results: 对比传统检测器，我们的DNN方法在实际数据中表现出较好的robust性和高效性，尤其在高信号噪声比（SNR）下表现出 aproximately 20%的改善Here’s the translation in English:
for: To improve the reliability and efficiency of AmBC systems, especially in practical communication channels with noise.
methods: Using a deep neural network (DNN) for channel state estimation (CSI) and data detection, and combining the two for improved data detection accuracy in AmBC systems.
results: Our DNN method outperforms traditional detectors in practical data recovery, with an approximately 20% improvement in bit error rate (BER) compared to the maximum likelihood (ML) approach, especially in high signal-to-noise ratio (SNR) conditions.Abstract
The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventional data detectors face limitations due to their inadequate knowledge of channel and RF-source parameters. To address this challenge, we propose an innovative approach using a deep neural network (DNN) for channel state estimation (CSI) and signal detection within AmBC systems. Unlike traditional methods that separate CSI estimation and data detection, our approach leverages a DNN to implicitly estimate CSI and simultaneously detect data. The DNN model, trained offline using simulated data derived from channel statistics, excels in online data recovery, ensuring robust performance in practical scenarios. Comprehensive evaluations validate the superiority of our proposed DNN method over traditional detectors, particularly in terms of bit error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method exhibits an impressive approximately 20% improvement in BER performance compared to the maximum likelihood (ML) approach. These results underscore the effectiveness of our developed approach for AmBC channel estimation and signal detection. In summary, our method outperforms traditional detectors, bolstering the reliability and efficiency of AmBC systems, even in challenging channel conditions.
摘要
现代无线通信技术已经提供了无限可靠、便宜的连接，开启了无数实用应用。在这个背景下， ambient backscatter 通信（AmBC）占据了一席之地，通过利用反射的 ambient 电磁波（RF）信号，实现了通过 passive 标签与读取器进行连接。然而，传统的数据检测器受到了通道和 RF 源参数的限制。为了解决这个挑战，我们提出了一种创新的方法，使用深度神经网络（DNN）进行通道状态估计（CSI）和信号检测在 AmBC 系统中。不同于传统的方法，我们的方法不分开 CSI 估计和数据检测，而是通过 DNN 来协同估计 CSI 和数据检测。DNN 模型，在线上训练使用 simulate 数据 derived from channel statistics，在实际应用中表现出了优秀的robust性。 comprehensive evaluations 表明，我们提出的 DNN 方法在 BER 性能方面比传统的 ML 方法有约 20% 的提升，特别在高 SNR 条件下。这些结果证明了我们开发的方法在 AmBC 通道估计和信号检测方面的效果。总之，我们的方法在实际应用中表现出了更高的可靠性和效率，即使在具有挑战性的通道条件下。
Network-Level Integrated Sensing and Communication: Interference Management and BS Coordination Using Stochastic Geometry
results: 研究表明，干扰抑制可以提高平均数据率和雷达信息率。另外，在ASE最大化情况下，共同BS集群大小的选择对S&C性能具有灵活的负面影响。此外，我们证明了在优化通信性能时，理想的用户数与发射天线数的比值是一定的常数值。实验结果表明，提案的协作ISAC方案可以在网络级别上获得显著的S&C性能提升。Abstract
In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for the case of the ASE maximization tends to employ all spacial resources towards multiplexing and diversity gain, without interference nulling. By contrast, for the sensing objectives, resource allocation tends to eliminate certain interference especially when the antenna resources are sufficient, because the inter-cell interference becomes a more dominant factor affecting sensing performance. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.
摘要
在这个研究中，我们研究了集成感知和通信（ISAC）网络，以实现网络水平的感知和通信（S&C）性能的平衡。我们将注意力集中在单频感知上，使用随机几何工具来捕捉S&C性能，从而照明ISAC网络中关键的合作依赖关系，并且优化关键网络级别参数。基于 derivated的面спектル效率（ASE）表达式，我们形ulated了 maximize 网络性能的优化问题，并且做出了关于 S&C 和服务/探测用户/目标的共同优化。研究结果表明，干扰消除可以有效提高平均数据率和雷达信息率。另外，在ASE maximization情况下，最佳通信交换倾向于使用所有空间资源进行多路复用和多样度增强，而不是干扰消除。在感知目标下，资源分配倾向于消除certain干扰，特别是当antenna资源充足时，因为 между� intercept 成为感知性能的主要影响因素。此外，我们证明了在优化通信性能时，用户数和发射天线数的比率是一定的常量值。实验结果表明，我们提出的合作ISAC方案在网络级别上实现了显著的感知和通信性能提升。
Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design
results: 作者通过 simulations 验证了提议的设计，并发现了感知、通信和能源传输之间的性能协调问题。Abstract
In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.
