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Improving the Ranging Performance of Random ISAC Signals Through Pulse Shaping Design
May 07, 2024
In this paper, we propose a novel pulse shaping design for single-carrier integrated sensing and communication (ISAC) transmission. Due to the communication information embedded in the ISAC signal, the resulting auto-correlation function (ACF) is determined by both the information-conveying random symbol sequence and the signaling pulse, where the former leads to random fluctuations in the sidelobes of the ACF, impairing the range estimation performance. To overcome this challenge, we first analyze the statistical characteristics of the random ACF under the symbol-wise pulse shaping (SWPS) regime. As a step further, we formulate an optimization problem to design ISAC pulse shaping filters, which minimizes the average integrated sidelobe level ratio (ISLR) while meeting the Nyquist criterion, subject to power and bandwidth constraints. We then show that the problem can be recast as a convex quadratic program by expressing it in the frequency domain, which can be readily solved through standard tools. Numerical results demonstrate that the proposed pulse shaping design achieves substantial ranging sidelobe reduction compared to the celebrated root-raised cosine (RRC) pulse shaping, given that the communication throughput is unchanged.
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Apr 28, 2024In recommender systems, multi-behavior methods have demonstrated their effectiveness in mitigating issues like data sparsity, a common challenge in traditional single-behavior recommendation approaches. These methods typically infer user preferences from various auxiliary behaviors and apply them to the target behavior for recommendations. However, this direct transfer can introduce noise to the target behavior in recommendation, due to variations in user attention across different behaviors. To address this issue, this paper introduces a novel approach, Behavior-Contextualized Item Preference Modeling (BCIPM), for multi-behavior recommendation. Our proposed Behavior-Contextualized Item Preference Network discerns and learns users' specific item preferences within each behavior. It then considers only those preferences relevant to the target behavior for final recommendations, significantly reducing noise from auxiliary behaviors. These auxiliary behaviors are utilized solely for training the network parameters, thereby refining the learning process without compromising the accuracy of the target behavior recommendations. To further enhance the effectiveness of BCIPM, we adopt a strategy of pre-training the initial embeddings. This step is crucial for enriching the item-aware preferences, particularly in scenarios where data related to the target behavior is sparse. Comprehensive experiments conducted on four real-world datasets demonstrate BCIPM's superior performance compared to several leading state-of-the-art models, validating the robustness and efficiency of our proposed approach.
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Apr 17, 2024Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation on benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN's ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.
Cluster-based Graph Collaborative Filtering
Apr 16, 2024Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. Thus, the noisy information from unreliable neighbor nodes (e.g., users with dissimilar interests) negatively impacts the representation learning of the target node. Additionally, conducting graph convolution operations without differentiating high-order neighbors suffers the over-smoothing issue when stacking more layers, resulting in performance degradation. In this paper, we aim to capture more valuable information from high-order neighboring nodes while avoiding noise for better representation learning of the target node. To achieve this goal, we propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF). This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them. Specifically, we design an unsupervised and optimizable soft node clustering approach to classify user and item nodes into multiple clusters. Based on the soft node clustering results and the topology of the user-item interaction graph, we assign the nodes with probabilities for different clusters to construct the cluster-specific graphs. To evaluate the effectiveness of ClusterGCF, we conducted extensive experiments on four publicly available datasets. Experimental results demonstrate that our model can significantly improve recommendation performance.
Fundamental Limits of Communication-Assisted Sensing in ISAC Systems
Apr 12, 2024Integrated sensing and communication (ISAC) system stands out as a pivotal usage scenario of 6G. To explore the coordination gains offered by the ISAC technique, this paper introduces a novel communication-assisted sensing (CAS) system. The CAS system can endow users with beyond-line-of-sight sensing capability, wherein the base station with favorable visibility senses device-free targets, simultaneously transmitting the acquired sensory information to users. Within the CAS framework, we characterize the fundamental limits to reveal the achievable distortion between the state of the targets of interest and their reconstruction at the users' end. Finally, within the confines of this theoretical framework, we employ a typical application as an illustrative example to demonstrate the minimization of distortion through dual-functional waveform design, showcasing the potential of CAS in enhancing sensing capabilities.
