2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 20623-20638· 0 citations· 60 references
Computer Science
TL;DR
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.
Abstract
Integrated sensing, communication, and computation (ISCC) enables next-generation wireless networks to perform environmental perception while processing massive data under stringent quality-of-service (QoS) requirements. Energy consumption is a crucial indicator for the ISCC system design. However, accounting for energy heterogeneity in ISCC system design is an open problem. Specifically, battery-constrained user equipments (UEs) and energy-abundant access points (APs) require fundamentally different energy allocation strategies based on device computational capabilities, battery states, and QoS constraints. In this paper, we introduce a nonconvex energy cost minimization problem by considering a user-specific energy cost ratio coefficient that explicitly balances UE-AP energy consumption according to heterogeneous device energy states. To efficiently address this problem, a double-loop framework combining successive convex approximation and alternating direction method of multipliers is also developed. Numerical results demonstrate that the proposed scheme significantly outperforms the fixed offloading baselines (full offloading, full local and half offloading) in terms of the total energy cost. In particular, the proposed scheme achieves up to $25-47.6\%$ energy cost reduction at moderate latency constraints over fixed offloading baselines, thereby supporting time-sensitive applications. Moreover, this work provides an effective solution for energy-efficient and QoS-aware 6G ISCC systems serving diverse devices with conflicting energy priorities.
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.
Yingsheng Peng, Jinbei Zhang, Jingpu Duan et al.· IEEE Transactions on Green C...· 0 citations
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
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
K. A. Bonsu, Ebenezer Baidoo Baidoo Bediako, K. Darkwah et al.· Applied Mathematics and Stat...· 0 citations
Reconfigurable intelligent surface (RIS)-assisted wireless-powered communication networks (WPCNs) introduce a new degree of freedom: the same passive beamforming array can concentrate the downlink energy toward harvesting devices and simultaneously shape the uplink interference environment. In this paper, we study a system where the base station (BS) waveform serves the dual role of wireless energy transfer (WET) and passive target sensing. Using the position error bound (PEB) derived from the equivalent Fisher information matrix (EFIM) as the sensing quality metric, we formulate a joint resource allocation problem that maximizes weighted uplink sum-rate subject to a PEB constraint, energy-causality, block-time sharing, and unit-modulus RIS phase constraints. Focusing on the practically important WET-only sensing case, we show that the problem separates into four tractable subproblems and propose a block coordinate descent (BCD) algorithm: (i) closed-form water-filling for WIT time-power allocation, (ii) semidefinite relaxation (SDR) with Dinkelbach iterations for per-slot WIT-RIS beamforming, (iii) golden-section search for the optimal WET duration, and (iv) a convex SDP for WET-RIS optimization under a PEB constraint. The BCD iterates converge monotonically. Simulations confirm a fundamental rate–sensing tradeoff and demonstrate significant gains from joint RIS-assisted optimization.
Yongjie Li, Jing Shen, Jizhao Lu et al.· 2026 8th International Confe...· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
A network energy-efficiency (EE) maximization framework for the uplink of acell-free massive MIMO with wireless fronthaul, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division and frequency-division operating modes in a unified manner.
Özlem Tuğfe Demir· 0 citations
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