摘要
在第六代（6G）网络中，巨量低功率设备预计将环境感知并传输巨大数据。为提高电磁资源效率，紧凑感测通信（ISAC）技术利用信号感测和通信功能，同时利用同一个信号作为信息和能量传输的同步无线信息和能量传输（SWIPT）技术。这两种技术的结合，形成了进一步的技术——集成感测通信和能量传输（ISCPT）。在本文中，我们考虑了一个多用户多输入多出力（MIMO）ISCPT系统，其中一个基站装备多个天线发送消息到多个信息收发器（IR），传输能量到多个能量收发器（ER），并同时感测目标。感测目标可以是点或扩展表面。当IR和ER的位置分开时，MIMO扩展设计用于提高感测性能，同时满足通信和能量传输要求。解决的非 convex 优化问题通过序列技术，如Schur complement transformation和矩阵减少。此外，当IR和ER均处于同一个位置时，共同优化功率分配和扩展设计以平衡通信和能量传输的性能。为了更好地理解ISCPT性能，我们进一步调查了目标位置问题。我们的提案的设计通过实验验证，并发现存在性能平衡问题。
Channel Estimation for mmWave MIMO using sub-6 GHz Out-of-Band Information
results: 比对传统使用只带内带信息的mmWave通信频道估算方法，提出三种新的通信频道估算方法，实验结果显示，提案方法在低SNR和高K因子下表现较为优秀，可以提高spectral efficiency。Abstract
Future wireless multiple-input multiple-output (MIMO) communication systems will employ sub-6 GHz and millimeter wave (mmWave) frequency bands working cooperatively. Establishing a MIMO communication link usually relies on estimating channel state information (CSI) which is difficult to acquire at mmWave frequencies due to a low signal-to-noise ratio (SNR). In this paper, we propose three novel methods to estimate mmWave MIMO channels using out-of-band information obtained from the sub-6GHz band. We compare the proposed channel estimation methods with a conventional one utilizing only in-band information. Simulation results show that the proposed methods outperform the conventional mmWave channel estimation method in terms of achievable spectral efficiency, especially at low SNR and high K-factor.
摘要
未来的无线多输入多出力（MIMO）通信系统将使用低于6GHz和毫米波（mmWave）频率band工作协作。建立MIMO通信链接通常需要估算通道状态信息（CSI），但mmWave频率band中的信号至噪比（SNR）很低，这使得频率估算变得更加困难。在这篇论文中，我们提出了三种新的mmWave通道估算方法，使用来自低于6GHz频率band的外带信息。我们与传统的尝试估算方法进行比较，结果显示，我们的提议方法在低SNR和高K因子下的可实现 spectral efficiency 方面具有显著的提高。
EMF-Aware Power Control for Massive MIMO: Cell-Free versus Cellular Networks
results: simulation结果表明，该策略可以轻松遵守EMF安全限制，同时不影响最小数据速率。此外，CF-mMIMO系统也比多单元巨量MIMO系统更高效，而且提档的能量分配策略可以提高系统公平性。Abstract
The impressive growth of wireless data networks has recently led to increased attention to the issue of electromagnetic pollution. Specific absorption rates and incident power densities have become popular indicators for measuring electromagnetic field (EMF) exposure. This paper tackles the problem of power control in user-centric cell-free massive multiple-input-multiple-output (CF-mMIMO) systems under EMF constraints. Specifically, the power allocation maximizing the minimum data rate across users is derived for both the uplink and the downlink under EMF constraints. The developed solution is also applied to a cellular mMIMO system and compared to other benchmark strategies. Simulation results prove that EMF safety restrictions can be easily met without jeopardizing the minimum data rate, that the CF-mMIMO outperforms the multi-cell massive MIMO deployment, and that the proposed power control strategy greatly improves the system fairness.
