As communications evolve into the terahertz (THz) band, the near-field region expands accordingly, providing additional distance-domain information that can be exploited for high-accuracy sensing. While the ultra-short THz wavelength offers high resolution, it in turn narrows the unambiguous estimation range and demands large-scale arrays for path loss compensation. Sparse arrays (SAs) are considered to reduce the cost of massive elements, but inevitably cause performance degradation. Moreover, near-field channels exhibit complex coupling among time, frequency, and angular parameters, complicating sensing information extraction. To address these challenges, we propose a coarse-to-fine velocity estimation algorithm, enabled by a tensor-based near-field channel decomposition, while balancing complexity through SAs. Simulation results show that the proposed scheme achieves near-CRB performance with an extended unambiguous speed range. Furthermore, analysis reveals that SAs significantly reduce the computational complexity, and the resulting performance loss can be effectively mitigated by moderately increasing the bandwidth or signal duration, enabling high-accuracy, low-complexity velocity estimation.

The ultra-wide Terahertz (THz) band with jointly high-speed transmission and precise sensing has come into vision to realize integrated sensing and communication (ISAC) for emerging immersive applications. However, THz networks face a coverage bottleneck. Narrow beams are exploited to compensate for the limited signal power and path loss. But they bring in beam misalignment that degrades link connectivity and affects the THz network coverage, characterized by coverage probability. ISAC-THz networks can benefit from the sensing-aided beam alignment to improve the coverage probability. But there exists a trade-off between sensing assistance and its cost, that requires efficient resource allocation. This paper provides time-frequency resource allocation for sensing signal mapping schemes that maximize the coverage probability of the ISAC-THz networks with reduced sensing costs. Results show the effectiveness of the scheme in reducing the sensing cost with near-ideal coverage. We reveal design insights into the sensing signal insertion and preferable THz transmission band selection that achieves the desired coverage with the least sensing overhead. Wider coverage requires more sensing resources, which are more allocated to bandwidth for accurate long-range sensing. The high angular resolution of narrow beams helps reduce the sensing cost, sparing resources in the time domain for velocity estimation.

Array-of-subarrays (AoSA) is an appealing architecture in terahertz (THz) communications since the analog beamformers on sub arrays can provide beam gain to combat severe propagation loss, by low-cost phase shifters. However, the traditional beamforming scheme for AoSA, i.e., each subarray serves an exclusive user, cannot cope with the effect of beam split in THz wideband communications. In this paper, we propose a novel concept, i.e., beam split aggregation and multiplexing (BSAM), to support wideband THz communication with AoSA architecture. Specifically, we first characterize the direction of beam split and then derive the maximum bandwidth of a subband that will not cause beam split. Finally, based on the above results, we propose a criterion to plan the subbands and design the analog beamformers for BSAM.

Terahertz (THz) communication is capable of providing ultra-wide bandwidth and high data rates. Therefore attracts widespread attention to its applications in next-generation networks. Highly directional antennas are used to compensate for the THz propagation loss, which also incurs beam management challenges. Specifically, caused by node mobility and blockage, frequent beam reselections and beam misalignment greatly degrade THz network performance in terms of reliability and spatial throughput. In this paper, using stochastic geometry, we fill the current research gap in system-level theoretical models for the analysis of beam misalignment and network spatial throughput by considering the effects of beamwidth, mobility, blockage, and molecular absorption. Our analyses show that an increase in nodes density or user mobility often results in severe beam misalignment, which in turn requires more signaling overhead and degrades THz network reliability and throughput. Although using wider beams reduces this impact, it increases THz network sensitivity to molecular absorption. To maximize spatial throughput, optimal beamwidth needs to be adjusted according to communication demand priority and network status. Our work provides useful insights into beamwidth adaptation according to parameters trade-off that helps THz network achieve higher reliability and throughput in different applications.

Intelligent reflecting surface (IRS) is envisioned as a promising technology to broaden signal coverage and enhance transmission in terahertz (THz) communications. Due to the passivity of IRS, the channel measurement can not be achieved by traditional pilot manner and the subsequent cooperative transmission design remains an open problem. This paper investigates the channel estimation and transmission solutions for massive multiple input multiple output (MIMO) IRS-assisted THz system. The channel estimation is realized by beam training and the quantization error is analyzed for evaluating performance. In addition, a novel hierarchical search codebook design is proposed as a low-complexity basis of beam training. Based on above foundations, we propose a cooperative channel estimation procedure to tactfully acquire the channel knowledge. Finally, by leveraging obtained channel information, the designs of IRS and transceivers are directly provided in closed form without reconstructing the full channel matrix or additional optimization. Simulation and numerical results are presented to illustrate the minimum signal to noise ratio (SNR) required for beam training and the efficacy of the proposed transmission solutions.

This paper exploits the potential of millimeter wave (mmWave) system, where large-scale antenna arrays are allowed to implement in small physical dimension. We investigate a novel hybrid beamforming design for joint data and artificial noise (AN) precoding and power fraction selection in massive multi-input multi-output (MIMO) system. We aim at the secrecy rate maximization problem with respect to hybrid precoders design. The challenge of this problem lies in its non-convexity. To address this issue, we decouple the design for analog and digital precoders. We conduct analog precoder to maximize corresponding channel gain. For digital data precoder design, we first remove the non-convex codebook constraint and propose an iterative algorithm for optimal equivalent digital precoder design. Then, reconsidering the constraint, we conduct the digital data precoder to approach to the optimal design. Next, aiming to maximize AN power aligned at the eavesdropper, AN precoder design is optimally derived in closed form. Finally, we get power fraction by one-dimensional (1-D) search. Simulation results indicate that our proposed AN- aided hybrid precoding scheme achieves better secrecy performance compared with existing hybrid precoding schemes.

This paper investigates the optimal secure beam steering design of millimeter wave (mmWave) communications, where an Alice-Bob pair wishes to communicate in secret in the presence of Eve, with each node equipped with large-scale antenna arrays. Owing to the reduced peak-to-average power ratio and hardware cost, beam steering design emerges as an attractive technique in mmWave communications recently. However, from the physical layer perspective, the beam steering design subject to security requirement has not been investigated yet. In this paper, we consider a secrecy rate maximization problem with respect to beam steering design, i.e., analog beam selection of radio frequency (RF) chains and power allocation over the selected RF chains, which turns out to be an intractable mixed integer nonlinear optimization problem. To tackle it, we first determine a set of optimal analog beam candidates, based on which the considered multi-input multi-output (MIMO) wiretap channel is decoupled into a sequence of parallel single-input single-output (SISO) wiretap channels. Then, it is shown that the optimal power allocation over the parallel wiretap channels can be derived in a semi-closed-form. Numerical results illustrate that the proposed design offer better secrecy performance than traditional beam steering design in the presence of wiretapping as long as the channel has more than two propagation paths.