Evidence map›Paper›PMID 42655611›Full record

ArticleSensors (Basel, Switzerland)2026

Optimization Scheme for Hybrid RIS-Assisted ISAC System for Controllable Communication and Sensing.

Zhishuo Deng, Bo Li, Hehang Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhishuo DengSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Bo LiSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Hehang WangSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.

Funding

Educational Department of Liaoning Province JYTMS20230862
6 · The paper itself

Abstract

Integrated sensing and communication (ISAC) systems are envisioned as a key enabler for next-generation wireless networks. To simultaneously achieve multi-function of communication and sensing for this system, a hybrid reconfigurable intelligent surface (RIS) comprising both active and passive reflecting elements is proposed. An index-wise importance score matrix and a factorized representation of complex reflection coefficients are optimized by introducing a communication-sensing controllable coefficient. A novel ISAC system is investigated, which exploits the low-power advantage of passive reflecting elements while retaining the signal amplification capability of active reflecting elements. In the multiple-input multiple-output (MIMO) communication networks, the RIS reflection coefficient matrix is adaptively optimized via a Riemannian Hessian-based method. At the same time, the transmit precoding matrix is obtained using an extended weighted minimum mean square error (WMMSE) and Lagrange multiplier methods. Compared with baseline schemes including active-only RIS, passive-only RIS, random-phase RIS, and non-RIS, the proposed scheme enables multi-mode adjustability of communication and sensing which can be easily transplanted in current ISAC system. Under the constraint of transmit power, it exhibits more robust performance on communication-sensing with varying number of RIS elements and different signal-to-noise ratios (SNRs).

Indexed as

communication-sensing controllable coefficienthybrid reconfigurable intelligent surfaceintegrated sensing and communicationmultiple-input multiple-outputprecoding matrixRiemannian Hessian-based method

Identifiers

PMID42655611
PMCPMC13517284

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.