摘要
“受到无线数据网络的快速发展所带来的电磁污染问题已经引起了更多的关注。特别是对电磁场(EMF)曝露的评估指标 Specific Absorption Rate 和 Incident Power Density 的使用。本文对用户中心的无线免系大量多input多output（CF-mMIMO）系统中的能量控制进行了研究，以满足EMF的限制。具体来说，我们在下调和下调之下，对于每个用户最大化最小的数据率的能量分配。此外，我们还将此解释应用到了一个细节的Cellular mMIMO系统中，并与其他参考策略进行比较。实验结果显示：1. EMF安全限制可以轻松满足，不会对最小的数据率造成影响；2. CF-mMIMO比多组巨量MIMO部署更好，3. 我们的能量控制策略可以大大提高系统公平性。”
Throughput Maximisation in Ultra-wideband Hybrid-amplified Links
results: 论文通过结合粒子群优化算法来提高hybrid-amplified 10.5 THz 117x57 km 链路的吞吐量，比起EDFAs-only配置提高了12%。Abstract
A semi-analytical, real-time nonlinear-interference model including ASE noise in hybrid-amplified links is introduced. Combined with particle-swarm optimisation, the capacity of a hybrid-amplified 10.5 THz 117x57 km link was maximised, increasing throughput by 12% versus an EDFAs-only configuration.
摘要
“一种半分析式、实时非线性干扰模型，包括ASE噪声，在混合增强链路中引入。与particle-swarm优化结合，hybrid-amplified 10.5 THz 117x57 km 链路的容量最大化，比EDFAs-only配置提高了12%的吞吐量。”Here's a breakdown of the translation:* “半分析式”(pán fēn yì jì) - semi-analytical* “实时非线性干扰模型”(shí jì fēn xiǎn yì jì mó delè) - real-time nonlinear-interference model* “ASE噪声”(ASE nóng shēng) - ASE noise* “混合增强链路”(hù hé zēng jiāng liàng) - hybrid-amplified link* “容量”(róng kè) - capacity* “最大化”(zmài huì) - maximized* “比EDFAs-only配置”(bǐ EDFAs-only zhèng jì) - compared to an EDFAs-only configuration* “吞吐量”(tōng chuō liàng) - throughputI hope this helps! Let me know if you have any further questions or if you'd like me to translate anything else.
Design and Implementation of a Hybrid Wireless Power and Communication System for Medical Implants
results: 该论文提出了一种基于401MHz无线电波的无线嵌入式设备，通过两个同时的无线链路进行设备之间的通信，并实现了深度401MHz的无线电力供应。Abstract
Data collection and analysis from multiple implant nodes in humans can provide targeted medicine and treatment strategies that can prevent many chronic diseases. This data can be collected for a long time and processed using artificial intelligence (AI) techniques in a medical network for early detection and prevention of diseases. Additionally, machine learning (ML) algorithms can be applied for the analysis of big data for health monitoring of the population. Wireless powering, sensing, and communication are essential parts of future wireless implants that aim to achieve the aforementioned goals. In this paper, we present the technical development of a wireless implant that is powered by radio frequency (RF) at 401 MHz, with the sensor data being communicated to an on-body reader. The implant communication is based on two simultaneous wireless links: RF backscatter for implant-to-on-body communication and a galvanic link for intra-body implant-to-implant connectivity. It is demonstrated that RF powering, using the proposed compact antennas, can provide an efficient and integrable system for powering up to an 8 cm depth inside body tissues. Furthermore, the same antennas are utilized for backscatter and galvanic communication.
摘要
《数据采集和分析从多个人体节点可以提供Targeted医疗和治疗策略，预防许多慢性疾病。这些数据可以长期采集并使用人工智能（AI）技术进行处理，在医疗网络中进行早期检测和预防疾病。此外，机器学习（ML）算法可以用于分析大量数据，进行人群健康监测。在这篇论文中，我们介绍了无线嵌入式设备的技术开发，该设备由401MHzRadio frequency（RF）电磁辐射供电，感知数据通过身体上的读取器与设备进行通信。该设备通信基于两个同时的无线链路：RF扫描 для设备与身体之间的通信，以及 galvanic链接用于体内设备之间的通信。我们展示了RF供电，使用我们提出的减小型天线，可以提供高效和可集成的系统，在身体组织中进行深度401 MHz的供电。此外，同一天线也用于扫描和 galvanic 通信。
Energy-Efficient Design of Satellite-Terrestrial Computing in 6G Wireless Networks
results: 对于卫星地面计算，提出了一种能效的卫星地面计算算法，通过同时优化卫星与地面基站之间数据传输和计算任务的分配，以最小化加权总能consumption，并保证计算任务的延迟要求。实验和理论分析结果都表明，提出的算法在6G无线网络中的卫星地面计算方面具有快速吞吐和优秀性能。Abstract
In this paper, we investigate the issue of satellite-terrestrial computing in the sixth generation (6G) wireless networks, where multiple terrestrial base stations (BSs) and low earth orbit (LEO) satellites collaboratively provide edge computing services to ground user equipments (GUEs) and space user equipments (SUEs) over the world. In particular, we design a complete process of satellite-terrestrial computing in terms of communication and computing according to the characteristics of 6G wireless networks. In order to minimize the weighted total energy consumption while ensuring delay requirements of computing tasks, an energy-efficient satellite-terrestrial computing algorithm is put forward by jointly optimizing offloading selection, beamforming design and resource allocation. Finally, both theoretical analysis and simulation results confirm fast convergence and superior performance of the proposed algorithm for satellite-terrestrial computing in 6G wireless networks.
