Precise positioning is becoming increasingly crucial across various applications, including rescue operations, intelligent transportation, logistics, and environmental monitoring. However, previous research often assumes that wireless sensor networks are perfectly synchronized or have unlimited communication resources. These assumptions frequently do not align with real-world scenarios, particularly in resource-constrained networks with stringent power and communication limits. In this paper, we propose a target localization and time synchronization algorithm based on the quantization of time-of-arrival (TOA) measurements. This approach accounts for both the quantization process and clock offsets caused by asynchronous clocks between sensor nodes and the target source. To tackle the problem, a variational Bayesian learning method is proposed to estimate the target source location and clock offsets jointly. This algorithm aims to find a feasible variational distribution that approximates the true posterior distribution, thereby conducting the Bayesian estimation over the approximate distributions and obtaining the accurate locations and clock offsets. Additionally, we derive the quantized Bayesian Cramr-Rao Bound (QBCRB) for quantized TOA measurements to evaluate localization and time synchronization performance. Simulation and experimental results demonstrate that the proposed method provides effective estimations and outperforms other comparison algorithms across various scenarios.

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparse angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.

We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves practical speedups supported by the N:M sparse tensor core. Therefore, we present a novel method of Bi-directional Masks (Bi-Mask) with its two central innovations in: 1) Separate sparse masks in the two directions of forward and backward propagation to obtain training acceleration. It disentangles the forward and backward weight sparsity and overcomes the very dense gradient computation. 2) An efficient weight row permutation method to maintain performance. It picks up the permutation candidate with the most eligible N:M weight blocks in the backward to minimize the gradient gap between traditional unidirectional masks and our bi-directional masks. Compared with existing uni-directional scenario that applies a transposable mask and enables backward acceleration, our Bi-Mask is experimentally demonstrated to be more superior in performance. Also, our Bi-Mask performs on par with or even better than methods that fail to achieve backward acceleration. Project of this paper is available at https://github.com/zyxxmu/Bi-Mask.

By forcing N out of M consecutive weights to be non-zero, the recent N:M fine-grained network sparsity has received increasing attention with its two attractive advantages over traditional irregular network sparsity methods: 1) Promising performance at a high sparsity. 2) Significant speedups when performed on NVIDIA A100 GPUs. Current implementation on N:M sparsity requires a tedious pre-training phase or computationally heavy from-scratch training. To circumvent these problems, this paper presents an efficient solution for achieving N:M fine-grained sparsity from scratch. Specifically, we first make a re-formulation to convert the N:M fine-grained sparsity into a combinatorial problem, in which, the object falls into choosing the best weight combination among $C_M^N$ candidates. Then, we equip each combination with a learnable importance score, which can be jointly optimized along with its associated weights. Through rigorous proof, we demonstrate that the magnitude of the optimized score well reflects the importance of its corresponding weights combination to the training loss. Therefore, by gradually removing combinations with smaller scores till the best one is left, N:M fine-grained sparsity can be efficiently optimized during the normal training phase without any extra expenditure. Comprehensive experimental results have demonstrated that our proposed method for learning best combination, dubbed as LBC, consistently increases the efficacy of the off-the-shelf N:M methods across varying networks and datasets. Our project is released at https://github.com/zyxxmu/LBC.

Most of the existing work about device-to-device (D2D) data offtoading do not simultaneously consider the mobile scenario and the trade-off between the revenue and cost of caching data. In this paper, we consider a more comprehensive and practical scenario of D2D data offloading, and design a mobility-aware incentive mechanism to efficiently select some mobile users to pre-cache the proper contents with the objective of maximizing social welfare by jointly considering mobile users' preference similarity and the social relationship. Simulation results demonstrates that the proposed mechanism outperforms other counterparts. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

Pre-caching popular contents in advance at the network edge such as base stations is a promising method to improve the service quality by reducing the transmission cost and network congestion. In this paper, by jointly considering the mobile users' rating on different contents into the incentive mechanism design, we proposed a rating-aware incentive mechanism for efficiently selecting some BSs to pre-cache the popular contents. The proposed mechanism can achieve higher performance compared with other existing methods in terms of the social welfare. In specific, the proposed mechanism can improve the achieved social welfare by 11.75% and 9.97% compared with the mechanism of random caching and the caching based on bid price, respectively. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.