Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Rodríguez-Fernández, Javier

doi:10.1109/twc.2021.3098241

Cited by 25 publications

(46 citation statements)

References 33 publications

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“…Note that the analog precoding and combining matrices are frequencyindependent while the digital precoding and combining matrices depend on subcarriers [6]. Denote…”

Section: A System Modelmentioning

confidence: 99%

“…as additive white Gaussian noise (AWGN) vector satisfying n ∼ CN (0, σ 2 I N A ), and H k u ∈ C N A ×M A as the channel matrix on the kth subcarrier between the BS and the uth user and can be expressed as [6] H…”

Section: A System Modelmentioning

confidence: 99%

“…Therefore, estimation of mmWave channels is usually time consuming. Moreover, the mmWave channels are frequency-selective in most of application environments [6]- [10], which brings more challenges for channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], a channel estimation scheme based on orthogonal matching pursuit (OMP) is proposed by exploring the sparsity of beamspace channels. A channel estimation scheme based on simultaneous weighted orthogonal matching pursuit (SWOMP) in [6] whitens the spatial noise components to improve the estimation accuracy using the OMP method. However, due to the limited beamspace resolution, the sparsity of beamspace channel may be impaired by power leakage [17], which brings extra challenges for the sparse recovery.…”

Section: Introductionmentioning

confidence: 99%

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High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

2020

IEEE Trans. Wireless Commun.

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In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.Index Terms-Millimeter wave communications, channel estimation, hybrid precoding, massive MIMO.

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“…Note that the analog precoding and combining matrices are frequencyindependent while the digital precoding and combining matrices depend on subcarriers [6]. Denote…”

Section: A System Modelmentioning

confidence: 99%

Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

2020

IEEE Trans. Wireless Commun.

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“…Another popular approach found in many publications is to formulate a sparse channel estimation problem and apply well-known or modified CS algorithms [7]- [11]. For example, orthogonal matching pursuit (OMP) or its variants are used in [7], [8], and [9] compares different channel estimation algorithms that include OMP as a representative of CS algorithms. The adaptive dictionary generation algorithm proposed in [12] is useful to deal with common issues regarding the dimension deficiency.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Beamformer Codebook Design and Ordering for Compressive mmWave Channel Estimation

Sung

Evans

2020

2020 International Conference on Computing, Networking and Communications (ICNC)

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In millimeter wave (mmWave) communication systems, beamforming with large antenna arrays is critical to overcome high path losses. Separating all-digital beamforming into analog and digital stages can provide the large reduction in power consumption and small loss in spectral efficiency needed for practical implementations. Developing algorithms with this favorable tradeoff is challenging due to the additional degrees of freedom in the analog stage and its accompanying hardware constraints. In hybrid beamforming systems, for example, channel estimation algorithms do not directly observe the channels, face a high channel count, and operate at low SNR before transmit-receive beam alignment. Since mmWave channels are sparse in time and beam domains, many compressed sensing (CS) channel estimation algorithms have been developed that randomly configure the analog beamformers, digital beamformers, and/or pilot symbols. In this paper, we propose to design deterministic beamformers and pilot symbols for openloop channel estimation. We use CS approaches that rely on low coherence for their recovery guarantees, and hence seek to minimize the mutual coherence of the compressed sensing matrix. We also propose a precoder column ordering to design the pilot symbols. Simulation results show that our beamformer designs reduce channel estimation error over competing methods.

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Deep frameworks based on residual dense network and reinforcement learning for mmWave massive MIMO systems

Faragallah

El‐Sayed

El‐Mashed

2022

Int J Communication

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In millimeter wave (mmWave) massive multiple-input-multiple-output (MIMO) systems, hybrid precoding plays a pivotal role in reducing complexity and cost while providing a good spectral efficiency. However, implementation of digital precoders with large number of antennas is difficult due to hardware constraints, while analog precoders offer confined performance. This leads to high computational complexity and cannot fully exploit the spatial information. Previous studies on hybrid precoding were based on exhaustive search solutions or greedy schemes, which result in higher complexity system performance. To face these challenges, this paper proposes deep hybrid precoding framework with phase quantization and residual dense network to design the matrix of analog and digital precoders. The proposed deep hybrid precoding technique consists of offline training stage and online deployment stage. In offline training stage, hybrid precoding is obtained assuming the approximate phase quantization. While in the online deployment stage, the matrix of analog precoding is calculated by exchanging approximate phase quantization with ideal phase and grouping the analog precoding vectors. In this paper, we also propose a deep reinforcement learning-based hybrid precoding. It consists of a deep reinforcement learning with employing convolutional neural network and long short-term memory (LSTM) methods. In our proposed frameworks, structures of proposed techniques are trained for maximum spectral efficiency.Our proposed techniques are compared with other precoding techniques. Results illustrate that the proposed techniques outperforms the other precoding techniques in terms of the spectral efficiency.

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Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Cited by 25 publications

References 33 publications

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

Hybrid Beamformer Codebook Design and Ordering for Compressive mmWave Channel Estimation

Deep frameworks based on residual dense network and reinforcement learning for mmWave massive MIMO systems

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