Browsing by Author "Wu, Qiang"

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    Energy-Efficient Design for Ris-Aided Cell-Free Ultra Dense Hetnets
    (Ieee, 2023) Li, Bin; Panayırcı, Erdal; Hu, Yulin; Dong, Zhicheng; Panayirci, Erdal; Jiang, Huilin; Wu, Qiang
    In this article, we investigate the energy efficiency of reconfigurable intelligent surfaces (RISs) aided full-duplex cellfree ultra dense hetNets (CFUDN), which has the advantages of both cell-free massive MIMO (CF-MMIMO) and ultra-dense hetNets (UDN). To maximize the EE of full-duplex CFUDN, users association and clustering, RISs subsurface associations are carefully designed. Then, the phase shift matrix of RISs and transmission power of base stations are jointly optimized. Due to the non-convexity and high complexity of formulated problem, it is extremely difficult to solve this problem. At present, the block coordinate descent (BCD) algorithm is the most commonly used method for joint optimization problems. However, as we all know, the BCD algorithm has some degree of performance loss due to alternate optimization. To overcome this challenging issue, a novel joint optimization framework based on Riemannian product manifolds (RPM) is proposed.
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    Energy-Efficient Secure Communication for Ios Aided Cfmmimo Network
    (Ieee, 2024) Li, Bin; Panayırcı, Erdal; Hu, Yulin; Dong, Zhicheng; Panayirci, Erdal; Jiang, Huilin; Wu, Qiang
    In this article, we investigate the security energy efficiency (SEE) of intelligent omni-surface (IOS) aided cell-free massive MIMO (CFMMIMO) networks. Firstly, we provide a SEE maximization design for the IOS-assisted CFMMIMO network. To address the formulated non-convex, multivariate problem, in particular, we first decouple the problem into two sub-problems, and design corresponding low-complexity algorithms for each sub-problem, including the joint optimization algorithm of the access point (AP) transmission beamforming and artificial noise covariance matrix based on semi-smooth Newton method (SSNM) as well as the joint optimization algorithm of IOS reflection-transmission phase shift matrix based on Riemannian product manifolds-conjugate gradient method (RPM-CG). The two subproblems are then iterated iteratively using the Block Coordinate Descent (BCD) algorithm to obtain the maximum SEE of the IOS-assisted CFMMIMO network. Simulation results show that the proposed algorithm outperforms the five baseline schemes in terms of SEE.
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    Citation - WoS: 8
    Citation - Scopus: 11
    An Improved Adaptive Subspace Tracking Algorithm Based on Approximated Power Iteration
    (IEEE-INST Electrical Electronics Engineers Inc, 2018) Panayırcı, Erdal; Zheng, Jian-Sheng; Dong, Zhicheng; Panayırcı, Erdal; Wu, Zhi-Qiang; Ren, Qingnuobu
    A subspace tracking technique has drawn a lot of attentions due to its wide applications. The main objective of this approach is to estimate signal or noise subspace basis for the sample covariance matrix. In this paper we focus on providing a fast stable and adaptive subspace tracking algorithm that is implemented with low computational complexity. An alternative realization of the fast approximate power iteration (FAPI) method termed modified FAPI (MFAPI) is also presented. Rather than solving an inverse square root of a matrix employed in the FAPI the MFAPI applies the matrix product directly to ensure the orthonormality of the subspace basis matrix at each recursion. This approach yields a simpler derivation and is numerically stable while maintaining a similar computational complexity as compared with that of the FAPI. Furthermore we present a detailed mathematical proof of the numerical stability of our proposed algorithm. Computer simulation results indicate that the MFAPI outperforms many classical subspace tracking algorithms particularly at the transient-state step.
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    Citation - WoS: 1
    Citation - Scopus: 1
    Joint Resource Allocation in Multi-Ris and Massive Mimo-Aided Cell-Free Iot Networks
    (Ieee-inst Electrical Electronics Engineers inc, 2024) Panayırcı, Erdal; Hu, Yulin; Dong, Zhicheng; Panayirci, Erdal; Jiang, Huilin; Wu, Qiang
    To meet the needs of high energy efficiency (EE) and various heterogeneous services for 6G, in this article, we probe into the EE of reconfigurable intelligent surfaces (RISs) subsurface (SSF) architecture-aided cell-free Internet of Things (CF-IoT) networks. Specifically, we jointly optimize the base station (BS)-RIS-IoT device (ID) joint associations, the RIS's phase shift matrix (PSM), and the BS's transmit power to enhance CF-IoT's EE. The elevated complexity (NP-hard) and nonconvexity of the formulated problem pose significant challenges, making the solution highly difficult and intricate. To handle this challenging problem, we first develop an alternating optimization framework based on block coordinate descent, which can decouple the original problem into several subproblems. We then carefully design the corresponding low-complexity algorithm for each subproblem to solve it. Moreover, the proposed joint optimization framework serves as a versatile solution applicable to a wide range of scenarios aiming to maximize EE with the assistance of RISs. Simulations confirm that deploying RISs in CF-IoT scenarios is beneficial for improving the EE of the system, and the SSF architecture can further enhance the EE of the system.