Integrated sensing and communication (ISAC) enables wireless systems to reuse communication signals for environmental sensing, where reconstructing the geometry of surrounding objects is a representative sensing task. However, many conventional methods rely on coherent processing and require accurate phase information, which is often hard to guarantee in practical communication systems, particularly at high carrier frequencies. To address this problem, this paper proposes a Multi-View Likelihood Accumulation Geometry Reconstruction (MVLA-GR) method based on channel impulse response (CIR) measurements, which uses only delay and power observations without requiring phase information. The method extracts dominant multipath components from each observation, and for each candidate spatial location, accumulates components across views whose propagation distances match the location as supporting evidence. A soft distance-matching kernel is introduced to tolerate range estimation errors and viewpoint-dependent scattering migration, and the received power of each component is used as a reliability weight. A joint thresholding strategy combining response magnitude and angular support continuity then converts the continuous support map into a binary geometry estimate. Ray-tracing simulations on canonical and complex targets, as well as real-world vehicle measurements at 36 GHz, demonstrate that MVLA-GR can effectively recover target geometry, providing a low-complexity phase-free solution for ISAC.
Bowei Xing, Yu-Xiang Zhang, Jian-Hua Zhang et al.· 0 citations
The new mid-band (FR3, 6-24 GHz) spectrum is expected to play an important role in future 6G networks by providing a favorable balance among coverage, capacity, and deployment feasibility. Meanwhile, extremely large-scale multiple-input multiple-output (XL-MIMO) has emerged as a key enabling technology to exploit the propagation and spatial multiplexing potential of these frequency bands. Firstly, this paper provides a systematic review of spectrum allocation and standardization activities for new mid-band spectrum, together with the 6G spectrum planning strategies of countries and regions. Secondly, the wideband massive MIMO channel sounder is also introduced, which is specially developed for channel measurements of new mid-band with over a thousand elements. Thirdly, propagation characteristics and channel modeling approaches of four representative XL-MIMO architectures, including co-located, cell-free, and intelligent XL-MIMO, are comprehensively reviewed and analyzed, with particular emphasis on near-field propagation, spatial non-stationarity, and capacity performance. Then, recent advances in channel estimation, beamforming, and artificial-intelligence-assisted signal processing are summarized. In addition, the performance of new mid-band XL-MIMO systems equipped with 1536 and 768 antenna elements is comparatively evaluated. Finally, real communication environment prototype system field trials conducted in the Upper 6 GHz (U6GHz) band are used to investigate practical system performance under realistic deployment conditions. The results indicate that the target signal-to-noise ratio is a critical factor affecting XL-MIMO performance in the U6GHz band.
Haiyang Miao, Jianhua Zhang, Feifei Gao et al.· 0 citations
The integration of sensing, communication, and intelligence is becoming a key enabler for sixth generation (6G) wireless systems, where intelligent terminals are expected to simultaneously support efficient link establishment and reliable environmental sensing. However, existing studies mainly exploit sensing information or communication information to address a single task, such as channel prediction or environment reconstruction. Motivated by the shared dependence of optical and radio-frequency signals on the surrounding environment, we propose the electromagnetic world model (EMWM), the first unified framework for joint environment reconstruction and channel prediction. EMWM learns a common electromagnetic representation with the potential to provide a modeling foundation for 6G tasks. Specifically, partial channel state information (CSI) and multi-view red-green-blue (RGB) images are encoded into CSI and visual tokens and jointly processed by a hierarchical world-model backbone with local and global aggregation. Based on the learned representation, a mixture-of-experts (MoE)-based CSI prediction head reconstructs the complete CSI, while a depth prediction head estimates multi-view depth maps that are further converted into three-dimensional (3D) point clouds. Moreover, a large-scale multi-modal dataset is constructed based on a campus digital twin. Experimental results show that EMWM outperforms conventional neural network and large language model (LLM) baselines in both CSI prediction and environment reconstruction, achieving a squared generalized cosine similarity (SGCS) of 0.9699 for CSI prediction while demonstrating robustness across different signal-to-noise ratio (SNR) conditions and zero-shot generalization at 28 GHz.
Yizhu Zhao, Li Yu, Jian-Hua Zhang et al.· 0 citations
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