2026· IEEE Transactions on Green Communications and Networking· Vol 10, pp. 3929-3940· 0 citations· 48 references
Computer Science
Abstract
Integrated sensing, communication, and computing (ISCC) technology significantly improves spectrum efficiency and reduces hardware costs by unifying the functionalities of sensing, communication, and computation. However, the degradation of wireless link quality caused by obstacles may lead to severe offloading latency. This paper investigates the application of reconfigurable intelligent surface (RIS) technology in ISCC network to enhance the reliability of wireless links and improve the efficiency of computation offloading. In the proposed network, integrated sensing and communication (ISAC) devices employ the same hardware and signaling mechanisms to perform both sensing and communication tasks, while mobile edge computing (MEC) technology is leveraged to process sensing data. To address the cross-layer resource management problem, we formulate a total latency minimization problem for both communication and computation under sensing accuracy constraints, by jointly optimizing the waveform precoding design matrix of ISAC devices, the beam pattern scaling factor, the RIS’s reflective beamforming, and the computing frequency of edge server (ES). Since this problem is highly non-convex, we propose an iterative algorithm based on block coordinate descent (BCD) framework, which leverages semi-definite relaxation (SDR) method, the Charnes-Cooper transform (CCT) method, and closed-form solution to alternately optimize three variable blocks until convergence is achieved for the final solution. Extensive simulations validate the effectiveness of the proposed scheme, demonstrating that the RIS-assisted joint optimization scheme significantly reduces the total system latency. Moreover, we reveal the trade-off between sensing accuracy and system latency.
This paper investigates an integrated sensing, communication, and computing network enabled by joint beamforming and cooperative device-to-device (D2D) and mobile edge computing (MEC) offloading. In the considered system, multiple full-duplex integrated sensing and communication devices perform target sensing while offloading partial latency-sensitive computation tasks through D2D and MEC links, thereby exploiting the complementary advantages of short-range D2D communication and the abundant computing resources of the MEC server. A D2D and MEC co-offloading framework is developed that simultaneously optimizes transceiver beamforming, task assignment, and computation resource allocation, with the aim of minimizing overall computation latency while ensuring sensing performance. To address the intractable issue of the formulated optimization problem, closed-form solutions of receive beamformers are first derived by solving minimum variance distortionless response problems. Subsequently, an alternating optimization algorithm is employed to decouple the original problem into two subproblems. To address their non-convexity, we apply semi-definite relaxation and successive convex approximation to transform each subproblem into a convex form and integrate penalty terms into the objective functions to promote rank-one solutions. Numerical results demonstrate that, compared to the state-of-the-art schemes, the proposed scheme achieves significant latency reduction under limited power and computing resources, while maintaining a high sensing signal-to-interference-plus-noise ratio.
Tao Jiang, Ming Jin, Qinghua Guo et al.· IEEE Transactions on Wireles...· 0 citations
A nonconvex energy cost minimization problem is introduced by considering a user-specific energy cost ratio coefficient that explicitly balances UE-AP energy consumption according to heterogeneous device energy states and a double-loop framework combining successive convex approximation and alternating direction method of multipliers is developed.
Kai Dong, Lei Wang, S. Vorobyov et al.· IEEE Transactions on Wireles...· 0 citations
Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leveraged to enable an ISAC system. Under the considered system model, we investigate transmit beamforming design for the TRIS transceiver to maximize the sum-rate/beampattern gain, subject to the predefined sensing beampattern gain/communication rate thresholds and the per-unit power constraints of the TRIS transceiver. Since the objective functions and constraints are non-convex, the above two optimization problems are highly challenging. To resolve the difficult optimization problems, we combine the fractional programming (FP) method and the majorization-minimization (MM) framework to develop second-order cone programming (SOCP)-based solutions. Since the per-element power constraints introduce a large number of constraints, this increases the complexity of solving the optimization problems. By splitting the coupling constraints and applying the alternating direction method of multipliers (ADMM) framework, we propose two analytic-based algorithms for efficiently updating the beamformer configurations in the sum-rate and beampattern gain maximization problems, respectively. Simulation results demonstrate the convergence and effectiveness of the proposed algorithms, and show that the low-complexity algorithms achieve performance close to the SOCP-based benchmarks with substantially reduced computational complexity.
This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the benchmark schemes.
Peng Liu, Zesong Fei, Xinyi Wang et al.· 0 citations
This letter investigates the sensing-centric design of reconfigurable intelligent surface (RIS)-enabled rate-splitting multiple access-integrated sensing and communication (RSMA-ISAC) systems. Specifically, we propose a new beam-gain approximation method to enhance the sensing beam gain while satisfying communication quality-of-service (QoS) constraints. Since the joint optimization of the beamforming vectors and RIS phase shifts is highly coupled and non-convex, existing methods typically rely on generic optimization solvers involving substantial computational complexity. To address this issue, we propose an efficient constraints-separation-based alternating optimization algorithm (CS-AO). Our proposed algorithm effectively decouples the optimization variables and yields closed-form solutions for all subproblems, thereby significantly reducing the computational burden. Simulation results show that the proposed algorithm achieves sensing beam-gain performance comparable to successive convex approximation (SCA) and semidefinite relaxation (SDR) benchmarks, while achieving more than 120-fold and 50-fold runtime reductions. In addition, compared with conventional space-division multiple access (SDMA) schemes, the proposed design exhibits substantial sensing beam gain.
Xue-Jun Cheng, Qian Zhang, Y. Jiao et al.· IEEE Wireless Communications...· 0 citations
In this paper, we investigate the covert communication performance and sensing performance of integrated sensing and communication (ISAC) systems enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). A novel non-orthogonal multiple access (NOMA) enabled covert framework is proposed, where the covert transmission can be enhanced by eliminating the interference of public signals and sensing signals at the covert user. The large system analytic estimation is employed to effectively decouple the correlation of the warden’s channel fading gains and derive a closed-form expression for the minimum average detection error probability of the warden. Both sensing and covert rate optimization problems are investigated through jointly designing base station transmit beamforming and STAR-RIS passive beamforming. To optimize sensing performance, we aim at minimizing the Cramér-Rao bound (CRB) while satisfying the covert rate requirement. Conversely, when maximizing the covert rate, the CRB is incorporated as a constraint in the sensing. To address these challenging optimization problems, an iterative algorithm based on penalty methods and semidefinite programming are proposed to obtain the transmit beamforming and the beamforming of STAR-RIS. Simulation results indicate that the CRB and covert rate of the proposed ISAC systems, assisted by STAR-RIS and NOMA, outperform the ISAC systems enhanced by orthogonal multiple access and conventional RIS.
Zheng Yang, Haoyang Li, Gaojie Chen et al.· IEEE Transactions on Wireles...· 0 citations
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