Waveform Design for Joint Communication and SAR Imaging Under Random Signaling
Mar 26, 2024
Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios. In this letter, we propose a joint communication and SAR imaging (JCASAR) system based on orthogonal frequency-division multiplexing (OFDM) signal with cyclic prefix (CP), which is capable of reconstructing the target profile while serving a communication user. In contrast to traditional matched filters, we propose a least squares (LS) estimator for range profiling. Then the SAR image is obtained followed by range cell migration correction (RCMC) and azimuth processing. By minimizing the mean squared error (MSE) of the proposed LS estimator, we investigate the optimal waveform design for SAR imaging, and JCASAR under random signaling, where power allocation strategies are conceived for Gaussian-distributed ISAC signals, in an effort to strike a flexible performance tradeoff between the communication and SAR imaging tasks. Numerical results are provided to validate the effectiveness of the proposed ISAC waveform design for JCASAR systems.
Task-Based Quantizer Design for Sensing With Random Signals
Mar 17, 2024
In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive multiple-input-multiple-out (MIMO) communication systems, sensing in ISAC systems needs to deal with random nonorthogonal transmitted signals rather than a fixed orthogonal pilot. Considering sensing performance and hardware implementation, we focus on task-based hardware-limited quantization with spatial analog combining. We propose two strategies of quantizer optimization, i.e., data-dependent (DD) and data-independent (DI). The former achieves optimized sensing performance with high implementation overhead. To reduce hardware complexity, the latter optimizes the quantizer with respect to the random signal from a stochastic perspective. We derive the optimal quantizers for both strategies and formulate an algorithm based on sample average approximation (SAA) to solve the optimization in the DI strategy. Numerical results show that the optimized quantizers outperform digital-only quantizers in terms of sensing performance. Additionally, the DI strategy, despite its lower computational complexity compared to the DD strategy, achieves near-optimal sensing performance.
Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives
Mar 05, 2024
With the mobile communication system evolving into 6th-generation (6G), the Internet of Everything (IoE) is becoming reality, which connects human, big data and intelligent machines to support the intelligent decision making, reconfiguring the traditional industries and human life. The applications of IoE require not only pure communication capability, but also high-accuracy and large-scale sensing capability. With the emerging integrated sensing and communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach to satisfy the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the cooperation at the resource-level is performed to improve sensing accuracy by fusing the sensing information carried in the time-frequency-space-code multi-domain resources. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes including Base Station (BS), User Equipment (UE), and Reconfigurable Intelligence Surface (RIS), etc. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE.
Sensing Mutual Information with Random Signals in Gaussian Channels: Bridging Sensing and Communication Metrics
Feb 06, 2024Sensing performance is typically evaluated by classical radar metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the performance metric for sensing and communication, where mutual information (MI) was proposed as a sensing performance metric with deterministic signals. However, the need of communication in ISAC systems necessitates the transmission of random signals for sensing applications, whereas an explicit evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper aims to fill the research gap and investigate the unification of sensing and communication performance metrics. For that purpose, we first derive the explicit expression for the SMI with random signals utilizing random matrix theory. On top of that, we further build up the connections between SMI and traditional sensing metrics, such as ergodic minimum mean square error (EMMSE), ergodic linear minimum mean square error (ELMMSE), and ergodic Bayesian Cram\'{e}r-Rao bound (EBCRB). Such connections open up the opportunity to unify sensing and communication performance metrics, which facilitates the analysis and design for ISAC systems. Finally, SMI is utilized to optimize the precoder for both sensing-only and ISAC applications. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed precoding designs.
Secure ISAC MIMO Systems: Exploiting Interference With Bayesian Cramér-Rao Bound Optimization
Jan 30, 2024In this paper, we present a signaling design for secure integrated sensing and communication (ISAC) systems comprising a dual-functional multi-input multi-output (MIMO) base station (BS) that simultaneously communicates with multiple users while detecting targets present in their vicinity, which are regarded as potential eavesdroppers. In particular, assuming that the distribution of each parameter to be estimated is known \textit{a priori}, we focus on optimizing the targets' sensing performance. To this end, we derive and minimize the Bayesian Cram\'er-Rao bound (BCRB), while ensuring certain communication quality of service (QoS) by exploiting constructive interference (CI). The latter scheme enforces that the received signals at the eavesdropping targets fall into the destructive region of the signal constellation, to deteriorate their decoding probability, thus enhancing the ISAC's system physical-layer security (PLS) capability. To tackle the nonconvexity of the formulated problem, a tailored successive convex approximation method is proposed for its efficient solution. Our extensive numerical results verify the effectiveness of the proposed secure ISAC design showing that the proposed algorithm outperforms block-level precoding techniques.