摘要
在这篇论文中，我们研究了 sixth generation（6G）无线网络中的卫星-地面计算问题，其中多个地面基站（BS）和低地球轨卫星（LEO）共同提供边缘计算服务给地面用户设备（GUE）和空间用户设备（SUE）。特别是，我们设计了6G无线网络中卫星-地面计算的完整过程，包括通信和计算方面。为了最小化加权总能 consumption，我们提出了一种能效的卫星-地面计算算法，通过共同优化卸载选择、扩散设计和资源分配来确保计算任务的延迟要求。最后，我们通过理论分析和模拟结果，证明了我们提出的算法在6G无线网络中的快速叠合和优秀表现。
RIS Position and Orientation Estimation via Multi-Carrier Transmissions and Multiple Receivers
results: simulations 结果表明，提案的 RIS 状态估计方法在不同系统操作参数下具有高效性。Abstract
Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cram\'er-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.
摘要
弹性智能表面（RIS）被视为 sixth generation无线系统的核心技术，具有很大的射频地域和感知潜力。一个RIS通常被视为一个已知位置和方向的锚点，当它被部署时。但是，它也可以附加到用户来实现半通信式的用户位置测量。在这篇论文中，我们考虑一个静止的用户，装备了RIS，并研究RIS的位置识别问题（即三维位置和方向的共同估计），在一个具有单antenna传送器和多个同步化单antenna接收器的系统中进行。我们提出了一个多阶估计器，使用时间对应和频率对应的测量，并 derivated Cramér-Rao下界来验证估计器的性能。我们的实验结果显示了该RIS状态估计方法的效率，在不同的系统运行参数下。
Aerial IRS with Robotic Anchoring Capabilities: A Novel Way for Adaptive Coverage Enhancement
for: 提高无线网络覆盖率和用户服务质量 (improving wireless network coverage and end-user Quality of Service)
methods: 使用机器人飞行智能反射表 (RA-IRSs)，具有能gie neutral manner具有钻取灯杆等高层城市地形的机制，从而完全消除飞行/停靠能 consumption和提供多小时或者多天服务 (eliminating flying/hovering energy consumption and providing multiple hours or days of service)
results: 使用RA-IRSs可以提高网络性能，通过变化锚点位置以遵循空间时间交通需求 (improving network performance by changing anchoring locations to follow spatial-temporal traffic demand)，提供了高度异ogeneous区域中的显著信噪比提升 (significant Signal-to-Noise ratio gain in highly heterogeneous regions)，并且在高异ogeneous区域中可以支持更多的流量需求 (sustaining more than 2 times the traffic demand in areas experiencing high heterogeneity)。Abstract
It is widely accepted that integrating intelligent reflecting surfaces (IRSs) with unmanned aerial vehicles (UAV) or drones can assist wireless networks in improving network coverage and end user Quality of Service (QoS). However, the critical constrain of drones is their very limited hovering/flying time. In this paper we propose the concept of robotic aerial IRSs (RA-IRSs), which are in essence drones that in addition to IRS embed an anchoring mechanism that allows them to grasp in an energy neutral manner at tall urban landforms such as lampposts. By doing so, RA-IRSs can completely eliminate the flying/hovering energy consumption and can offer service for multiple hours or even days (something not possible with UAV-mounted IRSs). Using that property we show how RA-IRS can increase network performance by changing their anchoring location to follow the spatio-temporal traffic demand. The proposed methodology, developed through Integer Linear Programming (ILP) formulations offers a significant Signal-to-Noise (SNR) gain in highly heterogeneous regions in terms of traffic demand compared to fixed IRS; hence, addressing urban coverage discrepancies effectively. Numerical simulations validate the superiority of RA-IRSs over fixed terrestrial IRSs in terms of traffic serviceability, sustaining more than 2 times the traffic demand in areas experiencing high heterogeneity, emphasizing their adaptability in improving coverage and QoS in complex urban terrains.
