The large antenna aperture in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems results in a hybrid far and near-field communication. Existing hybrid-field beam training works mostly treat plane waves as spherical ones with infinite distance, thereby inheriting several limitations associated with spherical wave based training, such as protocol incompatibility, high overhead, and complex hierarchical codebook design. To address these issues, we propose a unified far-field and near-field wavenumber domain beam training framework, involving a semi-codebook-based beam sweeping scheme and a hierarchical training strategy. The core idea is reinterpreting spherical wave as a superposition of plane waves, retaining accuracy while inheriting the charming protocol compatibility, low overhead, and simple codebook design provided by plane waves. Moreover, due to the linearity of plane waves, the ideal beam pattern with negligible power leakage can be easily obtained by the proposed phased-shifted alternative minimization (PS-AltMin) codeword design method. Finally, numerical results show that the proposed wavenumber domain beam training methods have a significant achievable rate gain compared to the benchmarks, which comes from the use of the semi-codebook-based transmission technique and the unified channel model for both far-field and near-field.

With the spurt progress of the industrial Internet of Things (IIoT), enhancing the IIoT’s ability to deal with burst traffic is essential. User perceived throughput (UPT) is an appropriate metric for evaluating the real user experience of burst traffic since it takes buffer state into account, which is overlooked by traditional throughput calculations. However, the inherent strong randomness coupled with the emphasis on long-term performance, poses significant challenges to UPT optimization in such a stochastic industrial environment with time-varying characteristics. In this paper, we focus on the UPT to measure the quality of experience (QoE) for burst IIoT, formulating a long-term UPT-maximization problem in the system. To address this, joint user scheduling at the medium access control (MAC) layer and beamforming design at the physical (PHY) layer optimization is considered. By applying Lyapunov optimization theory, the long-term optimization problem is decoupled into more manageable short-term problems. Subsequently, we propose both centralized and decentralized algorithms, based on fractional programming (FP) and successive convex approximation (SCA), respectively. Simulation results verify the effectiveness of the proposed algorithms compared to the baselines, and FP-based collaborative QoE-driven cross-layer co-design algorithm (FPCQA) demonstrates superior UPT gain.