results: 研究结果显示，通过使用机器学习技术可以提高时射频测量精度，并且在不同的室内环境中，时射频和指纹识别方法的选择会受到不同的室内环境和参考用户的分布和密度的影响。Abstract
High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also the density and distribution of reference user locations used for fingerprinting.
摘要
高精度定位已经在多个领域中受到广泛关注，如工业互联网 OF Things（IIoT）、医疗和娱乐等。无线电频（RF）测量广泛应用于用户地理位置。然而，非直线视线（NLOS）和多Path传播可能会降低定位精度。机器学习（ML）基于的估计器已经提议用于解决这些挑战。RF测量可以用于定位的多种方法，包括时间基本、角度基本和指纹识别方法。不同的方法对系统实施的要求不同，可能在给定的设置下有不同的准确性表现。在本文中，我们使用人工神经网络（ANNs）来实现时间基于的到来时间（ToA）和频率响应（CIR）指纹识别基本定位。我们对不同的室内环境进行比较，根据实际的 ultra-wideband（UWB）测量数据。我们首先表明，使用ML技术可以提高定位精度，比 convent ional技术更高。当比较时间基本和指纹 schemes使用 ANNs 时，我们表明，在不同的环境中，最佳的方法是不同的，其精度受到不仅电波传播条件，还受到参考用户位置的密度和分布的影响。
Distributed Optimization with Feasible Set Privacy
results: 我们证明，对于所有固定的映射函数f，我们的方案比使用SPIR基于的私人集合交会（PSI）协议来私下取得P_1∩P_2，并找到最佳解，具有更低的信息泄露量和下载成本。对于所有可能的固定映射函数f，我们的方案在高probability下表现更好。Abstract
We consider the setup of a constrained optimization problem with two agents $E_1$ and $E_2$ who jointly wish to learn the optimal solution set while keeping their feasible sets $\mathcal{P}_1$ and $\mathcal{P}_2$ private from each other. The objective function $f$ is globally known and each feasible set is a collection of points from a global alphabet. We adopt a sequential symmetric private information retrieval (SPIR) framework where one of the agents (say $E_1$) privately checks in $\mathcal{P}_2$, the presence of candidate solutions of the problem constrained to $\mathcal{P}_1$ only, while learning no further information on $\mathcal{P}_2$ than the solution alone. Further, we extract an information theoretically private threshold PSI (ThPSI) protocol from our scheme and characterize its download cost. We show that, compared to privately acquiring the feasible set $\mathcal{P}_1\cap \mathcal{P}_2$ using an SPIR-based private set intersection (PSI) protocol, and finding the optimum, our scheme is better as it incurs less information leakage and less download cost than the former. Over all possible uniform mappings of $f$ to a fixed range of values, our scheme outperforms the former with a high probability.
摘要
我们考虑一个受限制最佳化问题，其中两个代理人($E_1$和$E_2$)共同学习最佳解决方案，并将它们的可行集$\mathcal{P}_1$和$\mathcal{P}_2$保持对方不知。目标函数$f$是全球知道的，每个可行集是全球字母集中的一个集合点。我们采用一个对称的私人信息抽取（SPIR）框架，其中一个代理人($E_1$)在$\mathcal{P}_2$中私下检查候选解的存在，不会学习更多关于$\mathcal{P}_2$的资讯。另外，我们从我们的方案中提取了一个信息理论上隐私的阈值PSI（ThPSI）协议，并characterize its download cost。我们表明，相比 privately acquiring $\mathcal{P}_1\cap \mathcal{P}_2$ 使用 SPIR-based private set intersection（PSI）协议，并查找最佳解，我们的方案更好，因为它对信息泄露和下载成本具有较低的影响。对所有可能的均匀映射$f$到固定的值范围中，我们的方案在高概率下超过前者。
Joint State and Input Estimation for Linear Dynamical Systems with Sparse Control
results: 作者通过使用不同的假设来推估输入稀疏，并且对控制输入的公共支持进行扩展。他们的算法在精度和时间复杂度方面与现有方法相比，具有明显的优势，特别是在低维度测量 régime 下。Abstract
Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and {Bayesian learning-based estimation}. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.
摘要
Linear dynamics systems with control input sparsity constraints arise in many practical applications, such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms cannot accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and Bayesian learning-based estimation. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.Here's the translation in Traditional Chinese:Linear dynamics systems with control input sparsity constraints arise in many practical applications, such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms cannot accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and Bayesian learning-based estimation. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.
