FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and nearoptimal performance of the proposed statistical CSI aided RE optimization approach.

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“…where the Lagrange multipliers Ψ k 0(∀k) depend on the problem constraints. The gradient of g k (Λ) over Λ k can be derived from (25) as…”

Section: Appendix B Proof Of Propositionmentioning

confidence: 99%

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

You

Xiong

Zappone

et al. 2020

IEEE Trans. Signal Process.

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“…The uplink CCMs are obtained by projecting the sample covariance matrices to the Toeplitz, positive semidefinate cone. Then, the downlink CCMs are determined by an extrapolation scheme, where we set f ul /f = 1950 2140 [22]. Finally, the outer precode is designed by solving a mixed integer linear program and the inner precoder is computed as ZFBF discussed above.…”

Section: A Simulation Setupsmentioning

confidence: 99%

“…Experimental studies in [20] have evidenced the congruence of the directional properties of the uplink and downlink channels. Motivated by this observation, the authors in [21], [22] proposed to extract the spatial information and use the extracted information to reconstruct the downlink CCM, under the assumption of spatial reciprocity (i.e., angle reciprocity and power angular spectrum (PAS) reciprocity) between the uplink and downlink channels. 1 Besides, Ref.…”

Section: Introductionmentioning

confidence: 99%

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Liu

Yuan

Zhang

2020

IEEE Trans. Wireless Commun.

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In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design.Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.

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“…In [21] we show that the rank of H is given, with probability 1, by the size of the largest intersection submatrix whose associated bipartite graph contains a perfect matching. 2 With this observation, we can formulate the problem mentioned above as follows.…”

Section: Sparsification Precodingmentioning

confidence: 99%

“…It turns out that, this problem can be transformed into the following equivalent mixed integer linear program (MILP), denoted by P MILP [21]:…”

Section: Sparsification Precodingmentioning

confidence: 99%

Uplink-Downlink Channel Covariance Transformations and Precoding Design for FDD Massive MIMO

Khalilsarai

Song

Yang

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity does not hold and explicit downlink (DL) probing and uplink (UL) feedback are required in order to achieve spatial multiplexing gain. In order to support massive MIMO, i.e., a very large number of antennas at the base station (BS) side, the overhead incurred by conventional DL probing and UL feedback schemes scales linearly with the number of BS antennas and, therefore, may be very large. In this paper, we present a new approach to achieve a very competitive tradeoff between spatial multiplexing gain and probing-feedback overhead in such systems. Our approach is based on two novel methods: (i) an efficient regularization technique based on Deep Neural Networks (DNN) that learns the Angular Spread Function (ASF) of users channels and permits to estimate the DL covariance matrix from the noisy i.i.d. channel observations obtained freely via UL pilots (UL-DL covariance transformation), (ii) a novel "sparsifying precoding" technique that uses the estimated DL covariance matrix from (i) and imposes a controlled sparsity on the DL channel such that given any assigned DL pilot dimension, it is able to find an optimal sparsity level and a corresponding sparsifying precoder for which the "effective" channel vectors after sparsification can be estimated at the BS with a low mean-square error. We compare our proposed DNN-based method in (i) with other methods in the literature via numerical simulations and show that it yields a very competitive performance. We also compare our sparsifying precoder in (ii) with the state-of-the-art statistical beamforming methods under the assumption that those methods also have access to the covariance knowledge in the DL and show that our method yields higher spectral efficiency since it uses in addition the instantaneous channel information after sparsification.

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FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification

Cited by 107 publications

References 63 publications

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Uplink-Downlink Channel Covariance Transformations and Precoding Design for FDD Massive MIMO

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