Ali Görçin

Sefa Kayraklik, Ali Fuat Sahin, Onur Salan et al.

The vision of a smart wireless factory (SWF) demands highly flexible, low-latency, and reliable connectivity that goes beyond conventional wireless solutions. Reconfigurable intelligent surface (RIS)-empowered communications, when integrated with the open radio access network (O-RAN) architectures, have emerged as a promising enabler to meet these challenging requirements. This article introduces the methodology for the orchestration of RIS with xApps (ORIX), bringing the RIS technology into the O-RAN ecosystem through xApp-based control for SWF environments. ORIX features three key components: an O-RAN-compliant RIS service model for dynamic configuration, an RIS channel simulator that supports 3GPP indoor factory models with multiple industrial scenarios, and practical RIS optimization strategies with finite-resolution control. Together, these elements provide a realistic end-to-end emulation platform for evaluating RIS placement, control, and performance in SWF environments prior to deployment. The presented case study demonstrates how ORIX enables the evaluation of achievable performance gains, exploration of trade-offs among key RIS design parameters, and identification of deployment strategies that balance system performance with practical implementation constraints. By bridging theoretical advances with industrial feasibility, ORIX lays the groundwork for RIS-assisted O-RAN networks to power next-generation wireless communication in industrial scenarios.

Zehra Yigit, Sefa Kayraklik, Ertugrul Basar et al.

The synergy between integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RISs) unlocks novel applications and advanced services for next-generation wireless networks, yet also introduces new security challenges. In this study, a novel dual target-mounted RISs-assisted ISAC scheme is proposed, where a base station with ISAC capability performs sensing of two unmanned aerial vehicle (UAV) targets, one of which is legitimate and the other is eavesdropper, while communicating with the users through an RIS mounted on the legitimate UAV target. The proposed scheme addresses dual security threats posed by a hostile UAV target: eavesdropping on legitimate user communications and random interference attacks launched by a malicious RIS mounted on this eavesdropper UAV target, aiming to disrupt secure transmissions. Moreover, malicious RIS interference is also optimized for a worst-case scenario, in which both the channel state information (CSI) and the transmit beamforming of the base station are assumed to be fully compromised by a malicious RIS-mounted eavesdropper UAV. A non-convex optimization problem maximizing the secrecy rate of the users is formulated, and a semi-definite relaxation (SDR)-based two-stage solution is developed to optimize the transmit beamforming matrix of the base station and the phase shift coefficients of the legitimate RIS. Extensive computer simulations are conducted to evaluate the robustness of the proposed solution under various system configurations. The proposed system's communication performance is assessed using the secrecy rate metric, while the sensing performance is evaluated through the signal-to-interference-plus-noise ratio and the Cramer-Rao bound (CRB) for angle-of-departure (AoD) estimation of the eavesdropper UAV target.

Erhan Karakoca, Hüseyin Çevik, İbrahim Hökelek et al.

Neural receivers have recently become a popular topic, where the received signals can be directly decoded by data driven mechanisms such as machine learning and deep learning. In this paper, we propose two novel neural network based orthogonal frequency division multiplexing (OFDM) receivers performing channel estimation and equalization tasks and directly predicting log likelihood ratios (LLRs) from the received in phase and quadrature phase (IQ) signals. The first network, the Dual Attention Transformer (DAT), employs a state of the art (SOTA) transformer architecture with an attention mechanism. The second network, the Residual Dual Non Local Attention Network (RDNLA), utilizes a parallel residual architecture with a non local attention block. The bit error rate (BER) and block error rate (BLER) performance of various SOTA neural receiver architectures is compared with our proposed methods across different signal to noise ratio (SNR) levels. The simulation results show that DAT and RDNLA outperform both traditional communication systems and existing neural receiver models.

Sefa Kayraklik, Ibrahim Yildirim, Ertugrul Basar et al.

This paper presents reconfigurable intelligent surface (RIS)-aided deep learning (DL)-based spectrum sensing for next-generation cognitive radios. To that end, the secondary user (SU) monitors the primary transmitter (PT) signal, where the RIS plays a pivotal role in increasing the strength of the PT signal at the SU. The spectrograms of the synthesized dataset, including the 4G LTE and 5G NR signals, are mapped to images utilized for training the state-of-art object detection approaches, namely Detectron2 and YOLOv7. By conducting extensive experiments using a real RIS prototype, we demonstrate that the RIS can consistently and significantly improve the performance of the DL detectors to identify the PT signal type along with its time and frequency utilization. This study also paves the way for optimizing spectrum utilization through RIS-assisted CR application in next-generation wireless communication systems.

Kürşat Tekbıyık, Özkan Akbunar, Ali Rıza Ekti et al.

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured overthe-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.

Kürşat Tekbıyık, Ali Rıza Ekti, Ali Görçin et al.

Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in relation to that when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016.10a is the main dataset utilized for testing and evaluation of the proposed methods. However, the channel effects incorporated in this dataset and some others may lack the appropriate modeling of the real-world conditions since it only considers two distributions for channel models for a single tap configuration. Therefore, in this paper, a more comprehensive dataset, named as HisarMod2019.1, is also introduced, considering real-life applicability. HisarMod2019.1 includes 26 modulation classes passing through the channels with 5 different fading types and several numbers of taps for classification. It is shown that the proposed model performs better than the existing models in terms of both accuracy and training time under more realistic conditions. Even more, surpassed their performance when the RadioML2016.10a dataset is utilized.