Fixed-point methods for long-term power control and beamforming design in large-scale MIMO
paper_authors: Lorenzo Miretti, Renato L. G. Cavalcante, Sławomir Stańczak
for: 解决现有大规模MIMO系统中的开放 JOINT 电力控制和扩散设计问题
methods: 使用固定点方法解决问题
results: 提出了一种基于通道统计的长期电力控制和扩散设计方法，可以 Mitigate 竞争性短期优化算法所受的严重扩展性问题，并且通过对数字 simulations 进行比较，证明了该方法的优化性。Abstract
This study presents novel applications of fixed-point methods to solve previously open joint power control and beamforming design problems in modern large-scale MIMO systems, e.g., based on the cell-free massive MIMO and XL-MIMO concepts. In particular, motivated by the need for scalable system architectures, we revisit the classical sum power minimization and max-min fair design criteria by considering long-term power control and beamforming design based on channel statistics and possibly limited channel state information (CSI) sharing across distributed processing units. This approach is believed to mitigate the severe scalability issues of competing short-term optimal algorithms in the literature, which must be executed for every channel realization by a central controller endowed with global CSI, hence imposing very demanding requirements in terms of computation and interconnection capabilities. The obtained optimal algorithms are then illustrated and compared against existing short-term and long-term approaches via numerical simulations in a cell-free massive MIMO setup.
摘要
Here is the translation in Simplified Chinese:这个研究提出了fixed-point方法的新应用，用于解决现有的大规模MIMO系统中的 JOINT 功率控制和扫描设计问题。这个研究是基于现有的矩阵MIMO系统，如绝缘巨积MIMO和XL-MIMO概念。特别是，由于需要可扩展的系统架构，我们重新考虑了 classical sum 功率最小化和max-min 公平设计 criterion，通过考虑长期功率控制和扫描设计，基于通道统计信息，并可能有限的通道状态信息（CSI）共享 Across distributed 处理单元。这种方法被认为可以减轻竞争性短期优化算法在文献中的严重扩展性问题，这些算法必须在每个通道实现时执行，由一个中央控制器携带全局CSI，因此对计算和连接能力假设了非常高的要求。研究所得到的优化算法然后与现有的短期和长期方法进行比较，通过数值仿真在绝缘巨积MIMO设置中进行 illustrate。
Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels
results: 论文的数据表明，通过采用合适的天线间隔和天线配置，可以实现极高的数据传输速率，达到1 Tbps以上，并且一个大的基站可以服务一个实际上izable的移动设备。Abstract
Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, therank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.
摘要
未来无线网络必须提供越来越高的数据速率。可用频带增加约线性地，但覆盖范围减小很多。这篇论文探讨了 whether we can achieve massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels instead. In line-of-sight (LOS) scenarios, the rank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparse antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Therefore, MIMO technology contributes more to capacity growth than bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.
Kirchhoff Meets Johnson: In Pursuit of Unconditionally Secure Communication
results: 论文提出了一种基于噪声的安全键交换方案，并证明了其可以实现无条件安全的通信系统。Abstract
Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.
摘要
噪声：一个需要处理和限制通信系统性能的敌人。但是，如果在那里有金属吗？在传统工程中，我们主要专注排除、抑制、战斗或忽略噪声和其不良影响。然而，我们可以从生物学中借运用噪声相似的传输信号来传递信息？在这个意义上，噪声的使用或类似信号的利用在未来可能实现无条件安全通信系统。在这个教程文章中，我们从噪声基础的发展开始，探讨了一个实现绝对安全网络的重要应用：基于噪声的 Kirchhoff-法vere-Johnson 安全金钥交换方案（KLJN）。我们然后详细介绍了安全通信中的挑战，并说明了对物理基础的金钥分配方案在寻求无条件安全方面的重要性。同时，我们将提供一个简洁的量子钥分配（QKD）方案的概观，并对其与 KLJN 方案进行比较。最后，我们将通信 Loop 的传输扩展到无线通信，评估噪声信号在无线通信系统中的潜在应用。
Augmenting Channel Charting with Classical Wireless Source Localization Techniques
paper_authors: Florian Euchner, Phillip Stephan, Stephan ten Brink
for: Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information, with the goal of improving localization performance.
methods: The paper compares classical source localization techniques to Channel Charting with respect to localization performance, and suggests and evaluates methods to enhance Channel Charting with model-based localization approaches.
