results: 在各种MIMO配置下，通过考虑劳顿抖音滤波（Co-CFO）的影响，对比传统方案，提出方案显示出更好的性能，并提供了关于该方案计算复杂度的封闭公式。Abstract
Discrete Cosine Transform (DCT) can be used instead of conventional Discrete Fourier Transform (DFT) for the Orthogonal Frequency Division Multiplexing (OFDM) construction, which offers many advantages. In this paper, the Multiple-Input-Multiple-Output (MIMO) DCT-OFDM is enhanced using a proposed Cosine Domain Equalizer (CDE) instead of a Frequency Domain Equalizer (FDE). The results are evaluated through the Rayleigh fading channel with Co-Carrier Frequency Offset (Co-CFO) of different MIMO configurations. The average bit error probability and the simulated time of the proposed scheme and the conventional one is compared, which indicates the importance of the proposed scheme. Also, a closed formula for the number of arithmetic operations of the proposed equalizer is developed. The proposed equalizer gives a simulation time reduction of about 81.21%, 83.74% compared to that of the conventional LZF-FDE, and LMMSE-FDE, respectively for the case of 4x4 configuration.
摘要
离散归一Transform (DCT) 可以取代传统的离散弗洛勒Transform (DFT) 在Orthogonal Frequency Division Multiplexing (OFDM) 结构中使用，这有多种优点。在这篇论文中，我们使用一种提议的Cosine Domain Equalizer (CDE) 代替Frequency Domain Equalizer (FDE) 来进行多输入多输出 (MIMO) DCT-OFDM 的改进。我们通过套用辐射折射通道进行评估，并对不同的MIMO配置进行比较。结果表明，我们的方案比传统的方案更好，并且我们还发现了一个关闭的公式来计算提议的平衡器的数学操作数量。在4x4配置下，我们的平衡器可以减少计算时间约81.21%、83.74%，相比传统的LZF-FDE和LMMSE-FDE。
Study of MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks
methods: 研究人员使用了closed form sum-rate表达式 derivation дляCF和CLCF网络，并使用了线性预编码器（包括零干扰（ZF）和最小平均方差预编码器（MMSE））。
results: 研究人员提出了基于MMSE资源分配技术和改进的增加策略以提高CLCF网络的系统性能，并通过数值计算结果显示，该技术比既有方法有更好的性能，同时计算成本和信号占用量在CLCF网络中大幅减少。Abstract
In this paper, a downlink cell-free massive multiple-input multiple-output (CF massive MIMO) system and a network clustering is considered. Closed form sum-rate expressions are derived for CF and the clustered CF (CLCF) networks where linear precoders included zero forcing (ZF) and minimum mean square error (MMSE) are implemented. An MMSE-based resource allocation technique with multiuser scheduling based on an enhanced greedy technique and power allocation based on the gradient descent (GD) method is proposed in the CLCF network to improve the system performance. Numerical results show that the proposed technique is superior to the existing approaches and the computational cost and the signaling load are essentially reduced in the CLCF network.
