This paper presents a fundamental performance analysis of joint location and velocity estimation in a cell-free (CF) MIMO integrated sensing and communication (ISAC) system. Unlike prior studies that primarily rely on continuous-time signal models, we consider a more practical and challenging scenario in the discrete-time digital domain. Specifically, we first formulate a logarithmic likelihood function (LLF) and corresponding maximum likelihood estimation (MLE) for both single- and multiple-target sensing. Building upon the proposed LLF framework, closed-form Cramer-Rao lower bounds (CRLBs) for joint location and velocity estimation are derived under deterministic, unknown, and spatially varying radar cross-section (RCS) models. These CRLBs can serve as a fundamental performance metric to guide CF MIMO-ISAC system design. To enhance tractability, we also develop a class of simplified closed-form CRLBs, referred to as approximate CRLBs, along with a rigorous analysis of the conditions under which they remain accurate. Furthermore, we investigate how the sampling rate, squared effective bandwidth, and time width influence CRLB performance. For multi-target scenarios, the concepts of safety distance and safety velocity are introduced to characterize the conditions under which the CRLBs converge to their single-target counterparts. Extensive simulations using orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) validate the theoretical findings and provide practical insights for CF MIMO-ISAC system design

This paper, for the first time, presents a closed-form error performance analysis of uplink power-domain non-orthogonal multiple access (PD-NOMA) with dynamic successive interference cancellation (SIC) decoding, where the decoding order is adapted to the instantaneous channel conditions. We first develop an analytical framework that characterizes how dynamic ordering affects error probabilities in uplink PD-NOMA systems. For a two-user system over independent and non-identically distributed Rayleigh fading channels, we derive closed-form probability density functions (PDFs) of ordered channel gains and the corresponding unconditional pairwise error probabilities (PEPs). To address the mathematical complexity of characterizing ordered channel distributions, we employ a Gaussian fitting to approximate truncated distributions while maintaining analytical tractability. Finally, we extend the bit error rate analysis for various $M$-quadrature amplitude modulation schemes (QAM) in both homogeneous and heterogeneous scenarios. Numerical results validate the theoretical analysis and demonstrate that dynamic SIC eliminates the error floor issue observed in fixed-order SIC, achieving significantly improved performance in high signal-to-noise ratio regions. Our findings also highlight that larger power differences are essential for higher-order modulations, offering concrete guidance for practical uplink PD-NOMA deployment.

Deep neural networks (DNNs), such as the widely-used GPT-3 with billions of parameters, are often kept secret due to high training costs and privacy concerns surrounding the data used to train them. Previous approaches to securing DNNs typically require expensive circuit redesign, resulting in additional overheads such as increased area, energy consumption, and latency. To address these issues, we propose a novel hardware-software co-design approach for DNN intellectual property (IP) protection that capitalizes on the inherent aging characteristics of circuits and a novel differential orientation fine-tuning (DOFT) to ensure effective protection.

More and more end-to-end text spotting methods based on Transformer architecture have demonstrated superior performance. These methods utilize a bipartite graph matching algorithm to perform one-to-one optimal matching between predicted objects and actual objects. However, the instability of bipartite graph matching can lead to inconsistent optimization targets, thereby affecting the training performance of the model. Existing literature applies denoising training to solve the problem of bipartite graph matching instability in object detection tasks. Unfortunately, this denoising training method cannot be directly applied to text spotting tasks, as these tasks need to perform irregular shape detection tasks and more complex text recognition tasks than classification. To address this issue, we propose a novel denoising training method (DNTextSpotter) for arbitrary-shaped text spotting. Specifically, we decompose the queries of the denoising part into noised positional and noised content queries. We use the four Bezier control points of the Bezier center curve to generate the noised positional queries. For the noised content queries, considering that the output of the text in a fixed positional order is not conducive to aligning position with content, we employ a masked character sliding method to initialize noised content queries, thereby assisting in the alignment of text content and position. Additionally, to improve the model's perception of the background, we further utilize an additional loss function for background characters classification in the denoising training part. DNTextSpotter outperforms state-of-the-art methods on four benchmarks-Total-Text, SCUT-CTW1500, ICDAR15, and Inverse-Text-most notably achieving an 11.3% improvement over the best approach on Inverse-Text.