results: The paper demonstrates that Channel Charting can outperform classical localization methods on the considered dataset, and suggests incorporating information from model-based approaches during the training of the forward charting function for improved performance.Abstract
Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting channel charts usually accurately represent local neighborhood relationships, even under conditions with strong multipath propagation, they often fall short in capturing global geometric features. On the other hand, classical model-based localization methods, such as triangulation and multilateration, can easily localize signal sources in the global coordinate frame. However, these methods rely heavily on the assumption of line-of-sight channels and distributed antenna deployments. Based on measured data, we compare classical source localization techniques to channel charts with respect to localization performance. We suggest and evaluate methods to enhance Channel Charting with model-based localization approaches: One approach involves using information derived from classical localization methods to map channel chart locations to physical positions after conventional training of the forward charting function. Foremost, though, we suggest to incorporate information from model-based approaches during the training of the forward charting function in what we call "augmented Channel Charting". We demonstrate that Channel Charting can outperform classical localization methods on the considered dataset.
摘要
Translated into Simplified Chinese:通道图表（Channel Charting）的目标是通过利用通道状态信息中的相似关系来构建广播环境的地图。尽管结果的通道图表通常准确地表示当地的 neighourhood 关系，但它们经常无法捕捉全局的几何特征。相反，经典的模型基地的地方化方法，如三角形和多角形，可以轻松地在全球坐标系中确定信号源。但这些方法具有分布antenna部署的假设，而且它们依赖于直线通道的假设。基于测量数据，我们比较了经典的源localization技术和通道图表的localization性能。我们建议并评估了在Channel Charting中使用模型基地的方法来增强localization性能：一种方法是使用经典的localization方法中的信息来将通道图表的位置映射到物理位置。然而，我们更主要地建议在Channel Charting的训练过程中使用模型基地的方法，我们称之为“增强通道图表”。我们示出了Channel Charting可以在考虑的数据集上超越经典的localization方法。
Intelligent Reflecting Surface-Aided Electromagnetic Stealth Against Radar Detection
paper_authors: Beixiong Zheng, Xue Xiong, Jie Tang, Rui Zhang
For: 提出一种基于智能反射表面（IRS）的电磁隐身系统，以实现质量可控的隐身，并且可以在多种频率和方向范围内进行自适应调整。* Methods: 使用IRS的可调式反射元件实现电磁隐身，并且通过最小化所有敌方雷达接收信号总功率来优化IRS的反射。* Results: 通过 simulations, validate the performance advantages of the proposed IRS-aided electromagnetic stealth system with the proposed IRS reflection designs, including improved stealth performance and low computational complexity.Abstract
While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angle/direction range, making it challenging to ensure the expected stealth performance. In view of this, we propose in this paper a new intelligent reflecting surface (IRS)-aided electromagnetic stealth system mounted on targets to evade radar detection, by utilizing the tunable passive reflecting elements of IRS to achieve flexible and adaptive electromagnetic stealth in a cost-effective manner. Specifically, we optimize the IRS's reflection at the target to minimize the sum received signal power of all adversary radars. We first address the IRS's reflection optimization problem using the Lagrange multiplier method and derive a semi-closed-form optimal solution for the single-radar setup, which is then generalized to the multi-radar case. To meet real-time processing requirements, we further propose low-complexity closed-form solutions based on the reverse alignment/cancellation and minimum mean-square error (MMSE) criteria for the single-radar and multi-radar cases, respectively. Additionally, we propose practical low-complexity estimation schemes at the target to acquire angle-of-arrival (AoA) and/or path gain information via a small number of receive sensing devices. Simulation results validate the performance advantages of our proposed IRS-aided electromagnetic stealth system with the proposed IRS reflection designs.
