This paper studies an integrated sensing and communication (ISAC) system where a multi-antenna base station (BS) communicates with multiple single-antenna users in the downlink and senses the unknown and random angle information of a target based on its prior distribution information and the received echo signals. We focus on a challenging scenario with heterogeneous unknown parameters where the target's reflection coefficient is also unknown with no prior information. We consider a general transmit beamforming structure with both communication beams and dedicated sensing beams, where the communication users can cancel the interference caused by the pre-determined sensing signals. By adopting the periodic posterior Cramer-Rao bound (PCRB) to quantify a lower bound of the mean-cyclic error (MCE) for sensing the periodic angle parameter, we optimize the transmit beamforming to minimize the periodic PCRB, subject to individual communication user rate constraints, which is a non-convex problem. By leveraging the semi-definite relaxation (SDR) technique and Lagrange duality theory, we derive the optimal solution and prove that at most one dedicated sensing beam is needed. Numerical results validate our analysis and effectiveness of the proposed beamforming design.

Cloud computing optimizes service quality and resource efficiency via centralized hardware and computational resources. However, the predominantly centralized deployment and operation of cloud data centers increase the physical distance to end-users, leading to degraded service quality. Edge computing addresses this by offloading data processing and analysis tasks directly to devices at the network edge, reducing reliance on backhaul transmission and thus offering a more responsive solution for latency-sensitive applications. Nevertheless, ensuring that applications meet predefined Service Level Agreement (SLA) in resource-constrained edge environments remains challenging. To tackle these issues, this paper investigates elastic scaling strategies in cloud-edge collaborative settings. We propose an attention-enhanced bidirectional LSTM model (A-Bi-LSTM) for microservice workload prediction, and design an adaptive elastic scaling system named XScale. This system incorporates a fall-back scaling mechanism when predictions are unreliable and introduces a proactive load forwarding strategy to enhance overall edge node performance. Experimental results show that, compared to existing elastic scaling methods, XScale reduces SLA violations by 82.3%, increases average resource utilization by 17.4%, decreases average response time by 21.1%, and improves overall edge node performance by 36.3%.

Feature selection is challenging in high-dimensional and small-sample data, particularly in industrial informatics with diverse noise sources. The information entropy of feature noise is included in mutual information of a label and noise-corrupted features, which can be removed to increase classification accuracy. In this article, we propose a robust feature selection method by eliminating feature noise in the relevance measure. Feature noise is modeled as a zero-mean censored normal distribution, so its entropy is determined by solving the variance equation based on the maximum entropy principle. Then, a noisy channel for feature transmission is proposed to extract class-relevant noise component. Furthermore, a noise-free mutual information metric is developed by removing noise entropy within mutual information. Eventually, a novel criterion is proposed by maximizing relevance based on noise-free mutual information while minimizing redundancy. Experimental results confirm the effectiveness of our approach on datasets from various industrial sectors.

This paper studies an integrated sensing and communication (ISAC) system where a multi-antenna base station (BS) aims to communicate with a single-antenna user in the downlink and sense the unknown and random angle parameter of a target via exploiting its prior distribution information. We consider a general transmit beamforming structure where the BS sends one communication beam and potentially one or multiple dedicated sensing beam(s). Firstly, motivated by the periodic feature of the angle parameter, we derive the periodic posterior Cramer-Rao bound (PCRB) for quantifying a lower bound of the mean-cyclic error (MCE), which is more accurate than the conventional PCRB for bounding the mean-squared error (MSE). Then, note that more sensing beams enable higher flexibility in enhancing the sensing performance, while also generating extra interference to the communication user. To resolve this trade-off, we formulate the transmit beamforming optimization problem to minimize the periodic PCRB subject to a communication rate requirement for the user. Despite the non-convexity of this problem, we derive the optimal solution by leveraging the semi-definite relaxation (SDR) technique and Lagrange duality theory. Moreover, we analytically prove that at most one dedicated sensing beam is needed. Numerical results validate our analysis and the advantage of having a dedicated sensing beam.

In this paper, we study a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system where one multi-antenna base station (BS) sends information to a user with multiple antennas in the downlink and simultaneously senses the location parameter of a target based on its reflected echo signals received back at the BS receive antennas. We focus on the case where the location parameter to be sensed is unknown and random, for which the prior distribution information is available for exploitation. First, we propose to adopt the posterior Cramér-Rao bound (PCRB) as the sensing performance metric with prior information, which quantifies a lower bound of the mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to draw more insights. Moreover, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the CRB averaged over random location realizations without prior information exploitation. Next, we formulate the transmit covariance matrix optimization problem to minimize the sensing PCRB under a communication rate constraint. We obtain the optimal solution and derive useful properties on its rank. Then, by considering the derived PCRB upper bound as the objective function, we propose a low-complexity suboptimal solution in semi-closed form. Numerical results demonstrate the effectiveness of our proposed designs in MIMO ISAC systems exploiting prior information.

In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cramér-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cramer-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.

Using small cell base station (SBS) with unmanned aerial vehicle (UAV) as a carrier becomes a promising solution for areas with high-density mobile users. On the other hand, 5G network would apply the LTE technology into the unlicensed spectrum, named Licensed-assisted Access (LAA), due to the limitation of licensed band. In this paper, we propose to utilize LAA technology into the UAV to expand available transmission band. By focusing on the transmission experience of very important (VIP) users, we propose an enhanced LAA (eLAA) technology, which integrates LAA into the IEEE 802.11e protocol. Under the proposed UAV enabled eLAA network, our goal is to maximize the energy efficiency of the on-board communication device through a joint trajectory design and resource allocation strategy. The proposed nonlinear fractional problem has been solved by the Dinkelbach-type algorithm and the block coordinate descent (BCD) mechanism. Numerical results demonstrate the effectiveness of our proposed scheme.

Technological advances allow perceptual physical objects to be connected to the Internet and share their information over webpages. As a predominant source of public sensor data, automatic discovery of these data is critical and desirable for general Internet of Things search service, which is fundamental to many intelligent applications. However, discovering sensor data from webpages is quite challenging, since there are diverse data presentation modes, and webpage layouts and structures are complicated and heterogeneous. To this end, we explore webpages to discover and collect sensor data under a hierarchical mechanism. In this paper, we first devise novel textual features (TFs) to recognize potential webpages that may contain sensor data; specifically, we construct sensing information corpus to provide keyword reference for the features. Then to get the position of sensor data, we develop granularity adaptive page segmentation (GAPS) algorithm to segment potential webpages into a set of informative blocks; and accordingly, we extract several visual features (VFs) of the blocks so that sensor data can be identified via a block classifier. Based on the novel created sensing information pages dataset, which consists of webpages and manual markings about sensing data, extensive experiments are conducted to evaluate the performance of our exploration methods. Results demonstrate that the TFs achieve supreme performance in sensing data recognition when compared to the state-of-the-art approaches, and GAPS is efficient to locate multimodal sensor data in cooperation with the VFs.