arXiv subjects

Wenrong Chen

Publications and source records attributed to Wenrong Chen.

3 recordsLinked to original sources

arXiv2024

ISAC-Enabled Beam Alignment for Terahertz Networks: Scheme Design and Coverage Analysis

As a key pillar technology for the future 6G networks, Terahertz (THz) communications can provide high-capacity transmissions, but suffers from severe propagation loss and line-of-sight (LoS) blockage that limits the network coverage. Narrow beams are required to compensate for the loss, but they in turn bring in beam misalignment challenge and degrade the THz network coverage. The high sensing resolution of THz signals enables integrated sensing and communications (ISAC) technology to assist the LoS blockage and user mobility-induced beam misalignment, enhancing THz network coverage. Based on the 5G beam management, we propose a joint synchronization signal block (SSB) and reference signal (RS)-based sensing (JSRS) scheme to assist beam alignment. JSRS enables a predict-and-prevent procedure that provides early interventions for timely beam switches. To maximize performance of JSRS, we provide an optimal sensing signal insertion and time-to-frequency allocation to improve the joint range and velocity resolutions. We derive the coverage probability of the JSRS-enabled network to evaluate its abilities in beam misalignment reduction and coverage enhancement. The expression also instructs the network density deployment and beamwidth selection. Numerical results show that the JSRS scheme is effective and highly compatible with the 5G air interface. Averaged in the tested urban use cases, JSRS achieves near-ideal performance and reduces around 80% of beam misalignment, and enhances the coverage probability by about 75%, compared to the network with 5G-required positioning ability.

cs.IT↗

arXiv2021

Terahertz Multi-User Massive MIMO with Intelligent Reflecting Surface: Beam Training and Hybrid Beamforming

Terahertz (THz) communications open a new frontier for the wireless network thanks to their dramatically wider available bandwidth compared to the current micro-wave and forthcoming millimeter-wave communications. However, due to the short length of THz waves, they also suffer from severe path attenuation and poor diffraction. To compensate the THz-induced propagation loss, this paper proposes to combine two promising techniques, viz., massive multiple input multiple output (MIMO) and intelligent reflecting surface (IRS), in THz multi-user communications, considering their significant beamforming and aperture gains. Nonetheless, channel estimation and low-cost beamforming turn out to be two main obstacles to realizing this combination, due to the passivity of IRS for sending/receiving pilot signals and the large-scale use of expensive RF chains in massive MIMO. In view of these limitations, this paper first develops a cooperative beam training scheme to facilitate the channel estimation with IRS. In particular, we design two different hierarchical codebooks for the proposed training procedure, which are able to balance between the robustness against noise and searching complexity. Based on the training results, we further propose two cost-efficient hybrid beamforming (HB) designs for both single-user and multi-user scenarios, respectively. Simulation results demonstrate that the proposed joint beam training and HB scheme is able to achieve close performance to the optimal fully digital beamforming (FDB) which is implemented even under perfect channel state information (CSI).

cs.IT↗

arXiv2020

Channel Estimation and Transmission for Intelligent Reflecting Surface Assisted THz Communications

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.

cs.IT↗