摘要
传统电磁隐身材料/meta表面可以让目标在一定程度上 render 为无形visible，但它们缺乏 flexibility 和适应性，仅能在有限的频率和方向范围内运作，从而增加隐身性的挑战。为了解决这问题，我们在这篇文章中提出了一个新的智能反射表面（IRS）-支持的电磁隐身系统，通过利用 IRS 的可调的静电反射元件来实现可靠且适应的电磁隐身，在成本效益之间实现。具体来说，我们将 IRS 的反射问题优化为最小化所有敌人激光器的总接收信号功率。我们首先使用拉格朗日矩法来解决这问题，并 derive 一个半关注解的最佳解决方案 для单激光设置。然后，我们将这个解决方案扩展到多激光场景中。为了遵循实时处理需求，我们还提出了一些低复杂性的关联/抵销和最小平均方差（MMSE）的解决方案，分别适用于单激光和多激光场景。此外，我们还提出了实际的低复杂性估计方案，以确定目标上的射线来源信息。 simulation results 验证了我们提出的 IRS-aided 电磁隐身系统的性能优势。
Consensus-Based Distributed Nonlinear Filtering with Kernel Mean Embedding
for: 该文章提出了一种基于共识的分布式非线性筛选器，用于近似分布式非线性动力系统 posterior density 的扩展。
methods: 该筛选器使用了 kernel mean embedding (KME) 将系统状态嵌入到更高维度的 reproduce kernel Hilbert space (RKHS) 中，然后将非线性测量函数转换为线性形式。通过这样，提出了一个 KME 的 posterior distribution 更新规则。
results: 对比中心化筛选器，分布式筛选器可以保持分布式模式，同时具有中心化筛选器的准确性。两个示例演示了在目标跟踪场景中，包括几乎不变速度的目标和转弯目标，分布式筛选器可以准确地估计目标位置。Abstract
This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the system state is embedded into a higher-dimensional reproducing kernel Hilbert space (RKHS), and then the nonlinear measurement function is linearly converted. As a result, an update rule of KME of posterior distribution is established in the RKHS. To show the proposed distributed filter being capable of achieving the centralized estimation accuracy, a centralized filter, serving as an extension of the standard Kalman filter in the state space to the RKHS, is developed first. Benefited from the KME, the proposed distributed filter converges to the centralized one while maintaining the distributed pattern. Two examples are introduced to demonstrate the effectiveness of the developed filters in target tracking scenarios including nearly constantly moving target and turning target, respectively, with bearing-only, range and bearing measurements.
摘要
Translated into Simplified Chinese:这篇论文提出了一种基于协议的分布式非线性滤波器，使用核mean embedding（KME）来填充分布式非线性动态系统 posterior density 的 gap。为了 aproximate posterior density，系统状态被嵌入到一个更高维度的 reproduce kernel Hilbert space（RKHS）中，然后非线性测量函数被线性转换。这使得分布式滤波器中的KME posterior distribution的更新规则得到了确定。为了证明提出的分布式滤波器可以达到中央化估计精度，首先开发了基于state space的中央化滤波器，并将其扩展到RKHS中。由于KME，提出的分布式滤波器可以与中央化滤波器相同，同时保持分布式模式。两个例子被引入，用于演示在目标跟踪场景中，包括近 konstant moving target 和 turning target，分别使用 bearing-only、range和 bearing 测量。
Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI
paper_authors: Donia Ben Amor, Michael Joham, Wolfgang Utschick
for: 这篇论文的目的是Derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode.
results: 该论文通过数值研究表明，提议的方法在具有有限通道知识的场景下（即具有很少的射频）是有效的。此外，提出了一种更高效的SIWMMSE方法，其中 precoder 更新是固定的。Abstract
In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode. Assuming linear minimum mean square error (LMMSE) channel estimation is used, we establish a connection of the derived lower bound on the signal-to-interference-noise-ratio (SINR) to an average MSE that allows to reformulate the SR maximization problem as the minimization of the augmented weighted average MSE (AWAMSE). We propose an iterative precoder design with three alternating steps, all given in closed form, drastically reducing the computation time. We show numerically the effectiveness of the proposed approach in challenging scenarios with limited channel knowledge, i.e., we consider scenarios with a very limited number of pilots. We additionally propose a more efficient version of the well-known stochastic iterative WMMSE (SIWMMSE) approach, where the precoder update is given in closed form.
摘要
在这个工作中，我们 derivates a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode. Assuming linear minimum mean square error (LMMSE) channel estimation is used, we establish a connection of the derived lower bound on the signal-to-interference-noise-ratio (SINR) to an average mean squared error (MSE) that allows to reformulate the SR maximization problem as the minimization of the augmented weighted average MSE (AWAMSE). We propose an iterative precoder design with three alternating steps, all given in closed form, drastically reducing the computation time. We show numerically the effectiveness of the proposed approach in challenging scenarios with limited channel knowledge, i.e., we consider scenarios with a very limited number of pilots. We additionally propose a more efficient version of the well-known stochastic iterative WMMSE (SIWMMSE) approach, where the precoder update is given in closed form.Note: Please note that the translation is in Simplified Chinese, which is one of the two standardized Chinese languages. If you prefer Traditional Chinese, please let me know.
