作者:
Xia, FuningWang, JunyuanDai, LinTongji Univ
Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ
Inst Adv Study Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ
Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China City Univ Hong Kong
Dept Elect Engn SAR Hong Kong Peoples R China
Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered...
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Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ((BCF)-F-2-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed (BCF)-F-2-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our (BCF)-F-2-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.
Pulse diversity phase-coded (PDPC) waveform has received considerable attention recently because of its applications to obtain synthetic bandwidth, mitigate range-Doppler ambiguity, or suppress artificial repeat jammi...
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Pulse diversity phase-coded (PDPC) waveform has received considerable attention recently because of its applications to obtain synthetic bandwidth, mitigate range-Doppler ambiguity, or suppress artificial repeat jamming and clutter. However, when applied to pulse-Doppler (PD) radar, high range-Doppler sidelobes will occur at the receiver, which cannot be improved by classical ambiguity function (AF) shaping methods. To address this problem, we first derive the discrete periodic AF (PAF) to characterize the range-Doppler processing of the PD radar. On this basis, we formulate a joint design problem for PDPC waveform and mismatched filter bank over the range-Doppler bins of interest under signal-to-noise ratio (SNR) loss and constant modulus constraints, which can be used to simultaneously suppress strong sidelobe interference and artificial repeat jamming. To solve the PAF shaping problem, which contains high-order polynomial constraints, we generalize and apply the maximum block improvement type algorithm with the help of the alternating direction method of multipliers and introducing auxiliary variables. In particular, a time-varying step-size strategy based on the dual theory is presented to enhance the convergence performance and refine the optimal solution. Several simulations are performed to show the effectiveness of the presented approach in improving the detection performance of PD radar, especially under low SNR scenarios.
In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and...
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In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Lo & eacute;ve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cram & eacute;r-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes.
In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequenc...
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In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequency wideband (SFW) effects. To obtain high accuracy of channel state information (CSI), the proposed algorithm initially utilizes a deep neural network (DNN) for channel estimation. Then, the estimated channel is expressed as a third-order low-rank tensor model, on which the canonical polyadic (CP) decomposition is performed to obtain three factor matrices. These factor matrices hold the information pertaining to channel parameters. By fitting the constructed tensor model, channel parameters, such as Angles of Departure (AoDs), Angles of Arrival (AoAs), time delay, and complex gains, can be extracted. Ultimately, the positions of mobile station (MS) and scattering points are determined by utilizing the mapping relationship between the channel parameters and position coordinates. In contrast to existing algorithms, the proposed algorithm delivers greater precision in both channel estimation and positioning. The simulation results demonstrate that the proposed algorithm maintains outstanding ISAC performance, persisting even with diminished compression rate. Furthermore, the proposed algorithm proves effective in more complex scenarios lacking a line-of-sight (LOS) path.
For low-frequency source localization with a small-aperture acoustic array, we propose a high-resolution localization algorithm based on complex Wishart prior. The algorithm, named Wishart-CSM-SBL, employs vectorized ...
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For low-frequency source localization with a small-aperture acoustic array, we propose a high-resolution localization algorithm based on complex Wishart prior. The algorithm, named Wishart-CSM-SBL, employs vectorized cross-spectral matrix (CSM) preprocessing and performs parameter updating within the sparse Bayesian learning (SBL) framework. Existing SBL algorithms struggle to capture the complex correlations between nonadjacent columns of the dictionary set in small-aperture, low-frequency scenarios, often resulting in failed signal recovery. To solve this problem, the Wishart-CSM-SBL algorithm introduces the complex Wishart distribution and develops novel priors for sparse signal and noise. Specifically, the sparse signal is characterized by a two-layer prior model comprising complex Gaussian and complex Wishart distributions. By capturing the intricate correlations among the columns of the dictionary set, this modeling approach significantly improves the accuracy and robustness of sparse recovery. The complex Wishart distribution is employed to represent the noise with an unknown structure, addressing the performance degradation in existing algorithms that assume noise with uniform variance. This is achieved by accounting for noise in-homogeneity and correlation. In addition, a 2-D off-grid solution is extended to eliminate localization errors caused by coarse grid division. Finally, simulations verify that the algorithm outperforms existing algorithms for small-aperture arrays and low-frequency source scenarios.
This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined...
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This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirac Delta distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirac Delta-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines.
This paper proposes a blind equalization algorithm for dispersive wireless communication systems that employ high throughput quadrature amplitude modulation signals under both Gaussian and impulsive noise environments...