摘要
在这篇论文中，我们考虑了下降链Cell-free大量多输入多输出（CF大量MIMO）系统和网络划分。我们 derivatedclosed form的吞吐量表达式 дляCF和划分CF（CLCF）网络，其中使用了线性 precoder，包括零干扰（ZF）和最小平均方差error（MMSE）。我们提出了基于MMSE的资源分配技术，包括多用户调度和基于扩展的greedy技术，以及基于梯度下降（GD）方法的功率分配。numerical results表明，我们的方法与现有方法相比，具有更高的系统性能，并且计算成本和信号载量都有了显著的降低。
Robust Joint Estimation of Galaxy Redshift and Spectral Templates using Online Dictionary Learning
methods: 该算法使用了我们的非线性扩展，可以 JOINTLY estimating the underlying spectral features common across the entire dataset，以及每个星系的红shift。该算法可以处理大量数据，只需要处理一个spectrum at a time。
results: 该算法在一个Mock SPHEREx数据集上表现出色，与现有的状态 искусственный算法相比，具有较低的NMAD标准差（0.18%）和catastrophic error rate（0.40%），并且在各种信号噪比（SNR）下都能够达到低于1%的NMAD和catastrophic error。Abstract
We present a novel approach to analyzing astronomical spectral survey data using our non-linear extension of an online dictionary learning algorithm. Current and upcoming surveys such as SPHEREx will use spectral data to build a 3D map of the universe by estimating the redshifts of millions of galaxies. Existing algorithms rely on hand-curated external templates and have limited performance due to model mismatch error. Our algorithm addresses this limitation by jointly estimating both the underlying spectral features in common across the entire dataset, as well as the redshift of each galaxy. Our online approach scales well to large datasets since we only process a single spectrum in memory at a time. Our algorithm performs better than a state-of-the-art existing algorithm when analyzing a mock SPHEREx dataset, achieving a NMAD standard deviation of 0.18% and a catastrophic error rate of 0.40% when analyzing noiseless data. Our algorithm also performs well over a wide range of signal to noise ratios (SNR), delivering sub-percent NMAD and catastrophic error above median SNR of 20. We released our algorithm publicly at github.com/HyperspectralDictionaryLearning/BryanEtAl2023 .
摘要
我们提出了一种新的方法来分析天文 spectral 数据，使用我们的非线性扩展的在线词典学习算法。现有和未来的Surveys 如 SPHEREx 将使用spectral数据构建宇宙3D地图，估计数百万个星系的红shift。现有算法依赖于手工制作的外部模板，性能有限因模型匹配错误。我们的算法解决了这个限制，同时 estimate 天文spectral 数据中共同存在的下游特征，以及每个星系的红shift。我们的在线方法可扩展到大量数据，只需处理单个spectrum 的内存中数据。我们的算法在分析Mock SPHEREx 数据集时表现出优于现有状态态算法，实现了0.18%的NMAD标准差和0.40%的灾难性错误率，对于无噪数据。我们的算法还在各种信号噪比（SNR）范围内表现出优秀，实现了低于1%的NMAD和灾难性错误，SNR 的 median 值高于20。我们在github.com/HyperspectralDictionaryLearning/BryanEtAl2023 上公开了我们的算法。
Received Signal and Channel Parameter Estimation in Molecular Communications
paper_authors: O. Tansel Baydas, Ozgur B. Akan for:This paper provides a novel molecular communication (MC) model that incorporates a spherical transmitter and receiver with partial absorption, offering a more realistic representation than previous receiver architectures in literature.methods:The paper employs an optimization-based technique using particle swarm optimization (PSO) to accurately estimate the cumulative number of molecules received, and iterative maximum likelihood estimation (MLE) for parameter estimation.results:The proposed model and estimation technique achieve a significant improvement of 5 times in terms of root mean square error (RMSE), and provide an approximate analytical impulse response for estimating channel parameters such as distance, diffusion coefficient, or a combination of both.Abstract
Molecular communication (MC) is a paradigm that employs molecules as information transmitters, hence, requiring unconventional transceivers and detection techniques for the Internet of Bio-Nano Things (IoBNT). In this study, we provide a novel MC model that incorporates a spherical transmitter and receiver with partial absorption. This model offers a more realistic representation than receiver architectures in literature, e.g. passive or entirely absorbing configurations. An optimization-based technique utilizing particle swarm optimization (PSO) is employed to accurately estimate the cumulative number of molecules received. This technique yields nearly constant correction parameters and demonstrates a significant improvement of 5 times in terms of root mean square error (RMSE). The estimated channel model provides an approximate analytical impulse response; hence, it is used for estimating channel parameters such as distance, diffusion coefficient, or a combination of both. We apply iterative maximum likelihood estimation (MLE) for the parameter estimation, which gives consistent errors compared to the estimated Cramer-Rao Lower Bound (CLRB).