A Mapping of Triangular Block Interleavers to DRAM for Optical Satellite Communication
paper_authors: Lukas Steiner, Timo Lehnigk-Emden, Markus Fehrenz, Norbert Wehn
for: 优化低地球轨道卫星通信系统中的光学下降链的可靠性，通过交错处理实现可靠的数据传输。
methods: 使用三角形块交错器，并对象是使用JEDEC标准的DRAM设备。
results: 提出了一种新的mapping方法，可以在所有测试配置中实现过90%的带宽利用率，并且可以应用于任何JEDEC标准的DRAM设备。Abstract
Communication in optical downlinks of low earth orbit (LEO) satellites requires interleaving to enable reliable data transmission. These interleavers are orders of magnitude larger than conventional interleavers utilized for example in wireless communication. Hence, the capacity of on-chip memories (SRAMs) is insufficient to store all symbols and external memories (DRAMs) must be used. Due to the overall requirement for very high data rates beyond 100 Gbit/s, DRAM bandwidth then quickly becomes a critical bottleneck of the communication system. In this paper, we investigate triangular block interleavers for the aforementioned application and show that the standard mapping of symbols used for SRAMs results in low bandwidth utilization for DRAMs, in some cases below 50 %. As a solution, we present a novel mapping approach that combines different optimizations and achieves over 90 % bandwidth utilization in all tested configurations. Further, the mapping can be applied to any JEDEC-compliant DRAM device.
摘要
通信在低地球轨道卫星（LEO）下的光学下降链需要扩展，以确保可靠的数据传输。这些扩展器比普通的无线通信中使用的扩展器大得多。因此，在器件中的内存（SRAM）的容量不足以存储所有符号，需要使用外部 memories（DRAM）。由于需要非常高的数据速率，超过100 Gbit/s，DRAM带宽很快就成为通信系统的瓶颈。在这篇论文中，我们调查了三角形块扩展器，并发现了标准符号映射使用的SRAM中的低带宽利用率，在一些情况下低于50%。作为解决方案，我们提出了一种新的映射方法，该方法结合了不同的优化，在所有测试配置中都可以达到超过90%的带宽利用率。此外，该映射方法可以应用于任何JEDEC标准符合的DRAM设备。
results: 论文的数据显示， despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate.Abstract
Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both. To scale down the complexity and power consumption when operating across massive frequency and antenna dimensions, a sacrifice in the resolution of the digital-to-analog/analog-to-digital converters (DACs/ADCs) will be inevitable. In this paper, we analyze the extreme scenario where both the transmit- and receive-side are equipped with fully digital massive MIMO arrays and 1-bit DACs/ADCs, which leads to a system with minimum radio-frequency complexity, cost, and power consumption. Building upon the Bussgang decomposition, we derive a tractable approximation of the mean squared error (MSE) between the transmitted data symbols and their soft estimates. Numerical results show that, despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate.
摘要
启用(互访-)THz频段的通信将需要巨量的多输入多输出(MIMO)数组，可以是发送或接收方的一侧，或者是两个侧。为了降低复杂性和功耗占用，在巨大频率和天线维度上运行时，需要牺牲数字到Analog/Analog到Digital（DACs/ADCs）的解析精度。在这篇论文中，我们分析了极端情况下，发送和接收两侧都使用完全数字化巨大MIMO数组和1比特DACs/ADCs，这导致了最低的无线电频率复杂性、成本和功耗占用。基于Bussgang分解，我们 derivates一个可追加的MSE（均方差）近似方法，用于评估发送数据符号和其软估值之间的差异。数值结果表明，尽管其简单，一个两жды1比特量化的巨大MIMO系统可以在MSE和符号错误率方面提供很出色的表现。
Learning Channel Capacity with Neural Mutual Information Estimator Based on Message Importance Measure
methods: 我们提出了一种合作框架， simultaneous estimation of channel capacity and design of optimal codebook。首先，我们将MIM-based GAN，一种基于消息重要度度量的生成对抗网络（GAN），应用于共识度估计，并开发了一种新的方法，称为MIM-based mutual information estimator（MMIE）。然后，我们设计了一个通用合作框架，在该框架中，一个生成器被视为通道输入生成器，而一个判定器则是共识度估计器，通过对生成器进行对抗训练，生成器自动学习了最佳代码库，而判定器则估计了通道容量。
results: 数值实验表明，Compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.Abstract
Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel form of generative adversarial network (GAN) using message importance measure (MIM) as the information distance, into mutual information estimation, and develop a novel method, named MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through the adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity. Numerical experiments will demonstrate that compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.
摘要
First, we will use MIM-based GAN, a novel form of generative adversarial network (GAN) that uses message importance measure (MIM) as the information distance, to estimate mutual information. We will develop a novel method called MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity.Numerical experiments will demonstrate that compared to several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.