results: 我们提出了一个随机接入协议，以确保多个用户成功传输数据，并且限制延迟和能源损失。我们考虑了两种方案：一个固定传输机会（FTP）方案，其中每个用户的传输机会（TP）在数据传输开始时被更新；另一个是自适应传输机会（ATP）方案，其中TP在每次成功接收数据时被更新。我们分析了这两种协议的性能，包括延迟、能源消耗和失败率，并且使用对数律的传输框架大小。Abstract
The current body of research on terahertz (THz) wireless communications predominantly focuses on its application for single-user backhaul/fronthaul connectivity at sub-THz frequencies. First, we develop a generalized statistical model for signal propagation at THz frequencies encompassing physical layer impairments, including random path-loss with Gamma distribution for the molecular absorption coefficient, short-term fading characterized by the $\alpha$-$\eta$-$\kappa$-$\mu$ distribution, antenna misalignment errors, and transceiver hardware impairments. Next, we propose random access protocols for a cell-free wireless network, ensuring successful transmission for multiple users with limited delay and energy loss, exploiting the combined effect of random atmospheric absorption, non-linearity of fading, hardware impairments, and antenna misalignment errors. We consider two schemes: a fixed transmission probability (FTP) scheme where the transmission probability (TP) of each user is updated at the beginning of the data transmission and an adaptive transmission probability (ATP) scheme where the TP is updated with each successful reception of the data. We analyze the performance of both protocols using delay, energy consumption, and outage probability with scaling laws for the transmission of a data frame consisting of a single packet from users at a predefined quality of service (QoS).
摘要
Current research on terahertz (THz) wireless communications mainly focuses on its application for single-user backhaul/fronthaul connectivity at sub-THz frequencies. We first develop a generalized statistical model for signal propagation at THz frequencies, taking into account physical layer impairments such as random path-loss with Gamma distribution for the molecular absorption coefficient, short-term fading characterized by the $\alpha$-$\eta$-$\kappa$-$\mu$ distribution, antenna misalignment errors, and transceiver hardware impairments. Next, we propose random access protocols for a cell-free wireless network to ensure successful transmission for multiple users with limited delay and energy loss, leveraging the combined effect of random atmospheric absorption, non-linearity of fading, hardware impairments, and antenna misalignment errors. We consider two schemes: a fixed transmission probability (FTP) scheme where the transmission probability (TP) of each user is updated at the beginning of the data transmission, and an adaptive transmission probability (ATP) scheme where the TP is updated with each successful reception of the data. We analyze the performance of both protocols using delay, energy consumption, and outage probability with scaling laws for the transmission of a data frame consisting of a single packet from users at a predefined quality of service (QoS).
Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN
results: simulations results表明，提出的pasive ISAC方案可以有效地探测环境中的反射体信息，不会影响通信性能。Abstract
This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
摘要
Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison
paper_authors: Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu, Tony Xiao Han, Derrick Wing Kwan Ng
for: 本研究 investigate two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing system with fully-passive和semi-passive IRSs, respectively.
methods: 研究使用了一个基站（BS）、一个 uniform linear array（ULA）IRS和一个点target in the NLoS region of the BS。 Specifically, we analyze the sensing signal-to-noise ratio（SNR）performance for a target detection scenario and the estimation Cramér-Rao bound（CRB）performance for a target’s direction-of-arrival（DoA）estimation scenario.
results: 结果表明，当IRS中的反射元素数($N$) sufficiently large时，semi-passive-IRS sensing system的最大探测SNR将提高 proportional to $N^2$,而fully-passive-IRS counterpart will increase proportional to $N^4$. In addition, we found that the minimum CRB performance will decrease inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively.Abstract
This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully-passive and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized. First, for the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart. Then, for the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Specifically, when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively. Finally, numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. It is shown that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$.
摘要
For the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. Our results show that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart.For the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Our results show that when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively.Numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. Our results show that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$.
Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems
results: 计算结果表明，提出的感知帮助方法可以明显提高总可 achievable 率，比传统基于DFT稀疏基准无需感知的设计更高，这是因为它减少了训练负担并提高了重建精度，具体是通过限制反馈。Abstract
This correspondence presents a novel sensing-assisted sparse channel recovery approach for massive antenna wireless communication systems. We focus on a fundamental configuration with one massive-antenna base station (BS) and one single-antenna communication user (CU). The wireless channel exhibits sparsity and consists of multiple paths associated with scatterers detectable via radar sensing. Under this setup, the BS first sends downlink pilots to the CU and concurrently receives the echo pilot signals for sensing the surrounding scatterers. Subsequently, the CU sends feedback information on its received pilot signal to the BS. Accordingly, the BS determines the sparse basis based on the sensed scatterers and proceeds to recover the wireless channel, exploiting the feedback information based on advanced compressive sensing (CS) algorithms. Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate than the conventional design relying on a discrete Fourier transform (DFT)-based sparse basis without sensing, thanks to the reduced training overhead and enhanced recovery accuracy with limited feedback.
摘要
First, the BS sends downlink pilots to the CU and receives echo pilot signals for sensing the surrounding scatterers. Then, the CU sends feedback information on its received pilot signal to the BS. Based on the sensed scatterers, the BS determines the sparse basis and uses advanced compressive sensing (CS) algorithms to recover the wireless channel.Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate compared to the conventional design that relies on a discrete Fourier transform (DFT)-based sparse basis without sensing. This is because the reduced training overhead and enhanced recovery accuracy with limited feedback provided by sensing-assisted approach lead to better performance.