This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchronous and asynchronous modes are considered for retransmissions. Moreover, a factor graph aggregation (FGA) method is used for multi-user detection. Specifically, a large-scale factor graph is constructed by combining all the received superimposed signals and message passing algorithm (MPA) is applied to calculate log-likelihood ratio (LLR). Monte Carlo simulations are preformed to show that FGA surpasses bit-level combining (BLC) and HARQ with incremental redundancy (HARQ-IR) in synchronous mode. Moreover, FGA performs better than BLC at high signal-to-noise ratio (SNR) region in asynchronous mode. However, FGA in asynchronous mode is worse than BLC at low SNR, because significant error propagation is induced by the presence of failed messages after the maximum allowable HARQ rounds.

The mixed Analog-to-Digital Converter (ADC) architecture is considered a promising solution in balancing the trade-off between high-resolution and low-resolution quantization over the hardware costs, power consumption, and transmission demands in massive multiple-input multiple-output (MIMO) systems. Meanwhile, the Direction of Arrival (DOA) estimation is a prerequisite for accurate beam processing in MIMO systems. Therefore, evaluating the DOA estimation performance in linear array architectures of mixed-ADC based MIMO systems is crucial. However, local bounds, such as the widely used Cramer-Rao Bound (CRB), only offer rigorous performance analysis of the estimator in the high signal-to-noise ratio (SNR) regime. In this paper, we derive a globally effective and closed-form Ziv-Zakai Bound (ZZB) to assess the DOA estimation performance of mixed-resolution quantization structures. We have also provided the CRB for DOA estimation with mixed-resolution quantization and included the classical MUSIC algorithm as a comparison. Additionally, we have analyzed the impact of different prior information, the numbers of snapshots and sensors, quantization bits and parameter settings on the ZZB. Simulation results show that the ZZB provides globally effective bounds under Gaussian and uniform distributions. In particular, in the low SNRs region, the ZZB offers a tighter and more effective bound than CRB and BCRB.

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.

Secure localization and velocity estimation are of great importance in Internet-of-Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this article. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP)-based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms.

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms.