@jnu.edu.cn
School of Intelligent Systems Science and Engineering
Jinan University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Haoxuan Liu, Nan Qi, Kewei Wang, Theodoros A. Tsiftsis, Wenjing Wang, and Yawen Liu
Elsevier BV
Leila Tlebaldiyeva, Sultangali Arzykulov, Theodoros A. Tsiftsis, and Galymzhan Nauryzbayev
Elsevier BV
Nan Qi, Zhe Su, Wen-Jing Wang, Rugui Yao, and Theodoros A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Kefeng Guo, Haifeng Shuai, Xingwang Li, Liang Yang, Theodoros A. Tsiftsis, Arumugam Nallanathan, and Min Wu
Institute of Electrical and Electronics Engineers (IEEE)
Nikolaos I. Miridakis, Theodoros A. Tsiftsis, Panagiotis A. Karkazis, Helen C. Leligou, and Fotis Foukalas
Institute of Electrical and Electronics Engineers (IEEE)
Hongjiang Lei, Fangtao Yang, Hongwu Liu, Imran Shafique Ansari, Kyeong Jin Kim, and Theodoros A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Miaomiao Zhu, Kefeng Guo, Yinghui Ye, Liang Yang, Theodoros A. Tsiftsis, and Hongwu Liu
Institute of Electrical and Electronics Engineers (IEEE)
Tong Li, Rugui Yao, Ye Fan, Xiaoya Zuo, N. I. Miridakis, and T. A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Zhiqiang Liu, Di Zhang, Jingjing Guo, Theodoros A. Tsiftsis, Yuwei Su, Battulga Davaasambuu, Sahil Garg, and Takuro Sato
Institute of Electrical and Electronics Engineers (IEEE)
Rugui Yao, Yongsong Yu, Peng Wang, Ye Fan, Xudong Li, Xiaoya Zuo, Nan Qi, Nikolaos I. Miridakis, and Theodoros A. Tsiftsis
Institution of Engineering and Technology (IET)
Hongjiang Lei, Fangtao Yang, Imran Shafique Ansari, Hongwu Liu, Kyeong Jin Kim, and Theodoros A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Liang Yang, Wei Zhang, Petros S. Bithas, Hongwu Liu, Mazen O. Hasna, Theodoros A. Tsiftsis, and Derrick Wing Kwan Ng
Institute of Electrical and Electronics Engineers (IEEE)
Qiang Sun, Hongwu Liu, Shen Yan, Theodoros A. Tsiftsis, and Jinhong Yuan
Institute of Electrical and Electronics Engineers (IEEE)
Peixu Liu, Gang Jing, Hongwu Liu, Liang Yang, and Theodoros A. Tsiftsis
Elsevier BV
Pengxu Chen, Hongwu Liu, Yinghui Ye, Liang Yang, Kyeong Jin Kim, and Theodoros A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Xingwang Li, Qunshu Wang, Ming Zeng, Yuanwei Liu, Shuping Dang, Theodoros A. Tsiftsis, and Octavia A. Dobre
Institute of Electrical and Electronics Engineers (IEEE)
Haifeng Shuai, Kefeng Guo, Kang An, Yuzhen Huang, Theodoros A. Tsiftsis, and Shibing Zhu
Institute of Electrical and Electronics Engineers (IEEE)
Muthukrishnan Senthil Kumar, Aresh Dadlani, Masoumeh Moradian, Ahmad Khonsari, and Theodoros A. Tsiftsis
Institute of Electrical and Electronics Engineers (IEEE)
Jiawen Tian, Wenwen Guan, Xiao Li, Theodoros A. Tsiftsis, and Hongwu Liu
IEEE
Due to dynamic change of the wireless environment, devising the local phase shift matrix for reconfigurable intelligent surface (RIS) can be a thought-provoking task. Therefore, in this paper, we come up with a deep reinforcement learning (DRL)-based beamforming optimization algorithm for RIS-aided multiple-input single-output communication systems. To be precise, we exploit a DRL-based structure based on twin-delayed-deep-deterministic-policy-gradient (TD3) algorithm to automatically adjust the phase shift of each unit at RIS to maximize the downlink received signal-to-noise ratio (SNR). Simulation results show that the raised TD3 algorithm performs a higher received SNR than the conventional DRL algorithm with a reduced running time.
Xueyu Kang, Hongjiang Lei, Liang Yang, Gaofeng Pan, Theodoros A. Tsiftsis, and Hongwu Liu
Institute of Electrical and Electronics Engineers (IEEE)
Kefeng Guo, Haifeng Shuai, Kang An, Fuhui Zhou, Theodoros A. Tsiftsis, Xingwang Li, and Min Wu
Institute of Electrical and Electronics Engineers (IEEE)
Sravani Kurma, Keshav Singh, Prabhat Kumar Sharma, Chih-Peng Li, and Theodoros A. Tsiftsis
IEEE
The cell-free massive multiple-input and multiple-output (CF-mMIMO) communication technology has the ability to handle inter-cell interference in MIMO systems, making it a potential candidate for sixth-generation (6G) wireless communication. A CF-mMIMO system is investigated in this paper for mission-critical ultra-reliable low latency communication (URLLC) applications involving a central processing unit (CPU), many distributed access points (APs), each with multiple antennas, and multiple single-antenna user equipment (UEs). In order to maximize energy efficiency (EE) and throughput gains, each AP is linked to the CPU through a fronthaul link with limited capacity, which handles the quantized uplink data to the CPU. We assume that each AP serves fewer UEs. Our approach has a minimal signal processing complexity and offers UEs uniform quality of service (QoS) as well as improved EE. Closed-form expression for outage probability (OP) in the uplink of the CF-mMIMO system considering Welch-Satterthwaite approximation is derived using a variety of Doppler power spectra (DPS) models that consider imperfect channel state information (CSI) and mobility of UEs. Our numerical simulations validate the correctness of the derived expressions.
Muthukrishnan Senthil Kumar, Aresh Dadlani, Masoumeh Moradian, Behrouz Maham, and Theodoros A. Tsiftsis
IEEE
Concurrent with the rise of real-time wireless systems enabled by the Internet of Things, the age of information (AoI) has been widely perceived as a crucial destination-centric performance metric to quantify the timeliness of data delivery. This paper deals with the analysis of information freshness in a multi-source M/G/1 queueing system with utilization of idle server time, referred to as server vacation, in an effective manner. In particular, the status update packets in our model are generated independently by a finite set of source nodes and according to a Poisson process, while their service time follows a general random variable. Using stochastic decomposition and the Laplace-Stieltjes transform, we derive the average AoI (AAoI) expression for the multi-source M/G/1 queueing model with generally-distributed vacation time in closed form. Our numerical simulations validate the accuracy of the derived AAoI expression and assess the impact of different parameters on the system performance.
Yuncong Li, Yingyang Chen, Miaowen Wen, Duoying Zhang, Bingli Jiao, Zhiguo Ding, Theodoros A. Tsiftsis, and H. Vincent Poor
IEEE
In this paper, we propose a multi-cell full-duplex (FD) networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. We aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and the phase shift matrix at RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between SR and minimum mean square error. The optimal solutions of TPC matrices are obtained in closed form, while a successive convex approximation-based algorithm is developed to resolve the phase shift matrix suboptimally. Simulation results show that introducing an RIS into the FD networking system can improve the overall SR significantly. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for self-interference cancellation (SIC) and the number of BS antennas effectively, especially with enough reflecting elements. As a result, the utilization of RIS enables the originally cumbersome FD networking system to become efficient and practical.
Christos G. Tsinos, Theodoros A. Tsiftsis, and Robert Schober
IEEE
In this paper, a radio-frequency (RF) domain symbol level precoding technique is developed for reconfigurable intelligent surface (RIS)-assisted downlink multiuser multiple-input single-output (MU-MISO) systems. We study a system with a base station (BS) employing an analog architecture formed by a phase shifting network which serves a number of single antenna users with the help of a RIS. Such an architecture facilitates significant reductions in power consumption and hardware complexity. The objective of this paper is to jointly derive the optimal RF precoder, RIS reflection matrix and receive processing coefficients, subject to constraints on the BS analog architecture, the total transmit power, and the structure of the RIS reflection matrix. To that end, a difficult nonconvex optimization problem is formulated and solved. An efficient algorithmic solution is developed for the considered problem. Numerical results show that the derived solution offers significant energy efficiency gains when compared to non-RIS-assisted approaches.