Browsing by Author "Panayirci, E."

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Citation - Scopus: 1
Energy-Efficient Secure Design for IOS and AN Aided CF-mMIMO Network
(Institute of Electrical and Electronics Engineers Inc., 2025) Li, B.; Hu, Y.; Dong, Z.; Panayirci, E.; Jiang, H.; Wu, Q.
Intelligent Omni-Surface (IOS) has attracted considerable attention for its advantages of high energy efficiency, which are similar to those of Reconfigurable Intelligent Surface (RIS), while also being able to overcome the limited scope of RIS services. In this paper, we provide a security energy efficiency (SEE) maximization design for IOS and artificial noise (AN) assisted cell-free massive MIMO (CF-mMIMO) networks, via jointly optimizing the transmission beamforming and AN covariance matrix of the AP, the reflection and transmission phase-shift matrices of the IOS, and the reflection-transmission power ratio of the IOS. To handle the formulated problem with non-convexity and high complexity, we first decouple it into two sub-problems. Then, we design low-complexity algorithms for each sub-problem i.e., an AP transmission beamforming and AN noise covariance matrix joint optimization algorithm based on the SSNCG-ALM, and an IOS reflection and transmission phase-shift matrix joint optimization algorithm based on the RPM-TR. Finally, a SEE maximization iterative algorithm based on block coordinate descent and successive convex approximation is established. The simulation results demonstrate that the proposed design significantly enhances the SEE of CF-mMIMO networks. © 2002-2012 IEEE.
High- and Low-Frequency Cooperation Based Resource Allocation in Vehicular Edge Computing Via Deep Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Luo, Q.; Ou, Y.; Zheng, D.; Zhang, J.; Ma, Z.; Panayirci, E.
In vehicular edge computing (VEC) environment, the increasing task offloading requirements from diverse vehicular applications pose significant challenges to the limited and single communication resources. High- and low-frequency cooperation (HL-FC) has the advantages of large capacity, low latency, large coverage capability, and stable communication link during task offloading. However, how to efficiently allocate communication resources for task offloading in the presence of high- and low-frequency communication resources is a challenge. Furthermore, coupled with the allocation of computing resources and the offloading-decision making, the allocation of high- and low-frequency communication resources is even more complex and challenging. To cope with these challenges, in this paper, we investigate the resource allocation scheme under the high- and low-frequency cooperation in VEC. Specifically, to facilitate the processing of latency-sensitive and computation-intensive tasks, a multi-queue model for task caching is first designed to prioritize latency-sensitive workloads, enabling efficient data buffering and processing. Considering vehicle mobility, we then develop the communication model, task migration model, and the computing model. After that, we formulate a long-term average cost optimization problem that jointly optimizes resource expenditure and latency, which is a NP-hard problem. To obtain the optimal strategy, we leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed twin delayed deep deterministic policy gradient (TD3)-based two-phase resource allocation scheme (TTRAS). Finally, extensive simulations are conducted to assess and validate the effectiveness of the TTRAS. © 2025 Elsevier B.V., All rights reserved.
Interference Mitigation in Joint Communication and Sensing: A Precoding-Based Framework With SSK Modulation
(Institute of Electrical and Electronics Engineers Inc., 2025) Hassan, S.; Kazemipourleilabadi, N.; Kahraman, I.; Sadi, Y.; Koca, M.; Panayirci, E.; Poor, H.V.
This paper proposes a joint communication and sensing (JCS) system that integrates multiple-input multiple-output (MIMO) communication and radar functionalities within a shared spectrum. A novel precoder design incorporating Maximal Ratio Combining (MRC) is proposed to eliminate radar-induced and multi-user interference (MUI), ensuring robust communication while maintaining radar sensing accuracy. The communication subsystem leverages spatial shift keying (SSK) to enhance spectral efficiency, while the radar employs a co-located MIMO configuration for precise target detection. Simulation results show that the proposed system achieves a bit error rate (BER) below 10-2 at 20 dB signal-to-noise ratio (SNR) and a radar detection probability exceeding 90% at 5 dB SNR, validating its effectiveness in interference management. This approach enables seamless integration of communication and sensing, making it a promising solution for autonomous driving, smart cities, and next-generation wireless networks. © 2025 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Priority-Based Sensing Strategy for ISAC Systems With Primary and Secondary Targets
(Institute of Electrical and Electronics Engineers Inc., 2025) Du, L.; Ma, Z.; Liang, Q.; Fan, P.; Panayirci, E.
Integrated sensing and communication (ISAC) holds significant commercial potential in future 6G. The 3GPP technical report indicates that it is crucial to prioritize the sensing services of the ISAC system to ensure the efficient execution of critical services due to resource-constrained environments. Thus, this paper investigates priority-based resource allocation in ISAC systems, where an ISAC base station (BS) simultaneously serves a communication user (CU) and detects both a primary sensing target (PST) and a secondary sensing target (SST). First, A priority criteria for this ISAC system is proposed: 1) Above all, to ensure that the Cramer-Rao bound (CRB) for PST is maintained below a threshold; 2) Following this, the CRB of the SST should be optimized, while ensuring the communication spectral efficiency is preserved. Then, based on this criterion, three cases are derived, and for each case, a distinct resource allocation problem is formulated, yielding closed-form or semi-closed-form expressions of the optimal resource allocation for each problem. Furthermore, An algorithm is also proposed to determine the occurrence of each case and provide the corresponding optimal resource allocation. In addition, system performance under each case is analyzed based on these expressions. Finally, the simulation results demonstrate the impact of the priority strategy on performance, which is consistent with the previous performance analysis. © 1972-2012 IEEE.