摘要
分子通信（MC）是一种 paradigm，它使用分子作为信息传输者，因此需要不同于传统的接收器和检测技术。在这种研究中，我们提供了一种新的 MC 模型，该模型使用球形发射器和接收器，并具有部分吸收。这种模型比文献中的接收器体系结构更加真实，如完全吸收或被动的配置。我们使用优化基于 particle swarm optimization（PSO）技术来准确估计接收到的分子数的总和。这种技术可以提供相对固定的修正参数，并表现出5倍的改善， measured in terms of root mean square error（RMSE）。这个估计的通道模型可以提供一个近似的分析函数，因此可以用于估计通道参数，如距离、扩散系数或两者的组合。我们使用迭代最大 LIKElihood estimation（MLE）进行参数估计，这种方法可以与估计的 Cramer-Rao Lower Bound（CLRB）进行比较。
Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks
paper_authors: Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova
for: This paper is written for the purpose of exploring a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization in bandwidth-limited scenarios.
methods: The paper proposes a novel fusion-based estimation process that assigns appropriate weights to the range estimation of each base station (BS) to mitigate the effects of multipath and improve localization accuracy.
results: Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.Abstract
Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate localization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echos. Existing localization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FR1) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.
摘要
<>使用电信基站（BS）进行环境感知将在第六代（6G）网络中引入一项破坏性功能。然而，通过多path导致反射脉冲的准确位置和定位具有挑战性。现有的定位技术在具有大带宽的情况下表现良好，但在带宽限制的情况下表现不佳。为解决这个问题，在这项工作中，我们提出了基于5G新Radio（NR）的合作多单Static感知框架，用于在Frequency Range 1（FR1）频段进行投射式目标定位。我们提议一种基于权重融合的估算过程，以减少多path的影响。通过使用光栅轨迹的 simulations，我们证明了我们的多感知框架在带宽限制的情况下的效果。Note: "Frequency Range 1" (FR1) is a term used in 5G specifications to refer to the frequency range of 450 MHz to 6 GHz.
On RIS-Aided SIMO Gaussian Channels: Towards A Single-RF MIMO Transceiver Architecture
results: 研究发现，在某些情况下，使用 RIS 可以提高 SIMO 系统的容量。此外，提出了一种新的接收机架构，该架构只有一个 RF 前端，并可以使得通道被视为一个 concatenation 的vector Gaussian channel和多个 phase-modulated channel。Abstract
In this paper, for a single-input multiple-output (SIMO) system aided by a passive reconfigurable intelligent surface (RIS), the joint transmission accomplished by the single transmit antenna and the RIS with multiple controllable reflective elements is considered. Relying on a general capacity upper bound derived by a maximum-trace argument, we respectively characterize the capacity of such \rev{a} channel in the low-SNR or the rank-one regimes, in which the optimal configuration of the RIS is proved to be beamforming with carefully-chosen phase shifts. To exploit the potential of modulating extra information on the RIS, based on the QR decomposition, successive interference cancellation, and a strategy named \textit{partially beamforming and partially information-carrying}, we propose a novel transceiver architecture with only a single RF front end at the transmitter, by which the considered channel can be regarded as a concatenation of a vector Gaussian channel and several phase-modulated channels. Especially, we investigate a class of vector Gaussian channels with a hypersphere input support constraint, and not only generalize the existing result to arbitrary-dimensional real spaces but also present its high-order capacity asymptotics, by which both capacities of hypersphere-constrained channels and achievable rates of the proposed transceiver with two different signaling schemes can be well-approximated. Information-theoretic analyses show that the transceiver architecture designed for the SIMO channel has a boosted multiplexing gain, rather than one for the conventionally-used optimized beamforming scheme.Numerical results verify our derived asymptotics and show notable superiority of the proposed transceiver.
