Spatially Sparse Precoding in Millimeter Wave MIMO Systems

By Omar El Ayach, Sridhar Rajagopal, Shadi Abu-Surra, Zhouyue Pi, Robert W. Heath. IEEE Transactions on Wireless Communications, 2013

3,380 citations recorded by the source corpus.

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/261bfaf79635c455c7ed8bd2ddc6785fd9d9ced0

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Citations in the corpus

Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget (2018, methodology): “niques, as proposed in [8], [13], [48], [90], and [91].”
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Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling (2014, background): “The idea of two-stage “hybrid” beamforming has been considered by various authors [11], [22] and it motivated by the fact that, while implementing a large number of antennas in a reasonable form factor may be feasible, having a correspondingly large number of RF chains may be too expensive or power…”
Hybrid Block Diagonalization for Massive Multiuser MIMO Systems (2015, methodology): “The “dominan t” paths in P2P mmWave channels are captured through the hybrid processing in [14] and [15], where the former conside rs the single-stream transmission while the latter enables th multiple-stream communication.”
A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, background): “The paper also shows through simulation results that the proposed SIC-based hybrid precoding is near-optimal and enjoys higher energy efﬁciency than spatially sparse precoding [151] and fully digital precoding.”
Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization (2022, background): “…The noise powers at the each downlink user are set to σ 2 r = σ 2 l = − 70 dBm, ∀ l . at and We set the maximal transmit power budgets the BS each uplink user to P max = 18 dBW and P k = 5 dBW, ∀ k , respectively. the Assume that all the user channels follow LoS channel model [39], where , [26].”
A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018, methodology): “It uses a cluster channel model [40] to”
Communications and Signals Design for Wireless Power Transmission (2016, background): “devoted to enabling cost-effective massive MIMO and mmWave wireless communications via techniques such as analog beamforming [180], hybrid analog/digital processing [181], or the advanced lens antenna arrays [182], [183].”
Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces (2019, background): “Although this kind of constraints have been studied both in hybrid digital/analog precoding [11], [12] and in constant-envelope precoding in massive MIMO systems [13], [14], these studies were only focused on the designs at the transmitter, which are not applicable for the joint active beamforming d”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, methodology): “architectures that comply to these hardware constraints have therefore been proposed [95], [103]–[107].”
Multibeam for Joint Communication and Radar Sensing Using Steerable Analog Antenna Arrays (2018, background): “When the multipath is sparse, estimating these underlying parameters directly can be simpler than estimating the matrix elements [12].”
Millimeter Wave Communication: A Comprehensive Survey (2018, methodology): “Assuming perfect channel knowledge at the transmitter, Ayach et al. [72] explore the utilization of transmit precoding and receiver combining based on large antenna arrays.”
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Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding (2019, methodology): “Spectrum efficiency versus SNR in the case of the proposed deep learning based hybrid precoding scheme, the spatially sparse precoding method [3], and the fully digital GMD based precoding scheme.”
Coverage and capacity of millimeter-wave cellular networks (2014, background): “Hybrid precoding divides the required precoding processing between the analog and digital domains, and hence allows better control of the beam shape.”
Millimeter Wave Cellular Networks: A MAC Layer Perspective (2015, background): “The sparsity in the angular domain can be leveraged to realize efﬁcient channel estimation and beam-forming algorithms [7].”
Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems (2015, methodology): “This allows decoupling the transceiver design problem, and focusing on the hybrid precoders design to maximize the mutual information of the system [27], defined as”
A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges (2020, background): “Hence, MIMO solutions are essential for mmWave frequencies [25]–[27], since it exploits beamforming gain to establish links with a reasonable signal-to-noise ratio (SNR) to combat path loss and extend the range [2].”