Browsing by Author "Bahingayi, Eduard E."

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Hybrid combining based on selective activation of double constant phase shifter
    (IEEE, 2021) Doan, Anh N. N.; Bahingayi, Eduard E.; Lee, Kyungchun
    In this paper, we propose a new uplink hybrid beamforming architecture for multi-user multiple-input–multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. The proposed architecture employs constant phase shifters (CPSs) to control the phases of the signals in conjunction with double switches for signal selection in the radio frequency (RF) beamforming part. By using double switches to approximate the optimal double variable phase shifter (VPS)-based scheme, the proposed scheme can achieve near-optimal sum rates when the number of CPSs is large enough. In addition, with the support of the active/inactive switches, it selectively combines the antenna signals, which can further improve the sum rate while reducing power consumption. The simulation results show that the proposed scheme outperforms conventional VPS-based and fully digital combiners in terms of energy efficiency.
  • Thumbnail Image
    Item
    Joint hybrid-precoding design for MU-MISO systems with a subconnected architecture
    (SpringerOpen, 2023) Bahingayi, Eduard E.; Lee, Kyungchun
    In this study, we propose a joint hybrid-precoding algorithm for multiuser multiple input single-output downlink systems. Specifcally, we consider that the base station employs an energy-efcient hybrid-precoding subconnected (SC) architecture with fxed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efcient method to transform the system’s sum-rate optimization problem into a continuous and diferentiable objec tive function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifcally, in the proposed AO method, when the digital precoder is fxed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fxed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efciency of the SC-FESA architecture compared to existing algorithms.
  • Thumbnail Image
    Item
    Low-complexity beamforming algorithms for IRS-aided single-user massive MIMO mmwave systems
    (IEEE, 2022) Bahingayi, Eduard E.; Lee, Kyungchun
    This paper considers intelligent reflecting surface (IRS)-aided single-user (SU) massive multiple-input multiple-output (mMIMO) millimeter wave (mmWave) downlink communication system. We aim to maximize the achievable spectral efficiency by separately designing the passive beamforming and active precoding (combining) through a decoupling strategy to reduce computational complexity. We propose two algorithms for passive beamforming design, which are followed by singular value decomposition (SVD) of the effective channel matrix to generate the active precoding and combining matrices at the bases station (BS) and user equipment (UE), respectively. The first algorithm employs the SVD of the BS-IRS and the IRS-UE channel matrices to generate the unitary matrices. These matrices are used to develop the optimization problem, which is solved via a Riemannian conjugate gradient (RCG)-based algorithm, yielding a passive beamforming vector. In the second algorithm, we propose a greedy-search (GS)-based method to select the array response vectors and their corresponding path gains of the mmWave channels between the BS (IRS) and IRS (UE) required to formulate the optimization problem, which is also solved via the RCG-based algorithm, resulting in a passive beamforming vector. The simulation results show that the proposed schemes achieve an improved trade-off between the spectral efficiency and computational complexity.
  • Thumbnail Image
    Item
    Low-complexity incremental search-aided hybrid precoding and combining for massive MIMO systems
    (IEEE, 2020) Bahingayi, Eduard E.; LeeLee, Kyungchun
    The hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a dictionary-constrained low-complexity algorithm for hybrid precoding and combining design. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves near-optimal performance while providing approximately up to 99% complexity reduction compared to the conventional hybrid precoding and combining algorithms.n this study, we propose a joint hybrid-precoding algorithm for multiuser multiple input single-output downlink systems. Specifcally, we consider that the base station employs an energy-efcient hybrid-precoding subconnected (SC) architecture with fxed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efcient method to transform the system’s sum-rate optimization problem into a continuous and diferentiable objec tive function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifcally, in the proposed AO method, when the digital precoder is fxed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fxed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efciency of the SC-FESA architecture compared to existing algorithms.