摘要
在这篇论文中，我们考虑了单输入多输出（SIMO）系统，其中单个发射天线和pasive重配置智能表面（RIS）的多个可控反射元素的共同传输。我们根据最大跟踪Argument derive一个通用容量Upper bound，然后分别Characterize了这种渠道的容量在low SNR或rank-one regime中，并证明了在这些 régime中，最佳的RIS配置是 beamforming with carefully chosen phase shifts。为了利用RIS的可模塑性，我们基于QR分解、Successive Interference Cancellation和一种名为\textit{partially beamforming and partially information-carrying}的策略，提出了一种新的接收机架构，其中只有一个RF前端在发射器端，从而将考虑的渠道视为一个 concatenation of vector Gaussian channel和多个 phase-modulated channels。特别是，我们研究了一类vector Gaussian channels with hypersphere input support constraint，并不仅总结了现有结果到任意维度实空间中，还提供了其高阶容量尺度 asymptotics，从而可以良好地approximate hypersphere-constrained channels的容量和提案的接收机的可达性。信息理论分析表明，提案的接收机架构具有加大的多路复用效果，而不是使用传统的优化 beamforming scheme。numerical results verify our derived asymptotics and show notable superiority of the proposed transceiver.
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
paper_authors: Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
for: This paper is written for researchers and developers who are interested in designing hardware accelerators for spiking neural networks (SNNs).
methods: The paper proposes a lightweight neuron layer architecture that allows SNNs to be directly mapped onto digital hardware, using differential time coding of spike sequences and decoupling processing time and spike timing.
results: The authors present synthesis and performance results showing that their architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a state-of-the-art FPGA, and demonstrate the robustness of their approach to quantization.Here is the same information in Simplified Chinese text:
results: 作者们提供了合成和性能结果，证明他们的架构可以实现超过1000个神经元的网络，在当今最先进的FPGA上达到高频率，并证明其鲁棒性于量化。Abstract
Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs). Since the structure of SNNs is less suited for typically used accelerators such as GPUs than conventional ANNs, there is a demand for custom hardware accelerators for processing SNNs. In the past, the main focus was on platforms that resemble the structure of multiprocessor systems. In this work, we propose a lightweight neuron layer architecture that allows network structures to be directly mapped onto digital hardware. Our approach is based on differential time coding of spike sequences and the decoupling of processing time and spike timing that allows the SNN to be processed on different hardware platforms. We present synthesis and performance results showing that this architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a State-of-the-Art FPGA. We furthermore show results on the robustness of our approach to quantization. These results demonstrate that high-accuracy inference can be performed with bit widths as low as 4.
摘要
快活 нейрон网络（SNN）在最近几年内受到了关注，因为它们可以更好地处理稀疏和事件驱动的数据，而不是常见的人工神经网络（ANNs）。由于SNNs的结构不适合通常使用的加速器 such as GPUs，因此有一些需求为SNNs的特定硬件加速器。在过去，主要的注意力是关于类似多处理器系统的平台。在这种工作中，我们提出了一种轻量级神经层架构，允许网络结构直接被映射到数字硬件上。我们的方法基于时差编码的脉冲序列和脉冲时间和处理时间的解 couple，使SNN可以在不同的硬件平台上进行处理。我们提供了合成和性能结果，证明这种架构可以实现超过1000个神经元的网络，并且在现代FPGA上可以达到高频率。此外，我们还展示了量化效果的robustness。这些结果表明，使用4个bit宽度可以实现高精度的推理。
Exploiting Active RIS in NOMA Networks with Hardware Impairments
results: 研究结果显示：一、ARIS-NOMA-HIS网络在堆叠 Nakagami-m 抑压通道上提供了更高的多样性顺序和多重化吞吐量；二、随着ARIS的反射率增加，ARIS-NOMA-HIS网络的失败性能得到了强化；三、ARIS-NOMA-HIS网络比ARIS/PRIS-OMA网络和传统的合作方案更能节省能量。Abstract
Active reconfigurable intelligent surface (ARIS) is a promising way to compensate for multiplicative fading attenuation by amplifying and reflecting event signals to selected users. This paper investigates the performance of ARIS assisted non-orthogonal multiple access (NOMA) networks over cascaded Nakagami-m fading channels. The effects of hardware impairments (HIS) and reflection coefficients on ARIS-NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) are considered. More specifically, we develop new precise and asymptotic expressions of outage probability and ergodic data rate with ipSIC/pSIC for ARIS-NOMA-HIS networks. According to the approximated analyses, the diversity orders and multiplexing gains for couple of non-orthogonal users are attained in detail. Additionally, the energy efficiency of ARIS-NOMA-HIS networks is surveyed in delay-limited and delay-tolerant transmission schemes. The simulation findings are presented to demonstrate that: i) The outage behaviors and ergodic data rates of ARIS-NOMA-HIS networks precede that of ARIS aided orthogonal multiple access (OMA) and passive reconfigurable intelligent surface (PRIS) aided OMA; ii) As the reflection coefficient of ARIS increases, ARIS-NOMA-HIS networks have the ability to provide the strengthened outage performance; and iii) ARIS-NOMA-HIS networks are more energy efficient than ARIS/PRIS-OMA networks and conventional cooperative schemes.
