2019/11/10 by Changsheng You, Beixiong Zheng, You, Changsheng +3 · 15 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Advanced Wireless Communication Technologies #Algorithm #Beamforming #Channel (broadcasting) #Computational complexity theory #Computer science #Discrete Fourier transform (general) #Estimator #FOS: Computer and information sciences #Fourier transform #Information Theory (cs.IT) #Initialization #Mathematical optimization #Mathematics #Mean squared error #Minimum mean square error #Networking and Internet Architecture (cs.NI) #Ocular Disorders and Treatments #Orthogonal frequency-division multiplexing #Subcarrier #Telecommunications #Underwater Vehicles and Communication Systems #cs.IT #cs.NI #math.IT
paper · pdf · doi:10.48550/arxiv.1911.03916
published in arXiv (Cornell University) (Cornell University) · Submitted to IEEE conference
openalex publication_date 2019/11/10 · openalex created_date 2019/11/22 · arxiv created 2019/12/23 · arxiv updated 2019/12/24 · openalex updated_date 2026/08/05
In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.