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1. Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems

   Rottenberg, F. ; Choi, T ; Luo, P. ; Zhang, J. ; Molisch, A. F. ( January 2019 , ArXiv.org)

   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, which completely removes the training overhead. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive theoretical lower bounds (LB) for the mean squared error (MSE) of the extrapolated channel. Assuming that the paths are well separated, the LB is simplified in an expression that gives considerable physical insight. It is then shown that the MSE is inversely proportional to the number of receive antennas while the extrapolation performance penalty scales with the square of the ratio of the frequency offset and the training bandwidth. The channel extrapolation performance is validated through numeric simulations and experimental measurements taken in an anechoic chamber. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems if accurate system calibration is performed and favorable propagation conditions are present. 

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2. Scalable user selection in FDD massive MIMO

   https://doi.org/10.1186/s13638-021-02073-4  

   Zhang, Xing ; Sabharwal, Ashutosh ( December 2021 , EURASIP Journal on Wireless Communications and Networking)

   User subset selection requires full downlink channel state information to realize effective multi-user beamforming in frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. However, the channel estimation overhead scales with the number of users in FDD systems. In this paper, we propose a novel propagation domain-based user selection scheme, labeled aszero-measurement selection, for FDD massive MIMO systems with the aim of reducing the channel estimation overhead that scales with the number of users. The key idea is to infer downlink user channel norm and inter-user channel correlation from uplink channel in the propagation domain. In zero-measurement selection, the base-station performs downlink user selection before any downlink channel estimation. As a result, the downlink channel estimation overhead for both user selection and beamforming is independent of the total number of users. Then, we evaluate zero-measurement selection with both measured and simulated channels. The results show that zero-measurement selection achieves up to 92.5% weighted sum rate of genie-aided user selection on the average and scales well with both the number of base-station antennas and the number of users. We also employ simulated channels for further performance validation, and the numerical results yield similar observations as the experimental findings.

    

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3. Channel Extrapolation for FDD Massive MIMO: Procedure and Experimental Results

   Choi, T ; Rottenberg, F. ; Gomez, J. ; Akshai, R. ; Peng, L. Zhang ; Molisch, A. F. ( January 2019 , IEEE Vehicular Technology Conference Workshops)

   Application of massive multiple-input multipleoutput (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink training and feedback. Channel extrapolation, i.e., estimating the channel response at the downlink frequency band based on measurements in the disjoint uplink band, is a promising solution to overcome this bottleneck. This paper presents measurement campaigns obtained by using a wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64 element antenna array, in both an anechoic chamber and outdoor environment. The Space Alternating Generalized Expectation-Maximization (SAGE) algorithm was used to extract the parameters (amplitude, delay, and angular information) of the multipath components from the attained channel data within the “training” (uplink) band. The channel in the downlink band is then reconstructed based on these path parameters. The performance of the extrapolated channel is evaluated in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in comparison to the “ground truth”, i.e., the measured channel at the downlink frequency. We find strong sensitivity to calibration errors and model mismatch, and also find that performance depends on propagation conditions: LOS performs significantly better than NLOS. 

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4. Uplink Power Control and SNR-Dependent Beam Alignment Errors in MmWave Cellular Networks

   https://doi.org/10.1109/PIMRC54779.2022.9977460  

   Zia, Muhammad Saad ; Blough, Douglas M. ; Weitnauer, Mary Ann ( January 2022 , Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications)

   Beam alignment is a critical aspect in millimeter wave (mm-wave) cellular systems. However, the inherent limitations of channel estimation result in beam alignment errors, which degrade the system performance. For systems with a large number of antennas at the base station, downlink channel estimation is performed using uplink pilot signals. The beam alignment errors, thus, depend on the user equipment (UE) transmit power, which needs to be managed properly as the UEs are battery powered. This paper investigates how the use of uplink power control for the transmission of pilot signals in a mm-wave network affects the downlink beam alignment errors, which depend on various link parameters. We use stochastic geometry and statistics of the Student's t -distribution to develop an analytical model, which captures the interplay between the uplink power control and downlink signal-to-noise ratio (SNR) coverage probability. Our results indicate that using uplink power control significantly reduces UE power consumption without adversely affecting the downlink SNR coverage. 

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5. Training for Chanel Estimation in Nonlinear Multi-Antenna Transceivers

   Kang Gao, J. Nicholas ( February 2019 , Information Theory and Applications)

   Recent efforts to obtain high data rates in wireless systems have focused on what can be achieved in systems that have nonlinear or coarsely quantized transceiver architectures. Estimating the channel in such a system is challenging because the nonlinearities distort the channel estimation process. It is therefore of interest to determine how much training is needed to estimate the channel sufficiently well so that the channel estimate can be used during data communication. We provide a way to determine how much training is needed by deriving a lower bound on the achievable rate in a training-based scheme that can be computed and analyzed even when the number of antennas is very large. This lower bound can be tight, especially at high SNR. One conclusion is that the optimal number of training symbols may paradoxically be smaller than the number of transmitters for systems with coarselyquantized transceivers. We show how the training time can be strongly dependent on the number of receivers, and give an example where doubling the number of receivers reduces the training time by about 37 percent. 

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