Pilotless Uplink for Massive MIMO Systems

TL;DR

This work addresses pilot overhead in uplink massive MIMO-OFDM by introducing a pilotless receiver that blind-demodulates user data and simultaneously estimates user channels using an alternating minimization framework. It leverages a low-rank signal model and carefully designed initial-point strategies to ensure convergence, with additional mechanisms to resolve the inherent scaling/rotation ambiguities via a minimal number of pilots. The method scales to single- and multi-user scenarios, achieving BER performance comparable to conventional pilot-based schemes while dramatically reducing pilot overhead and enabling spectral efficiency gains; complexity can be further reduced by exploiting temporal channel correlations across OFDM symbols. The results show substantial spectral-usage gains (near 100% data occupancy with only a single pilot per user) and robust performance across realistic channel models, including antenna correlation and multi-path fading, making it attractive for 5G/6G uplink deployments.

Abstract

Massive MIMO OFDM waveforms help support a large number of users in the same time-frequency resource and also provide significant array gain for uplink reception in cellular systems. However, channel estimation in such large antenna systems can be tricky as pilot assignment for multiple users becomes more challenging with increasing number of users. Additionally, the pilot overhead especially for wideband rapidly changing channels can diminish the system throughput quite significantly. In this paper, we propose an iterative matrix decomposition algorithm for the blind demodulation of massive MIMO OFDM signals without using any pilots. This new decomposition technique provides estimates of both the user symbols and the user channel in the frequency domain simultaneously (to a scaling factor) without any pilots. We discuss methods for finding the appropriate initial points for the algorithm that ensure its convergence in different types of wireless channels. We also propose new methods for resolving the scaling factor in the estimated signal that do not increase pilot overhead. We show how the method can be adapted to both single-user and multi-user systems. Simulation results demonstrate that the lack of pilots does not affect the error performance of the proposed algorithm when compared to the conventional pilot-based channel estimation and equalization methods across a wide range of channels for both single and multi-user cases. We also demonstrate techniques to reduce the complexity of the estimation algorithm over multiple OFDM symbols in a 5G MIMO system by leveraging the temporal correlations in the channel.

Figures (23)

• Figure 1: Scatter-plot of potential initial points constructed from $\mathbf{u_1}$ and different columns of the DFT matrix, $\mathbf{F_L}$ as per \ref{['eq.15']} for a 4-tap channel with the second tap having the highest relative power. When $\mathbf{u_1}$ is multiplied with the conjugate of the column of $\mathbf{F_L}$ corresponding to the dominant tap (second tap in the above case), the initial point strongly resembles a QAM constellation.
• Figure 2: Error performance of the variance-based (Algorithm \ref{['BlindIPvar']}) and circularity-based (Algorithm \ref{['BlindIPcirc']}) initial point estimation methods for a single user.
• Figure 3: Bit error rates of blind estimation using alternating minimization algorithm (10 iterations) for different values of the regularization parameter, $\mu$.
• Figure 4: $\mathbf{\hat{X}}$ output of 10 iterations of the matrix decomposition (MD) algorithm for $10$ dB SNR, 1024 FFT, $64$ antennas, (a) before de-rotation, and (b) after de-rotation using the scaling factor $\lambda$ estimated with one pilot shown against the Voronoi regions for 64-QAM.
• Figure 5: Centroids (in red) of the clusters obtained from the constellation in Fig. \ref{['fig:md_2']} using Lloyd's k-means algorithm, (a) with residual rotation. The slope of the line through a row of centroids (in blue) gives the residual rotation to be corrected. (b) Centroids after correction of the residual rotation using the slope of the blue line from (a). Points in black correspond to the standard 64-QAM constellation.