Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

By Shuowen Zhang, Rui Zhang. IEEE Journal on Selected Areas in Communications, 2019

763 citations recorded by the source corpus.

Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2853c936d15641bee2ddc863ec4f2e050e4e54ac

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Citations in the corpus

Reconfigurable Intelligent Surface Aided NOMA Networks (2019, methodology): “The channel capacity of a RIS-aided MIMO network was maximized, where both analog and digital beamforming, as well as hybrid beamforming were considered [26].”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, methodology): “Scenarios Phase shifts CSI Main Objectives Techniques/Characteristics [73] SU/MU-DL-MISO Continuous Perfect Transmit power AO-SDR-based algorithm and two stage algorithm [74] SU/MU-DL-MISO Discrete Perfect Transmit power Near-optimal ZF-based successive refinement method [75] MU-DL-MISO Continuous P”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, methodology): “By exploiting the unique channel structure of an RIS-aided MIMO system, the authors of [189] derived the optimal solution for the RIS reflection coefficients in closed form, despite the non-convexity of the unit-modulus constraint.”
Double-IRS Assisted Multi-User MIMO: Cooperative Passive Beamforming Design (2020, background): “, orthogonal frequency division multiplexing (OFDM) [6]– [10], multi-antenna communication [11]–[13], non-orthogonal multiple access (NOMA) [14]–[16], etc.”
Channel Estimation and Passive Beamforming for Intelligent Reflecting Surface: Discrete Phase Shift and Progressive Refinement (2019, background): “Moreover, IRS passive beamforming has been jointly designed with other communication techniques, such as orthogonal frequency division multiplexing (OFDM) [14], MIMO [15], [16], non-orthogonal multiple access (NOMA) [17]–[20], physical-layer security [21]–[24], simultaneous wireless information and ”
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems (2020, background): “, G) from x̄, the knowledge of x̄ itself is enough for joint beamforming for the MIMO scenario as the joint beamforming problem can be converted to an optimization problem which maximizes ‖H̄‖(2)F = ‖vec(H̄)‖(2)2 with respect to v [19], [20].”
Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis (2019, background): “It can be shown that the number of channel coefficients involved for designing the IRS reflection coefficients based on the algorithms proposed in [6], [7] is KMN +KM .”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, methodology): “Specifically, with given transmit covariance matrix and any N − 1 IRS phase shifts, the optimal solution of the remaining phase shift to (P5) can be obtained in closed-form [59]; on the other hand, with given IRS reflection matrix Θ and consequently the overall MIMO channel H̃ , the optimal solution”
A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels (2020, background): “All the above-mentioned literature [4]–[11] did not consider the transmission design by taking into account the channel estimation error.”
Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G (2023, neutral): “7 for the source-RIS and RIS-destination links, respectively [69].”
Intelligent Reflecting Surface Enhanced Wireless Networks: Two-Timescale Beamforming Optimization (2019, methodology): “, [5], [6], [11], [13], based on the I-CSI of all channels, the beamforming design complexity and channel estimation overhead can be significantly reduced by the proposed new protocol based on S-CSI.”
Simultaneously Transmitting And Reflecting (STAR) RIS Aided Wireless Communications (2021, methodology): “Furthermore, the communication capacity of a RIS-aided MIMO system and a RIS-aided multi-user system was characterized in [15] and [16], respectively.”
MIMO Capacity Characterization for Movable Antenna Systems (2022, background): “Specifically, in step 4 and step 9, the complexity to calculate Q ⋆ and S ⋆ based on water-filling is O ( MN min( M, N )) [36].”