing the interaction between channel observations and surrounding urban structures. In addition, the proposed framework estimates a confidence map instead of directly regressing user equipment (UE) coordinates, which improves robustness under ambiguous propagation conditions. To support training and evaluation, we also develop an urban environment generator and a ray tracing-based channel simulator that produce large-scale datasets with physically consistent alignment between channels and 3-D environments. This framework enables systematic evaluation and robust localization in previously unseen urban environments.
Y. Noh, Kae Won Choi· IEEE Internet of Things Jour...· 0 citations
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