摘要
广泛接受到了将智能反射表（IRS）与无人机（UAV）或无人飞行器（drone）结合，以提高无线网络的覆盖范围和用户服务质量（QoS）。然而，无人机的缺点是其很有限的悬挂/飞行时间。在这篇论文中，我们提出了机器人空中智能反射表（RA-IRS）的概念，它们是具有悬挂机制的无人机，可以在能源中立Positions中静止，以减少或完全消除飞行/悬挂时间的能量消耗。通过这种方式，RA-IRS可以提供多个小时或者多天的服务（不可能由UAV-IRS所实现）。我们使用了Integer Linear Programming（ILP）方法开发了一种新的方法ологи，以便通过更改悬挂位置来跟踪空间时间具有不同强度的负载均衡问题。我们的方法可以在高度不均的区域中提供明显的信噪比（SNR）提升，从而有效地解决城市覆盖不平等问题。数值仿真 validate了RA-IRSs的superiority，可以在高度不均的区域中支持更多的流量，达到2倍以上的流量可用性，强调其适应性在复杂的城市地形中。
Phase retrieval with semi-algebraic and ReLU neural network priors
paper_authors: Tamir Bendory, Nadav Dym, Dan Edidin, Arun Suresh
for: 解决phaserecovery问题
methods: 使用semi-algebraic set prior
results: 可以从傅ри格式中获取信号的签名，并且可以确定信号的签名是唯一的，只要信号 lie在一个 semi-algebraic set中。这个result generalizes to multi-reference alignment models with multiplicity free representations of compact groups.Abstract
The key ingredient to retrieving a signal from its Fourier magnitudes, namely, to solve the phase retrieval problem, is an effective prior on the sought signal. In this paper, we study the phase retrieval problem under the prior that the signal lies in a semi-algebraic set. This is a very general prior as semi-algebraic sets include linear models, sparse models, and ReLU neural network generative models. The latter is the main motivation of this paper, due to the remarkable success of deep generative models in a variety of imaging tasks, including phase retrieval. We prove that almost all signals in R^N can be determined from their Fourier magnitudes, up to a sign, if they lie in a (generic) semi-algebraic set of dimension N/2. The same is true for all signals if the semi-algebraic set is of dimension N/4. We also generalize these results to the problem of signal recovery from the second moment in multi-reference alignment models with multiplicity free representations of compact groups. This general result is then used to derive improved sample complexity bounds for recovering band-limited functions on the sphere from their noisy copies, each acted upon by a random element of SO(3).
摘要
“对于从傅立叶展开的信号重建问题，键的因素是一个有效的先前知识。本文研究对半代数集的信号重建问题，这是非常通用的先前知识，因为半代数集包括线性模型、简单模型和ReLU神经网络生成模型。后者是这篇文章的主要动机，因为深度生成模型在各种图像任务中表现出色。我们证明，如果信号 lying in a （普通）半代数集的话，则可以从傅立叶展开中恢复信号，保留信号的符号， provided that the dimension of the semi-algebraic set is at least N/2。同样的，如果半代数集的维度是 N/4，则所有的信号都可以从傅立叶展开中恢复。我们还将这些结果推广到多对对称定理中的问题，并使用多对对称定理来 derive improved sample complexity bounds for recovering band-limited functions on the sphere from their noisy copies, each acted upon by a random element of SO(3).”Note: The translation is in Simplified Chinese, which is one of the two standard forms of Chinese writing. The other form is Traditional Chinese.
Wireless Communications in Cavity: A Reconfigurable Boundary Modulation based Approach
results: 实验结果显示，这种方法可以在原型中实现约2Mbps的比特率，并具有强 resistivity to channel的频率选择性，导致比特错误率非常低。Abstract
This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments. Unlike in free space, we utilize the sensitivity to boundaries of the enclosed electromagnetic (EM) field and the equivalent perturbation of RISs. For the first time, we introduce the framework of reconfigurable boundary modulation in the cavities . We have proposed a robust boundary modulation scheme that exploits the continuity of object motion and the mutation of the codebook switch, which achieves pulse position modulation (PPM) by RIS-generated equivalent pulses for wireless communication in cavities. This approach achieves around 2 Mbps bit rate in the prototype and demonstrates strong resistance to channel's frequency selectivity resulting in an extremely low bit error rate (BER).
