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Researcher profile

Yue Xiu

Yue Xiu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPArtificial IntelligenceComputation and Language

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Published work

6 published item(s)

preprint2026arXiv

MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory

Agentic memory evolves across tasks into durable derived artifacts: summaries, cached outputs, embeddings, learned skills, and executable tool procedures. When a source artifact is deleted, corrected, or invalidated by tool or API migration, descendants derived from that source can remain visible and steer future actions with stale support. We formalize this failure mode as the cascade update problem, where repair targets the visible derived state of the memory store. We present MemoRepair, a barrier-first cascade-repair contract for agentic memory. A repair event induces a controlled transition from invalidated descendant state to validated successor state: affected descendants are withdrawn before repair, successors are constructed from retained support and staged repaired predecessors under the current interface, and republication is restricted to validated predecessor-closed successors. This contract induces a scalarized repair-selection problem for a fixed repair-cost tradeoff. We show that the induced publication problem reduces to maximum-weight predecessor closure and can be solved exactly by a single s-t min-cut. Experiments on ToolBench and MemoryArena show that, with complete influence provenance, MemoRepair reduces invalidated-memory exposure from 69.8-94.3% under systems without cascade repair to 0%. Compared with exhaustive Repair all, it recovers 91.1-94.3% of validated successors while reducing normalized repair-operator cost from 1.00 to 0.57-0.76.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

CRB Minimization for RIS-aided mmWave Integrated Sensing and Communications

In this paper, reconfigurable intelligent surface (RIS) is employed in a millimeter wave (mmWave) integrated sensing and communications (ISAC) system. To alleviate the multi-hop attenuation, the semi-self sensing RIS approach is adopted, wherein sensors are configured at the RIS to receive the radar echo signal. Focusing on the estimation accuracy, the Cramer-Rao bound (CRB) for estimating the direction-of-the-angles is derived as the metric for sensing performance. A joint optimization problem on hybrid beamforming and RIS phaseshifts is proposed to minimize the CRB, while maintaining satisfactory communication performance evaluated by the achievable data rate. The CRB minimization problem is first transformed as a more tractable form based on Fisher information matrix (FIM). To solve the complex non-convex problem, a double layer loop algorithm is proposed based on penalty concave-convex procedure (penalty-CCCP) and block coordinate descent (BCD) method with two sub-problems. Successive convex approximation (SCA) algorithm and second order cone (SOC) constraints are employed to tackle the non-convexity in the hybrid beamforming optimization. To optimize the unit modulus constrained analog beamforming and phase shifts, manifold optimization (MO) is adopted. Finally, the numerical results verify the effectiveness of the proposed CRB minimization algorithm, and show the performance improvement compared with other baselines. Additionally, the proposed hybrid beamforming algorithm can achieve approximately 96% of the sensing performance exhibited by the full digital approach within only a limited number of radio frequency (RF) chains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Weighted Sum Age of Information Minimization in Wireless Networks with Aerial IRS

In this letter, we analyze a terrestrial wireless communication network assisted by an aerial intelligent reflecting surface (IRS). We consider a packet scheduling problem at the ground base station (BS) aimed at improving the information freshness by selecting packets based on their AoI. To further improve the communication quality, the trajectory of the unmanned aerial vehicle (UAV) which carries the IRS is optimized with joint active and passive beamforming design. To solve the formulated non-convex problem, we propose an iterative alternating optimization problem based on a successive convex approximation (SCA) algorithm. The simulation results shows significant performance improvement in terms of weighted sum AoI, and the SCA solution converges quickly with low computational complexity.

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preprint2021arXiv

Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC

In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations. Compared to the RIS-aided systems in most existing works, we consider the case of the RIS-aided mmWave system with low-resolution digital-to-analog converters (LDACs). We formulate a secrecy rate maximization problem hardware constraints. Then by optimizing the RIS phase shift and the transmit beamforming to maximize the secrecy rate. Due to the nonconvexity of the problem, the formulated problem is intractable. To handle the problem, an alternating optimization (AO)-based algorithm is proposed. Specifically, we first use the successive convex approximation (SCA) method to obtain the transmit beamforming. Then the element-wise block coordinate descent (BCD) method is used to obtain the RIS phase shift. Numerical results demonstrate that the RIS can mitigate the hardware loss, and the proposed AO-based algorithm with low complexity outperformances the baselines.

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preprint2021arXiv

Sum-Rate Maximization in Distributed Intelligent Reflecting Surfaces-Aided mmWave Communications

In this paper, we focus on the sum-rate optimization in a multi-user millimeter-wave (mmWave) system with distributed intelligent reflecting surfaces (D-IRSs), where a base station (BS) communicates with users via multiple IRSs. The BS transmit beamforming, IRS switch vector, and phase shifts of the IRS are jointly optimized to maximize the sum-rate under minimum user rate, unit-modulus, and transmit power constraints. To solve the resulting non-convex optimization problem, we develop an efficient alternating optimization (AO) algorithm. Specifically, the non-convex problem is converted into three subproblems, which are solved alternatively. The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector. The complexity of the proposed AO algorithm is analyzed theoretically. Numerical results show that the D-IRSs-aided scheme can significantly improve the sum-rate and energy efficiency performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design

Intelligent reflecting surface (IRS) technology offers more feasible propagation paths for millimeter-wave (mmWave) communication systems to overcome blockage than existing technologies. In this paper, we consider a downlink wireless system with the IRS and formulate a joint power allocation and beamforming design problem to maximize the weighted sum-rate, which is a multi-variable optimization problem. To solve the problem, we propose a novel alternating manifold optimization based beamforming algorithm. Simulation results show that our proposed optimization algorithm outperforms existing algorithms significantly in improving the weighted sum-rate of the wireless communication system.

0 citations0 reviewsNo code linkedOpen access