Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications Surveys & Tutorial

Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision. Codes will be open to all on https://gaozhen16.github.io/ soo

Time-Domain Channel Estimation for Extremely Large MIMO THz Communications with Beam Squint

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the Terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures, which leads to frequency selectivity, a problem known as beam squint. Multi-carrier transmission schemes which are usually deployed to address this problem, suffer from high peak-to-average power ratio, which is specifically dominant in THz communications where low transmit power is realized. Diverging from the usual approach, we devise a novel channel estimation problem formulation in the time domain for single-carrier (SC) modulation, which favors transmissions in THz, and incorporate the beam-squint effect in a sparse vector recovery problem that is solved via sparse optimization tools. In particular, the beam squint and the sparse MIMO channel are jointly tracked by using an alternating minimization approach that decomposes the two estimation problems. The presented performance evaluation results validate that the proposed SC technique exhibits superior performance than the conventional one as well as than state-of-the-art multi-carrier approaches

Role of Reconfigurable Intelligent Surfaces in 6G Radio Localization: Recent Developments, Opportunities, Challenges, and Applications

Reconfigurable intelligent surfaces (RISs) are seen as a key enabler low-cost and energy-efficient technology for 6G radio communication and localization. In this paper, we aim to provide a comprehensive overview of the current research progress on the RIS technology in radio localization for 6G. Particularly, we discuss the RIS-assisted radio localization taxonomy and review the studies of RIS-assisted radio localization for different network scenarios, bands of transmission, deployment environments, as well as near-field operations. Based on this review, we highlight the future research directions, associated technical challenges, real-world applications, and limitations of RIS-assisted radio localization

Cross-Field Channel Estimation for Ultra Massive-MIMO THz Systems

The large bandwidth combined with ultra-massive multiple-input multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve terabits-per-second throughput. The THz systems are expected to operate in the near, intermediate, as well as the far-field. As such, channel estimation strategies suitable for the near, intermediate, or far-field have been introduced in the literature. In this work, we propose a cross-field, i.e., able to operate in near, intermediate, and far-field, compressive channel estimation strategy. For an array-of-subarrays (AoSA) architecture, the proposed method compares the received signals across the arrays to determine whether a near, intermediate, or far-field channel estimation approach will be appropriate. Subsequently, compressed estimation is performed in which the proximity of multiple subarrays (SAs) at the transmitter and receiver is exploited to reduce computational complexity and increase estimation accuracy. Numerical results show that the proposed method can enhance channel estimation accuracy and complexity at all distances of interest.Comment: 30 pages, 7 pages, journa