Browsing by Author "Wu, Qiang"

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    Energy-Efficient Design for RIS-Aided Cell-Free Ultra Dense HetNets
    (Ieee, 2023) 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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    Article
    Citation Count: 8
    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.