摘要
（本文探讨了嵌入式智能表面（RIS）在干扰波传播环境中的无线通信应用。与在自由空间中不同，我们利用封闭电磁场边界敏感性和相同的RIS做法。我们为首次引入了可重新配置边缘调制框架在镜室中。我们提出了一种强健的边缘调制方案，利用物体运动连续性和代码库交换互变，实现了由RIS生成的等效脉冲干扰器为无线通信在镜室中实现的脉冲位调制（PPM）。这种方法在原型中实现了约2Mbps的比特率和非常低的比特错误率（BER）。）
Channel Capacity and Bounds In Mixed Gaussian-Impulsive Noise
for: This paper investigates the channel capacity of communication systems under mixed noise, which consists of both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN).
methods: The authors use mathematical proofs and numerical results to study the channel capacity under p-th moment constraint and show that there are only finite mass points in the capacity-achieving distribution.
results: The authors provide lower and upper capacity bounds with closed forms, and show that the lower bounds can degenerate to the well-known Shannon formula under special scenarios. Numerical results reveal that the capacity decreases when the impulsiveness of the mixed noise becomes dominant, and the obtained capacity bounds are shown to be very tight.Here’s the Chinese text:
results: 作者提供了下界和上界的频率容量 bound，并显示了这些下界可以在特定情况下逐渐变为著名的雪伦 формула。numerical results表明，杂噪的强度增加时，频率容量减少，并且获得的容量 bound是非常紧致的。Abstract
Communication systems suffer from the mixed noise consisting of both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many practical applications. However, there is little literature about the channel capacity under mixed noise. In this paper, we prove the existence of the capacity under p-th moment constraint and show that there are only finite mass points in the capacity-achieving distribution. Moreover, we provide lower and upper capacity bounds with closed forms. It is shown that the lower bounds can degenerate to the well-known Shannon formula under special scenarios. In addition, the capacity for specific modulations and the corresponding lower bounds are discussed. Numerical results reveal that the capacity decreases when the impulsiveness of the mixed noise becomes dominant and the obtained capacity bounds are shown to be very tight.
摘要
通信系统在实际应用中常常受到杂合噪声（IN）和白噪声（WGN）的杂合噪声的影响。然而，关于杂合噪声下的通道容量，现有文献不多。在这篇论文中，我们证明了容量下p-th moment约束的存在，并表明了 achieve 分布中具有finite mass点。此外，我们还提供了下界和上界的容量 bound，它们具有封闭形式。显示，下界可以在特定情况下逐渐变为著名的雪伦方程。此外，我们还讨论了特定模ulation的容量和相关下界。numerical result表明，杂合噪声占主导地位时，通道容量减少，而我们所获得的容量 bound 具有很高精度。
High-Resolution DOA Estimation via a Novel Tree Model-based Deep Neural Network
paper_authors: Yifan Li, Feng Shu, Yaoliang Song, Jiangzhou Wang for: 这种论文是为了提高深度神经网络（DNN）在评估偏角时的性能而写的。methods: 该论文提出了一种基于树模型的深度神经网络（TDNN），它包含多个小规模DNN的层，从第一层到最后一层，每个层都是将angular region分解成更小的子区域，并通过积加多个层的分类结果来获得最终的DOA估计结果。results: simulations results表明，TDNN在单源和多源情况下的估计性能都远胜传统方法，特别是在低信号噪听比（SNR）下。Abstract
Traditional deep neural networks (DNNs) have bad performance on estimating off-grid angles, and the most direct solution is to increase the number of output classes for improving angular resolution. But more output classes can weaken the model accuracy of DNNs and thus decreasing the direction-of-arrival (DOA) estimation accuracy. In this work, a tree-model based deep neural networks (TDNN) is proposed, which contains H layers and each layer is consist of multiple small-scale DNNs. From the first layer to the last layer of TDNN, the angular region is gradually divided into smaller subregions by these DNNs, and the estimated DOA is finally obtained by cumulative calculating the classification results of all the layers. TDNN can improve the angular resolution by increasing the number of layers or the number of DNNs in any layer instead of changing the structure of single DNN, so the model accuracy of TDNN will not decrease with the improvement of angular resolution and its estimation performance is also stable. In addition, the Q-TDNN method is also proposed for multi-sources DOA estimation, which can obtain Q different DOAs from the same signals by combining Q independent and parallel TDNNs. The simulation results validate TDNN has much better estimation performance than traditional methods in both single-source and multi-sources cases, especially at low signal-to-noise ratio (SNR).