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This paper proposes a blind equalization algorithm for dispersive wireless communication systems that employ high throughput quadrature amplitude modulation signals under both Gaussian and impulsive noise environments. A novel cost function that combines the modulus match error function with the negative Gaussian kernel function is established to efficiently obtain the weight vector associated with the blind equalizer. Some preferable properties of the novel cost function are presented. Intensive studies show that the proposed cost function efficiently reduces the maladjustment caused by the modulus mismatch error and efficiently suppresses the negative influence resulting from large errors. Moreover, an efficient successive approximation method for minimizing the established cost function is proposed for fast searching of the optimal weight vector. Very importantly, it is proved that the proposed successive approximation method possesses superlinear convergence. Finally, extensive simulations are provided to demonstrate that the proposed blind equalizer has better performances than the existing methods under both Gaussian and impulsive noise circumstances in terms of equalization quality and equalization efficiency.
The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of ...
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The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(epsilon(-2)) to achieve an epsilon-stationary solution, we introduce and analyze a new formulation termed Diffusion Stochastic Same-Sample Optimistic Gradient (DSS-OG). We prove its convergence and resolve the large batch issue by establishing a tighter upper bound, under the more general setting of nonconvex Polyak-Lojasiewicz (PL) risk functions. We also extend the applicability of the proposed method to the distributed scenario, where agents communicate with their neighbors via a left-stochastic protocol. To implement DSS-OG, we can query the stochastic gradient oracles in parallel with some extra memory overhead, resulting in a complexity comparable to its conventional counterpart. To demonstrate the efficacy of the proposed algorithm, we conduct tests by training generative adversarial networks.
作者:
Wang, BoChen, FengMo, ShiqiHarbin Engn Univ
Natl Key Lab Underwater Acoust Technol Harbin 150001 Peoples R China Harbin Engn Univ
Minist Ind & Informat Technol Key Lab Marine Informat Acquisit & Secur Harbin 150001 Peoples R China Harbin Engn Univ
Coll Underwater Acoust Engn Harbin 150001 Peoples R China
The actual underwater acoustic field environment is subject to various interferences, resulting in high background noise, thereby complicating underwater target detection. Some traditional subspace direction-of-arriva...
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The actual underwater acoustic field environment is subject to various interferences, resulting in high background noise, thereby complicating underwater target detection. Some traditional subspace direction-of-arrival (DOA) estimation algorithms often require prior knowledge of the number of sources, leading to performance degradation if the number of sources is estimated inaccurately. With advancements in sensor technology, scholars have developed acoustic vector sensors (AVSs). However, some traditional algorithms applied directly to AVS result in degraded performance of the algorithm. To address these challenges, we propose a DOA estimation algorithm that is independent of the number of sources and is applicable to AVS. This is achieved by constructing the generalized minimum variance distortionless response (G-MVDR) model based on AVS and exploring the eigenvalue ordering principle under the G-MVDR framework. It provides high-resolution DOA results independent of the number of sources and can be flexibly applied to AVS arrays. The proposed algorithm is analyzed with respect to eigenvalue ranking, and the optimal threshold is determined. Finally, the accuracy and effectiveness of the proposed algorithm are validated through simulations and experiments.
Frequency-modulated continuous-wave (FMCW) radar has been widely utilized for target detection and motion sensing. However, conventional techniques are constrained by radar bandwidth, spectral leakage, and noise, maki...
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Frequency-modulated continuous-wave (FMCW) radar has been widely utilized for target detection and motion sensing. However, conventional techniques are constrained by radar bandwidth, spectral leakage, and noise, making the separation and accurate detection of multiple targets within range resolution a significant challenge. To address these limitations, a reconfigurable frequency-spatial equivalent array (RFSEA) technique is proposed. A frequency-domain virtual array is constructed by exploiting the characteristics of the spatial array, effectively improving the signal-to-noise ratio (SNR), enhancing the performance of the subspace algorithm, and significantly substantially increasing range resolution to achieve more accurate close-spaced multitargets separation. In addition, the introduction of frequency-domain adaptive digital beamforming (FDADBF) technology minimizes mutual interference caused by spectral leakage, facilitating highly accurate motion measurement. Simulation and experimental results demonstrate that the proposed technique improves range resolution by 42.8%, achieves motion measurement within range resolution with average root-mean-square errors (RMSEs) below 10%, and delivers a threefold improvement in accuracy compared to conventional methods. These findings underscore the potential of RFSEA in advancing high-resolution and precise motion sensing applications.
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