To liberate indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks from wired fronthaul, this paper proposes a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. In our scheme, two layers of fronthaul links rely on the mutually orthogonal subcarrier sets in the same THz band, while access links are implemented over sub-6G band. However, the proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets and the resource allocation at both the central processing unit (CPU) and APs. Hence, in order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the involved binary variables. Then, the overall end-to-end optimization is implemented by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the networks of varying sizes. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that empowered by sufficient bandwidth, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (J-ABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellation-based J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.

Digital twin is considered an alternative for optimizing real-world performance within virtual context, which also applies to the optimization ofSupply Chain Management(SCM). Blockchain, which facilitates data secure storage and trusted tracking, is deemed to be a proper assistant technology for achieving digital twin implementation. In this work, we propose a blockchain-based digital twin solution to reengineer SCM system, which promotes the digitization and intelligence of SCM to fit in massive service volumes in complex-intercrossed industry system. A strong-weak consensus mode is developed to achieve energy and time savings. We also design intelligent switch-based algorithms to generate time-saving consensus plans under energy constraints. Finally, we set up multiple experiments to compare our algorithm with three baseline algorithms, includingEffective Iterative Greedy(EIG),Two Dimensional Genetic(TDG), andHigh-level Task Scheduling Dynamic Programming(HTSDP). Findings from evaluation demonstrate the potential of our proposed model. Specifically, our algorithm reduces time and energy consumption of EIG algorithm in consensus by 46.84% and 16.25%, respectively. Compared with TDG algorithm, consensus time and energy consumption of our algorithm are reduced by 50.05% and 48.46%. Our algorithm cuts down time spent of HTSDP algorithm in generating consensus plan by a factor of 9.88.

Artificial Intelligence (AI) technology has been empowered to be a significant driven force within the edge context for powering up contemporary complex systems, such as smart critical infrastructure. Interconnectivity between physical and cyber spaces further introduces the needs of digital twin, which allows AI-based solutions to optimize various tasks in physical operations. However, due to the complexity of the setting in digital twin, task allocation is encountering multiple challenges, such as concurrent meeting the requirements of energy saving, efficiency, and accuracy. In this work, we propose a Digital Twin-Enabled Edge AI (DTE2AI), supported by our Energy-aware High Accuracy Strategy (EAHAS), which focuses on optimizing the training accuracy of AI tasks under the limits of training time and energy consumption. The average of the training accuracy was enhanced 12% based on our experiment evaluations.

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

We investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price), and temporally coupled operational constraints, it is very challenging to design an optimal energy management algorithm for scheduling heating, ventilation, and air conditioning systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management algorithm based on deep deterministic policy gradients. It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building the thermal dynamics model. The simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.

An important issue in an edge computing (EC) network is to increase the utilities of the end users concurrently accessing the computation resources. In this paper, we consider the task offloading in EC-enabled networks where the end users efficiently utilize the dispersed computation and communication resources in a multi-path multi-hop manner. We propose a binary optimization framework that generalizes multi-hop wireless EC task offloading as jointly making decisions of server selecting and traffic routing in networks of arbitrary topology (JoSRAT). We further develop an approximation algorithm JoSRAT that enables for a fully distributed implementation together with the worst-case performance guarantees. Interestingly, our proposed distributed algorithm achieves nearly optimal in the numerical evaluations, significantly outperforming the worst-case guarantees. The proposed algorithm also outperforms a widely-used heuristic, i.e., First Fit, in terms of computational time complexity, indicating the superior capability of the proposed framework.