Reciprocity Calibration for Massive MIMO: Proposal, Modeling, and Validation

Vieira, João; Rusek, Fredrik; Edfors, Ove; Malkowsky, Steffen; Liu, Liang; Tufvesson, Fredrik

doi:10.1109/twc.2017.2674659

Cited by 161 publications

(109 citation statements)

References 25 publications

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“…We finally note that a further requirement for using uplink CSI for downlink beamforming is reciprocity calibration [25]- [27], since upconverters and downconverters might have different transfer functions that have to be compensated for by suitable calibration. Since reciprocity calibration affects FDD and TDD systems in the same manner, we disregard it in the following derivations and simulations, i.e., we assume that it is perfect.…”

Section: Channel Modelmentioning

confidence: 99%

Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems

Rottenberg

Choi

Luo

et al. 2020

IEEE Trans. Wireless Commun.

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Channel estimation for the downlink of frequency division duplex (FDD) massive MIMO systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users.The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived.The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

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Section: Channel Modelmentioning

confidence: 99%

Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems

Rottenberg

Choi

Luo

et al. 2020

IEEE Trans. Wireless Commun.

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“…, Q K , and hence finding the maximum is a nontrivial problem. One would like to apply the BC-multiple access channel (MAC) duality theorem [29] so as to transform the nonconcave problem (26) into an equivalent, concave one, for which efficient solvers are known to exist [55]. However, the presence of B in the constraint…”

Section: Algorithm 1 Ue-side Greedy Beam Selectionmentioning

confidence: 99%

Massive MIMO Performance—TDD Versus FDD: What Do Measurements Say?

Flordelis

Rusek

Tufvesson

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-Downlink beamforming in Massive MIMO either relies on uplink pilot measurements-exploiting reciprocity and time-division duplexing (TDD) operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in frequency-division duplexing (FDD) operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, gridof-beams. It has been analytically shown that with isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless, there remains controversy regarding their relative performance in practical channels. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.

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“…An attractive alternative to consider is frequency-division duplexing (FDD) based cell-free massive MIMO systems for the following reasons: 1) channel reciprocity in TDD mode might not be accurate due to calibration errors in radio frequency (RF) chains [12], 2) with the lack of downlink training symbols in TDD systems, users may not be able to acquire instantaneous CSI, and thus system performance will deteriorate in detecting and decoding the intended signals, 3) while TDD operation is preferable at sub-6 GHz massive MIMO, in millimeter wave (mmWave) bands FDD may be equally good since the angular parameters of the channel are reciprocal over a wide bandwidth [13], and 4) FDD systems dominate current wireless communications and have arXiv:2001.07438v1 [cs.IT] 21 Jan 2020 many benefits such as lower cost and greater coverage than TDD [14].…”

Section: Introductionmentioning

confidence: 99%

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the socalled angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-ofarrival (AoA)-based beamforming/combining schemes for FDDbased cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a maxmin power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.Index Terms-FDD mode, cell-free massive MIMO, multipath component estimation, array signal processing, angle-based beamforming/combining, power control.

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Reciprocity Calibration for Massive MIMO: Proposal, Modeling, and Validation

Cited by 161 publications

References 25 publications

Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems

Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems

Massive MIMO Performance—TDD Versus FDD: What Do Measurements Say?

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

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