摘要
传统的深度神经网络（DNNs）在未经授标的角度估计方面表现不佳，而直接解决方法是增加出力类型数以提高角度分辨率。但是，增加出力类型数会弱化DNNs模型的准确性，因此降低方向来源估计精度。在这种情况下，一种基于树模型的深度神经网络（TDNN）被提出，它包含H层，每层都包含多个小规模DNNs。从TDNN的第一层到最后一层，每层的angular region逐渐分解为更小的子区域，并通过累加所有层的类别结果来获得最终的DOA估计结果。TDNN可以通过增加层数或增加每层DNNs的数量来提高角度分辨率，而不是改变单个DNN的结构，因此TDNN的模型准确性不会随着角度分辨率的提高而下降。此外，Q-TDNN方法也被提出用于多源DOA估计，可以通过将Q个独立并平行的TDNN组合来获得Q个不同的DOA。实验结果表明，TDNN在单源和多源情况下都有较好的估计性能，特别是在低SNR情况下。
Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach
methods: 我们提出了一种True-Time Delayer (TTD)-包含的分析式束缚技术，以 Address the interplay between near-field propagation and wideband beamsplit。我们采用了一种两阶段的优化方法，包括半数字解决方案和分析approximation。
results: 我们的方法可以 clearly demonstrate the superiority of the proposed methods over TTD-free approaches in fortifying wideband PLS, as well as the advantageous secrecy energy efficiency achieved by leveraging low-cost analog devices。Abstract
In the rapidly advancing landscape of six-genration (6G), characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation and wideband beamsplit, an uncharted domain in existing literature. Our approach to maximizing secrecy rates involves formulating an optimization problem for joint power allocation and analog beamformer design, employing a two-stage process encompassing a semi-digital solution and analog approximation. This problem is efficiently solved through a combination of alternating optimization, fractional programming, and block successive upper-bound minimization techniques. Additionally, we present a low-complexity beamsplit-aware beamfocusing strategy, capitalizing on geometric insights from near-field wideband propagation, which can also serve as a robust initial value for the optimization-based approach. Numerical results substantiate the efficacy of the proposed methods, clearly demonstrating their superiority over TTD-free approaches in fortifying wideband PLS, as well as the advantageous secrecy energy efficiency achieved by leveraging low-cost analog devices.
摘要
在六代（6G）迅速发展的背景下，我们的论文关注Physical Layer Security（PLS）在近场宽频通信中的提升。我们介绍了包含真实时间延迟（TTD）的Analog beamforming技术，以解决近场传播和宽频扫描之间的互动，这是现有文献中未曾探讨的领域。我们的方法是通过对共同功率分配和Analog beamformer设计进行优化，使用两个阶段的过程：一个半数字解决方案和Analog近似。这个问题可以通过 alternate optimization、分数编程和块顺序上升最小化技术来有效地解决。此外，我们还提出了一种低复杂度扫描矩阵射频环境中的Beamforming策略，基于近场宽频传播的几何意味，这也可以作为优化方法的robust初值。数值结果证明了我们提出的方法的有效性，明确地表明它们在加强宽频PLS方面的优势，以及通过利用低成本的Analog设备实现的高效秘密能量占用率。
Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces
paper_authors: Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Kai-Kit Wong
for: 提高无线能量传输效率和互联网智能设备的连接数量
methods: 使用低成本、静止反射元件，并利用phasered beam rotation技术，实现无 Channel State Information (CSI) 探测 schemes
results: 提高了无线传输效率和设备连接数量，并在大规模设备场景下表现更高效，不需要额外的硬件更新或修改Abstract
Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting elements to enhance the passive beam gain, improve Wireless Energy Transfer (WET) efficiency, and enable its deployment for numerous Internet of Things (IoT) devices. However, the increasing number of IRS elements presents considerable channel estimation challenges. This is due to the lack of active Radio Frequency (RF) chains in an IRS, while pilot overhead becomes intolerable. To address this issue, we propose a Channel State Information (CSI)-free scheme that maximizes received energy in a specific direction and covers the entire space through phased beam rotation. Furthermore, we take into account the impact of an imperfect IRS and meticulously design the active precoder and IRS reflecting phase shift to mitigate its effects. Our proposed technique does not alter the existing IRS hardware architecture, allowing for easy implementation in the current system, and enabling access or removal of any Energy Receivers (ERs) without additional cost. Numerical results illustrate the efficacy of our CSI-free scheme in facilitating large-scale IRS without compromising performance due to excessive pilot overhead. Furthermore, our scheme outperforms the CSI-based counterpart in scenarios involving large-scale ERs, making it a promising solution in the era of IoT.
