paper_authors: Zhongze Zhang, Tao Jiang, Wei Yu for: This paper proposes a solution to the uplink localization problem for remote users with the aid of reconfigurable intelligent surfaces (RIS).methods: The proposed method uses long short-term memory (LSTM) networks to exploit temporal correlation between measurements and construct scalable information vectors. A deep neural network (DNN) is used to map the LSTM cell state to the RIS configuration and the final DNN is used to map the LSTM cell state to the estimated user equipment (UE) position.results: The proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.Abstract
This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already received so far in order to produce an accurate estimate of the user location at the end. This is a challenging active sensing problem for which finding an optimal solution involves a search through a complicated functional space whose dimension increases with the number of measurements. In this paper, we show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable information vectors (called hidden state) based on the measurements. Subsequently, the state vector can be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position. Numerical result shows that the proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.
摘要
To address this problem, this paper proposes using a long short-term memory (LSTM) network to exploit the latent temporal correlation between measurements and construct scalable information vectors (called hidden state) based on the measurements. The state vector can then be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position.Numerical results show that the proposed active RIS design results in lower localization error compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.
High Dynamic Range mmWave Massive MU-MIMO with Householder Reflections
paper_authors: Victoria Palhares, Gian Marti, Oscar Castañeda, Christoph Studer
For: 该论文旨在解决当 simultanoously transmitting user equipments (UEs) with vastly different BS-side receive powers时，低分辨率数字化转换器 (ADCs) 的问题。* Methods: 该论文提出了一种新的高 Dynamics 范围 (HDR) MIMO 技术，该技术结合了适应性的analog spatial transform和数字平衡，以实现同时接收强大和弱 UE 的功能。* Results: 该论文通过使用 Householder reflections 作为空间变换， demonstarted the efficacy of HDR MIMO in a massive MU-MIMO mmWave scenario.Abstract
All-digital massive multiuser (MU) multiple-input multiple-output (MIMO) at millimeter-wave (mmWave) frequencies is a promising technology for next-generation wireless systems. Low-resolution analog-to-digital converters (ADCs) can be utilized to reduce the power consumption of all-digital basestation (BS) designs. However, simultaneously transmitting user equipments (UEs) with vastly different BS-side receive powers either drown weak UEs in quantization noise or saturate the ADCs. To address this issue, we propose high dynamic range (HDR) MIMO, a new paradigm that enables simultaneous reception of strong and weak UEs with low-resolution ADCs. HDR MIMO combines an adaptive analog spatial transform with digital equalization: The spatial transform focuses strong UEs on a subset of ADCs in order to mitigate quantization and saturation artifacts; digital equalization is then used for data detection. We demonstrate the efficacy of HDR MIMO in a massive MU-MIMO mmWave scenario that uses Householder reflections as spatial transform.
摘要
全数位大规模多用户（MU）多输入多输出（MIMO）在 millimeter 波（mmWave）频率上是未来无线系统的承让技术。低分辨率数字转换器（ADC）可以降低全数位基站（BS）设计的功耗。然而，同时发送用户设备（UE）的大大不同BS-side接收功率会使用量化杂音淹没弱UE，或者使ADC发生饱和。为解决这个问题，我们提出高动态范围（HDR）MIMO，一种新的思想，允许同时接收强UE和弱UE，使用低分辨率ADC。HDR MIMO将适应性的分析Transform与数字平衡相结合：分析Transform将强UE集中在一些ADC上，以降低量化和饱和artefacts；数字平衡后再进行数据检测。我们在大规模MU-MIMO mmWave场景中使用Householder reflections作为分析Transform，并证明HDR MIMO的有效性。
Capacity Limitation and Optimization Strategy for Flexible Point-to-Multi-Point Optical Networks
results: 该论文提出了可变PtMP光纤网络的能量负荷和容量的理论限制，并提出了最佳的剪辑率来实现最高的容量限制。基于准确的剪辑噪声模型，该论文建立了三维 lookup表来计算比特错误率、spectral efficiency和链损失。该论文还提出了一种优化策略来实现可变PtMP光纤网络的最高容量。Abstract
Point-to-multi-point (PtMP) optical networks become the main solutions for network-edge applications such as passive optical networks and radio access networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core technology to achieve low latency and approach high capacity for flexible PtMP optical networks. However, the high peak-to-average power ratio of the entropy-loading DSCM signal limits the power budget and restricts the capacity, which can be reduced effectively by clipping operation. In this paper, we derive the theoretical capacity limitation of the flexible PtMP optical networks based on the entropy-loading DSCM signal. Meanwhile, an optimal clipping ratio for the clipping operation is acquired to approach the highest capacity limitation. Based on an accurate clipping-noise model under the optimal clipping ratio, we establish a three-dimensional look-up table for bit-error ratio, spectral efficiency, and link loss. Based on the three-dimensional look-up table, an optimization strategy is proposed to acquire optimal spectral efficiencies for achieving a higher capacity of the flexible PtMP optical networks.