摘要
活动可重新配置智能表面（ARIS）是一种有前途的方法，用于资料减少多重折射减弱的影响，通过增强和反射事件信号来选择性地向用户进行传输。这篇论文研究了ARIS协助非对称多接入（NOMA）网络在堆积 Nakagami-m 折射通道上的性能。我们考虑了硬件缺陷（HIS）和反射系数的影响，并分析了 ipSIC 和 pSIC 在 ARIS-NOMA-HIS 网络中的效果。具体来说，我们开发了新的精确和极限表达，用于计算 ARIS-NOMA-HIS 网络的失败概率和平均数据速率。根据这些分析结果，我们得出了几个非对称用户的多样性顺序和多重化增益的详细分析结果。此外，我们还对 ARIS-NOMA-HIS 网络的能效性进行了评估，并在延迟限制和延迟忍受传输方案下进行了对比分析。实验结果表明：一、ARIS-NOMA-HIS 网络在失败性和平均数据速率方面比 ARIS 帮助的对称多接入（OMA）和 passive 重配置智能表面（PRIS）帮助的 OMA 网络更佳；二、ARIS 的反射系数增加，ARIS-NOMA-HIS 网络可以提供更强的失业性表现；三、ARIS-NOMA-HIS 网络比 ARIS/PRIS-OMA 网络和传统的合作方案更能效。
An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint
methods: 本文使用了ADMM优化框架，并提出了两种全球最佳解方案，基于 Von Neumann矩阵跟踪不等式 theorem和大致最小化（MM）算法。
results: 通过大量的 simulations，本文证明了提案的ADMM优化算法的有效性和实用性。Abstract
Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking. It is well known that the UAVs-source (sensors-target) geometry, namely geometric configuration, significantly affects the final localization accuracy. This paper focuses on the geometric configuration optimization for received signal strength difference (RSSD)-based passive source localization by drone swarm. Different from prior works, this paper considers a general measuring condition where the spread angle of drone swarm centered on the source is constrained. Subject to this constraint, a geometric configuration optimization problem with the aim of maximizing the determinant of Fisher information matrix (FIM) is formulated. After transforming this problem using matrix theory, an alternating direction method of multipliers (ADMM)-based optimization framework is proposed. To solve the subproblems in this framework, two global optimal solutions based on the Von Neumann matrix trace inequality theorem and majorize-minimize (MM) algorithm are proposed respectively. Finally, the effectiveness as well as the practicality of the proposed ADMM-based optimization algorithm are demonstrated by extensive simulations.
摘要
发射多架无人飞行器（UAV）以找到发射信号源的应用范围广泛，包括搜救和目标跟踪。已知UAV-源（感知器-目标）几何configure significantly affects the final localization accuracy。本文关注于基于接收信号强度差（RSSD）的无人机群找源位置优化 geometric configuration。与先前作品不同，本文考虑了一般测量条件下，无人机群中心点处的扩散角度受限。基于这些限制，我们形ulated a geometric configuration optimization problem to maximize the determinant of Fisher information matrix (FIM)。经过矩阵理论的变换，我们提出了基于 alternate direction method of multipliers (ADMM) 优化框架。为解这些子问题，我们提出了两种全球最优解决方案，分别基于 Von Neumann 矩阵跟踪不等式定理和 majorize-minimize (MM) 算法。最后，我们通过广泛的 simulations 展示了提议的 ADMM-based 优化算法的有效性和实用性。