Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

By Kiran Venugopal, Ahmed Alkhateeb, Nuria González-Prelcic, Robert W. Heath. IEEE Journal on Selected Areas in Communications, 2016

384 citations recorded by the source corpus.

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/72338a7d1adbb51f5d4db7b5357bbe9d9b972acf

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Citations in the corpus

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018, background): “Wideband mmWave channel estimation problem for hybrid architectures is discussed in [140].”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, methodology): “Conventionally, training-based channel estimation [206] and beam sweeping [207] are two widely adopted approaches for hybrid beamforming design.”
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, neutral): “In this case, and assuming uniform linear arrays (ULAs) at both ends, the received signal for the k th training frame can be written as [46] where h vec is the sparse vector containing the time-domain complex channel gains after vectorization of the channel matrix; Φ ( k ) td is the sensing matrix…”
Millimeter Wave Communications for Future Mobile Networks (2017, background): “The second approach is to regard (7) as a classical sparse signal recovery problem [150]–[152].”
Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems (2016, methodology): “M ILLIMETER wave (mmWave) has been receiving tremendous interests from the academia, industry, and government for future 5G cellular systems [1]–[9] due to”
Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems (2021, background): “The complexity of the OMP algorithm is given by O(nml(3)), where n is the length of the measurement data, m is the length of the sparse signal with sparsity level l [25].”
Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information (2017, methodology): “Note that, in this work, we do not exploit the sparsity in delay-domain, as in [35].”
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems (2018, background): “Since the sparse mmWave channel is highly correlated in the frequency domain [54], we will only consider a subset of the OFDM symbols for the inputs of the learning model.”
Prospective Multiple Antenna Technologies for Beyond 5G (2019, background): “Moreover, compared to lens arrays with phase shifters (see for e.g. [106] and references therein), lens arrays offer substantial hardware and power savings.”
Reconfigurable Intelligent Surfaces: A signal processing perspective with wireless applications (2021, neutral): “We obtain the following observation of the uncontrollable channel: Similarly, for the i th configuration of the controllable channel { , , }), Q 1 f The estimation of the uncontrollable channel can be performed using any standard channel approximation technique, e.g., compressive sensing [43].”