摘要
智能反射Surface（IRS）利用低成本、 Passtive 反射元件提高无源能量传输（WET）效率，并使其可以用于互联网未来设备。然而，随着IRS元件的增加，通道估算带来了很大挑战。这是因为IRS没有活动Radio Frequency（RF）链，而且在pilot overhead增加的情况下，估算变得不可持。为了解决这个问题，我们提出了一种CSIfree的方案，它可以在特定的方向上最大化接收能量，并通过phasered beam rotation覆盖整个空间。此外，我们考虑了IRS的不完美性，并仔细设计了活动预编器和反射相位调整，以mitigate其影响。我们的提议方法不会改变现有IRS硬件架构，因此可以轻松实现在现有系统中，并且允许ER的添加或 removalfoundation without additional cost。数字结果表明我们的CSIfree方案可以在大规模IRS scenario中实现高性能，而且在大量ER的场景下，我们的方案比CSIs based counterpart更高效，这使得它成为iot时代的一个有望的解决方案。
Low Complexity High Speed Deep Neural Network Augmented Wireless Channel Estimation
methods: 使用 Deep Neural Network-Augmented Least Square (LC-LSDNN) 算法，并对 received complex symbols 使用不同的 Deep Neural Network (DNN) 进行处理。
results: 与 MMSE 和现有 DL-based CE 比较，LC-LSDNN 具有更高的精度和速度，可以在 60% 更高的时钟频率下运行，并且具有更低的计算资源。Abstract
The channel estimation (CE) in wireless receivers is one of the most critical and computationally complex signal processing operations. Recently, various works have shown that the deep learning (DL) based CE outperforms conventional minimum mean square error (MMSE) based CE, and it is hardware-friendly. However, DL-based CE has higher complexity and latency than popularly used least square (LS) based CE. In this work, we propose a novel low complexity high-speed Deep Neural Network-Augmented Least Square (LC-LSDNN) algorithm for IEEE 802.11p wireless physical layer and efficiently implement it on Zynq system on chip (ZSoC). The novelty of the LC-LSDNN is to use different DNNs for real and imaginary values of received complex symbols. This helps reduce the size of DL by 59% and optimize the critical path, allowing it to operate at 60% higher clock frequency. We also explore three different architectures for MMSE-based CE. We show that LC-LSDNN significantly outperforms MMSE and state-of-the-art DL-based CE for a wide range of signal-to-noise ratios (SNR) and different wireless channels. Also, it is computationally efficient, with around 50% lower resources than existing DL-based CE.
摘要
频率接收器的频率估计（CE）是无线接收器中最 kritical 和 computationally 复杂的信号处理操作。最近几年，不同的工作表明，使用深度学习（DL）基于CE可以超越传统的最小平均方差（MMSE）基于CE，同时具有硬件友好性。然而，DL-based CE 的复杂性和延迟比较高于常用的最小二乘（LS）基于CE。在这个工作中，我们提出了一种新的低复杂度高速的深度神经网络增强了最小二乘（LSDNN）算法，用于 IEEE 802.11p 无线物理层。LSDNN 的新特点在于，使用不同的深度神经网络来处理实数和虚数据的收到复杂符号的两个部分。这有助于减少 DL 的大小，并且优化了关键路径，使其能够在更高的时钟频率上运行。我们还探索了三种不同的 MMSE 基于 CE 架构。我们发现，LC-LSDNN 可以明显超越 MMSE 和现有的 DL-based CE，在各种信号噪声比（SNR）和不同的无线通道上。此外，它还是计算效率高，与现有的 DL-based CE 的资源使用量相比，下降约50%。