摘要
点对多点（PtMP）光网成为网络边缘应用的主要解决方案，如无活动光网和无线接入网。Entropy-loading数字子副载多plexing（DSCM）是实现低延迟和高容量灵活PtMP光网的核心技术。然而，高峰值平均功率比ENTROPY-loading DSCM信号限制了功率预算，这可以通过剪辑操作来降低。在这篇论文中，我们 derive了灵活PtMP光网的理论容量限制基于ENTROPY-loading DSCM信号。同时，我们获得了最佳剪辑率来接近最高容量限制。基于最佳剪辑噪声模型，我们建立了三维look-up表，其中包括比特错误率、spectral efficiency和链接产生率。基于三维look-up表，我们提出了一种优化策略，以实现灵活PtMP光网的更高容量。
Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework
results: numerical 结果表明该方法的效果，并 validate了感知和通信之间的性能负荷。Abstract
In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) system is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users a sensing target. Based on the theory of mutual-information (MI), communication MI and sensing MI rate are utilized as the performance metrics under the presence of clutters. In particular, we propose an novel MI-based WMMSE-ISAC method by developing a unique transceiver design mechanism to maximize the weighted sensing and communication sum-rate of this system. Such a maximization process is achieved by utilizing the classical method -- WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated.
摘要
在这封信中，一种权重最小平均方差 empowered integreated sensing and communication（ISAC）系统被研究。一个发射基站和一个接收无线访问点被考虑，以服务多个用户感知目标。基于互信息（MI）理论，在存在噪声的情况下，通信MI和感知MI率被用作这系统的性能指标。特别是，我们提出了一种新的 MI-based WMMSE-ISAC 方法，通过开发一种特有的天线设计机制，以最大化这个系统的权重感知和通信总速率。这个最大化过程通过使用经典方法——WMMSE，以更好地管理感知噪声和用户之间的干扰。 numerically 的结果表明我们的提出方法的有效性，并且 validate 了感知和通信之间的性能质量负担。
Can Electromagnetic Information Theory Improve Wireless Systems? A Channel Estimation Example
paper_authors: Jieao Zhu, Xiaofeng Su, Zhongzhichao Wan, Linglong Dai, Tie Jun Cui
for: 本研究旨在探讨electromagnetic information theory（EIT）如何提高无线通信系统的性能。
methods: 本文提出了一种基于EIT的渠道估计方法，将电磁知识integrated into classical MMSE渠道估计器中，并通过用 Gaussian process regression（GPR） derive the channel estimations。此外， authors还提出了EMkernel learning来调整EM kernel的参数。
results: simulation results show that EIT-based channel estimator可以超过传统的均方差MMSE算法，证明EIT在实际应用中的实用性。Abstract
Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify how EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel demonstration of the application of EIT. By integrating EM knowledge into the classical MMSE channel estimator, we observe for the first time that EIT is capable of improving the channel estimation performace. Specifically, the EM knowledge is first encoded into a spatio-temporal correlation function (STCF), which we term as the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. In addition, since the EM kernel allows parameter tuning, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that the application of EIT to the channel estimator enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.
