Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

By Ahmed Alkhateeb, Omar El Ayach, Geert Leus, Robert W. Heath. IEEE Journal on Selected Topics in Signal Processing, 2014

2,473 citations recorded by the source corpus.

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/25eb28b67d181f379fccd8392ab737d51a4e397d

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Citations in the corpus

Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems (2016, methodology): “Each scatterer is further assumed to contribute a single propagation path between transmitters and receivers [10], [26], [33].”
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems (2020, methodology): “Following [14], a narrowband geometric channel model is used to characterize the BS-IRS channel G and the IRS-user channel hr.”
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems (2018, methodology): “In order to enable spatial multiplexing at mmWave systems, [24]–[28] proposed to leverage the sparsity of mmWave channels and formulated the mmWave channel estimation problem as a sparse reconstruction problem.”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, methodology): “Aside from beamspace MIMO, another technique for reducing the number of RF chains needed is hybrid precoding [66], which relies on a small-size digital precoder that needs a small number of RF chains and is connected to a larger-size analog precoder.”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, background): “To reduce such overhead, various beam codebook designs such as hierarchical codebooks have been studied [60,61].”
Hybrid Beamforming for Massive MIMO - A Survey (2016, methodology): “The computational complexity in acquiring the instantaneous CSI for constructing the targeted fully-digital beamformer can be reduced using adaptive compressive sensing that exploit the sparse nature of mm-wave channels [67].”
Near-Field Communications: A Tutorial Review (2023, methodology): “The design of beam training involves two parts, namely codebook design and training protocol design, which have been widely investigated for FFC [75], [76], [77], [78], [79], [80], [81], [82].”
Millimeter Wave Communications for Future Mobile Networks (2017, methodology): “To explain the basic idea, we consider the simplest case where both the transmitter and receiver use one RF chain for channel estimation 6 [141].”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, methodology): “This can be used, for example, to realize beam patterns with better characteristics [126].”
Coverage and capacity of millimeter-wave cellular networks (2014, background): “The need for low-cost and low-power hardware, however, has pushed mmWave toward a simpler analog architecture that contains only digitally controlled constant modulus phase shifters.”
A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, methodology): “Compared to other methods, such as the OMP [180], AMP [181], CNN [182], and RNN [183] based methods, the proposed channel estimation scheme converged fast and achieved high accuracy with 5.2 dB NMSE improvement and had substantially low complexity.”
Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm (2015, background): “…channel estimation is a challenging task due to the large-antenna dimension as well as the low SNR before beamforming is applied [14], [16], [21]; whereas with lens antenna arrays, by exploiting its energy focusing as well as the multi-path sparsity of mmWave channels, the effective channel…”
Millimeter Wave Cellular Networks: A MAC Layer Perspective (2015, background): “timation and beamforming algorithms [18]–[21].”
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019, methodology): “Estimating these channel vectors at the LIS, however, normally requires that every LIS antenna gets connected to the baseband processing unit through a fully-digital or hybrid architecture [23], [25], [33].”
5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice (2017, methodology): “With the advent of massive MIMO, especially at mm-wave frequencies, the need for large numbers of RF chains and the corresponding ADC/DACs means increasing power consumption, cost and complexity at the base station [19], [20], [87], [88], [120], [121].”
Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays (2016, background): “The idea of antenna soft selection is reintroduced under the name of hybrid beamforming for mmWave frequencies [27]–[29].”
Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems (2016, methodology): “estimation in frequency-flat mmWave channels [12]–[15], [15]–[20].”
Channel Estimation for Millimeter-Wave Massive MIMO With Hybrid Precoding Over Frequency-Selective Fading Channels (2016, methodology): “[7] proposed an adaptive channel estimation for mmWave massive MIMO, but it is limited to single-user scenario.”