This paper proposes a joint channel and data estimation (JCDE) algorithm for uplink multiuser extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the initial channel estimates are refined by employing a JCDE framework driven by both non-orthogonal pilots and estimated data. The JCDE problem is solved by sequential expectation propagation (EP) algorithms, where the channel and data are alternately updated in an iterative manner. In the channel estimation phase, integrating Bayesian inference with a model-based deterministic approach provides precise estimations to effectively exploit the near-field characteristics in the beam-domain. In the data estimation phase, a linear minimum mean square error (LMMSE)-based filter is designed at each sub-array to address the correlation due to energy leakage in the beam-domain arising from the near-field effects. Numerical simulations reveal that the proposed initial channel estimation and JCDE algorithm outperforms the state-of-the-art approaches in terms of channel estimation, data detection, and computational complexity.

This paper presents an analytical model for blockage loss in millimeter-wave (mmWave) systems using analog beam-forming. The proposed approach computes the average blockage loss by marginalizing the loss obtained from the conventional extended METIS model over all possible blocker positions. This averaging enables the derivation of closed-form expressions for both the upper and lower bounds of the average blockage loss. The analysis reveals a trade-off between array gain and blockage loss, indicating that there exists an optimal number of antenna elements that maximizes the received power under stochastic blockage conditions.

This paper proposes an intensive null-steering around the target in angular domain to effectively suppress inter-user interference (IUI) leakage caused by channel varying environment such as vehicular multiuser spatial multiplexing. Multiuser MIMO can enhance spectral efficiency by multiplexing a number of user terminals in spatial domain. Suppose applying multiuser MIMO downlink in vehicle-to-everything (V2X) scenario, vehicles move at high speed which causes IUI. Null-space expansion has been conceived that can improve IUI suppression capability by steering nulls to the past and the present channel states on interfered users. Collective perception in intelligent transport systems (ITS) provides location information of vehicles every 100 ms. Exploiting this feature, this paper proposes angular-domain null-space expansion; broad-range null-steering (BRNS). Computer simulation verifies its effectiveness.

This paper investigates beamforming design with multi-panel arrays for millimeter-wave (mmWave) communications in the presence of channel fluctuations due to user equipment (UE) movement and sudden path blockage. In current commercial mmWave systems, a sharp analog beam is designed to be aligned to a line-of-sight (LoS) path using all antenna elements (full array) to attain high array gain. Although this approach is effective for static channels, it may lead to frequent disconnections of the communication link in actual mmWave environment with temporal fluctuations. Therefore, this paper revisits the design of analog beamforming composed of multi-panel arrays and studies the effectiveness of multiple beams with wide directivity, which are aligned not only to the LoS path but also to non-line-of-sight (NLoS) paths. Computer simulations illustrate the limitations of conventional sharp directive beamforming focused solely on maximizing array gain, and reveal the effectiveness of the proposed multiple beam design with multi-panel array over practical mmWave channels.

This paper proposes a self-calibration algorithm for a hybrid millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) channel estimation in the presence of array manifold errors composed of mutual coupling, antenna gain/phase errors, and antenna spacing errors due to hardware impairments. The proposed algorithm can jointly estimate angles of arrivals (AoAs), angles of departures (AoDs), path gains, and array manifold errors. The initial coarse estimates for AoAs, AoDs, and path gains are derived by a compressed sensing (CS) algorithm without prior knowledge of array manifold errors. Subsequently, AoAs, AoDs, path gains, and array manifold errors are updated based on corresponding maximum likelihood (ML) criteria. Numerical results demonstrate that the proposed algorithm has superior performance with low pilot overhead compared to conventional methods.