摘要
电磁信息理论（EIT）是一个emerging的interdisciplinary subject，它将经典的Maxwell电磁学和Shannon信息理论相结合。EIT的目标是从电磁（EM）角度来描述无线系统中信息传输机制。现有的EIT研究主要关注度OF（DoF）、系统容量和电磁通道的特性。然而，这些研究并没有解释如何使用EIT提高无线通信系统的性能。为回答这个问题，在这篇论文中，我们提供了一种新的EIT应用示例。我们首先将EM知识编码成一个空间-时间协同函数（STCF），我们称之为EM核。这个EM核在渠道估计器中扮演着侧信息的角色。由于EM核是GP的形式，我们提议使用EIT基于GP回归（EIT-GPR）来 derivate渠道估计结果。此外，由于EM核允许参数调整，我们提议使用EM核学习来适应渠道观测。实验结果表明，通过应用EIT到渠道估计器，可以超越传统的均方差MMSE算法，从而证明EIT在实践中的价值。
results: 通过RIS的部署，可以提高高速列车通信系统的无线覆盖和可用性，并且可以适应不同的系统参数。Abstract
Reconfigurable intelligent surface (RIS) emerges as an efficient and promising technology for the next wireless generation networks and has attracted a lot of attention owing to the capability of extending wireless coverage by reflecting signals toward targeted receivers. In this paper, we consider a RIS-assisted high-speed train (HST) communication system to enhance wireless coverage and improve coverage probability. First, coverage performance of the downlink single-input-single-output system is investigated, and the closed-form expression of coverage probability is derived. Moreover, travel distance maximization problem is formulated to facilitate RIS discrete phase design and RIS placement optimization, which is subject to coverage probability constraint. Simulation results validate that better coverage performance and higher travel distance can be achieved with deployment of RIS. The impacts of some key system parameters including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST on system performance are investigated. In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications.
摘要
《卷积智能表面（RIS）助成高速列车（HST）通信系统的可靠性和可能性》Abstract:随着下一代无线网络技术的发展，可 Configurable intelligent surface (RIS) 已经成为一种有效和有前途的技术，可以扩展无线覆盖范围并将信号反射向目标接收器。在这篇论文中，我们考虑了一个RIS协助HST通信系统，以提高无线覆盖和提高通信可靠性。首先，我们研究了下降链单输入单出口系统的覆盖性能，并 derivatedclosed-form表达式来计算覆盖可能性。此外，我们还解决了RIS精度设计和RIS布置优化问题，该问题是基于覆盖可能性约束。实验结果表明，通过RIS部署，可以实现更好的覆盖性能和更长的旅行距离。此外，我们还 investigate了一些关键系统参数的影响，包括传输功率、信号噪声比阈值、RIS元素数量、RIS逻辑位数、基站与RIS之间的水平距离和高速列车速度。研究结果表明，RIS可以很好地提高高速列车通信可靠性，同时减少功率消耗。Introduction:With the development of the next generation wireless networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology that can extend wireless coverage and improve communication reliability. In this paper, we consider a RIS-assisted high-speed train (HST) communication system to enhance wireless coverage and improve coverage probability. First, we investigate the coverage performance of the downlink single-input-single-output system and derive a closed-form expression for the coverage probability. Moreover, we formulate a travel distance maximization problem to facilitate RIS discrete phase design and RIS placement optimization, which is subject to coverage probability constraint. Simulation results validate that better coverage performance and longer travel distance can be achieved with the deployment of RIS. Furthermore, we investigate the impacts of some key system parameters on system performance, including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST. Results show that RIS can well improve coverage probability with limited power consumption for HST communications.Main Body:1. Coverage Performance of Downlink Single-Input-Single-Output SystemWe first investigate the coverage performance of the downlink single-input-single-output system. By deriving the closed-form expression of coverage probability, we can analyze the impact of RIS on the coverage performance. The results show that RIS can significantly improve the coverage probability, especially when the distance between the base station and the user is large.2. Travel Distance Maximization ProblemTo facilitate RIS discrete phase design and RIS placement optimization, we formulate a travel distance maximization problem subject to coverage probability constraint. The problem is to find the optimal phase shifts of the RIS elements that maximize the travel distance of the HST while ensuring a certain coverage probability. We solve the problem using a numerical optimization algorithm and show that the optimized RIS phase shifts can significantly improve the travel distance of the HST.3. Impacts of System Parameters on System PerformanceWe investigate the impacts of some key system parameters on system performance, including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST. The results show that these parameters have a significant impact on the coverage performance and travel distance of the HST. In particular, we find that RIS can well improve coverage probability with limited power consumption for HST communications.Conclusion:In conclusion, we have proposed a RIS-assisted HST communication system to enhance wireless coverage and improve coverage probability. By deriving the closed-form expression of coverage probability and formulating a travel distance maximization problem, we have shown that RIS can significantly improve the coverage performance and travel distance of the HST. Furthermore, we have investigated the impacts of some key system parameters on system performance and found that RIS can well improve coverage probability with limited power consumption for HST communications. Our results demonstrate the potential of RIS technology for improving wireless communication systems in high-speed train applications.