Analysis of data-aided channel tracking for hybrid massive MIMO systems in millimeter wave communications

As the data traffic in future wireless communications will explosively grow up to 1000 folds by the deployment of 5G, several technologies are emerging to satisfy this demand, including massive multiple-input multiple-output (MIMO), millimeter wave(mmWave) communications, Non-Orthogonal Multiple Access (NOMA), etc. The combination of millimeter wave communication and massive MIMO is a promising solution since it can provide tens of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As the wavelength of higher frequency shrinks, it is possible to design more compact antenna array with a very large number of antennas. However, this will cause enormous hardware cost, energy consumption and computation complexity of decent RF(Radio Frequency) chains. To this end, spatial sparsity is widely explored to enable hybrid mmWave massive MIMO systems with limited RF chains to achieve high spectral and energy efficiency. On the other hand, channel estimation problem for systems with limited RF chains is quite challenging due to the unaffordable overhead. To be specific, the conventional pilot-based channel estimation requires to repeatedly transmit the same pilot because only a limited number of antennas will be activated for each time slot. Therefore, it consumes a huge amount of temporal and spectral resources. To overcome this problem, channel estimation for mmWave massive MIMO systems is still an on-going research area. Among plenty of candidates, channel tracking is the most promising one. To achieve the extremely low cost and complexity, which is also the greatest motivation of this thesis, data-aided channel tracking method is thoroughly investigated with closed-form CRLB(Cram´er-Rao lower bound). In this thesis, data-aided channel tracking systems with different types of antenna, including ULA(Uniform Linear Antenna array), DLA(Discrete Lens Antenna ar ray) and UPA(Uniform Planar Antenna array), are comprehensively studied and proposed, and the closed-form expressions of the corresponding CRLBs are carefully derived. The numerical results of the simulations for each case are shown respectively, and they reveal that the performance of the proposed data-aided channel tracking system approaches the CRLB very well. In addition, to further explore the data-aided channel tracking system, the multi-user scenario is investigated in this thesis. This is motivated by the highway and high-speed railway application, where overtaking operation happens frequently. In this case, the users in the same beam suffer from high channel interference, thus degrading the channel estimation performance or even causing outage. To deal with this issue, we proposed an estimated SER(Symbol Error Rate) metric to indicate if a scheduling operation is necessary to be taken place and restart of the whole channel tracking system is required. This metric is included as the Update phase in the proposed channel tracking method for multiuser scenario with DLA. The theoretical SER closed-form expression is also derived for multi-user data detection. The numerical results of the simulations verified the theoretical SER expression, and the scheduling metric based on the estimated SER performance is also discussed

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

When base stations (BSs) are deployed with multiple antennas, they need to have downlink (DL) channel state information (CSI) to optimize downlink transmissions by beamforming. The DL CSI is usually measured at mobile stations (MSs) through DL training and fed back to the BS in frequency division duplexing (FDD). The DL training and uplink (UL) feedback might become infeasible due to insufficient coherence time interval when the channel rapidly changes due to high speed of MSs. Without the feedback from MSs, it may be possible for the BS to directly obtain the DL CSI using the inherent relation of UL and DL channels even in FDD, which is called DL extrapolation. Although the exact relation would be highly nonlinear, previous studies have shown that a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the BS. Most of previous works on this line of research trained the NN using full dimensional UL and DL channels; however, the NN training complexity becomes severe as the number of antennas at the BS increases. To reduce the training complexity and improve DL CSI estimation quality, this paper proposes a novel DL extrapolation technique using simplified input and output of the NN. It is shown through many measurement campaigns that the UL and DL channels still share common components like path delays and angles in FDD. The proposed technique first extracts these common coefficients from the UL and DL channels and trains the NN only using the path gains, which depend on frequency bands, with reduced dimension compared to the full UL and DL channels. Extensive simulation results show that the proposed technique outperforms the conventional approach, which relies on the full UL and DL channels to train the NN, regardless of the speed of MSs.Comment: accepted for IEEE Acces

Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO

In this paper, we present a new performance bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower bound (CRLB) due to considering more statistics since performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.Comment: accepted for presentation in a poster session at the ICASSP 2020 conferenc