Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.

High-Performance Low-Complexity Hierarchical Frequency Synchronization for Distributed Massive MIMO-OFDMA Systems

We propose a high-performance yet low-complexity hierarchical frequency synchronization scheme for orthogonal frequency-division multiple-access (OFDMA) aided distributed massive multi-input multi-output (MIMO) systems, where multi-ple carrier frequency offsets (CFOs) have to be estimated in the uplink. To solve this multi-CFO estimation problem efficiently, we classify the active antenna units (AAUs) as the master and the slaves. Then, we split the scheme into two stages. During the first stage the distributed slave AAUs are synchronized with the master AAU, while the user equipment (UE) is synchronized with the closest slave AAU during the second stage. The mean square error (MSE) performance of our scheme is better than that of the representative state-of-the-art baseline schemes, while its computational complexity is substantially lower.