Massive heterogeneous IoT networks encounter significant random access (RA) challenges due to diverse Quality of Service (QoS) requirements and resource constraints. To address these issues, we first propose a fuzzy logic-assisted multi-criterion access priority ranking (FL-MCAPR) scheme to prioritize RA for IoT devices, integrating delay, channel interference, and energy factors. The resulting suitability values enable adaptive and fine-grained backoff adjustments in large-scale IoT deployments. Next, hybrid RA control schemes with a deployability-descending double-queue (D3Q) structure and access priority-backoff window model optimize preamble and backoff allocation. In addition, preamble parallelization and early-stage collision detection enhance RA throughput by expanding resources and reducing collisions. Using D3Q, the analytical RA throughput is derived, informing an optimization problem to determine Access Class Barring (ACB) factors balancing low delay and energy efficiency, considering all RA resources. Building on these results, the hybrid fuzzy logic-guided RA management (HFL-RAM) scheme is developed for comprehensive RA management, systematically evaluated in terms of delay and throughput. Finally, a lightweight pseudo-Bayesian estimation method is applied, which relies solely on two observable quantities to estimate the contending MTCD traffic. Simulation results demonstrate that the proposed HFL-RAM scheme consistently outperforms conventional approaches, effectively managing traffic heterogeneity across a wide range of traffic loads.

In this paper, a comprehensive and detailed uplink performance analysis is provided for cell-free massive multipleinput multiple-output orthogonal frequency division multiplexing (CF m-MIMO OFDM) systems, which consider the impact of multiple user equipment (UE) heterogeneous factors. This is the first performance analysis work on CF m-MIMO OFDM systems that simultaneously accounts for the heterogeneous mobility speed, activation probability and serving priority. Considering that UE's serving priority determines the amount of its allocated time-frequency resources, a novel closed-form expression of uplink spectral efficiency (SE) is derived by weighting each UE's SE based on its allocated time-frequency resources. The derived SE expression can quantify the impact of multiple UE heterogeneous factors on the uplink performance. Additionally, the SE performance analysis of local processing and fully centralized processing is also included for comparison. Simulation results show that CF m-MIMO OFDM systems under multiple UE heterogeneous factors outperform existing CF m-MIMO systems in terms of the 90 %-likely uplink SE, and allow a trade-off among fronthaul overhead, complexity and SE performance.

In this paper, a novel three-stage data-aided dual-space (DADS) (i.e., beamspace and signal subspace) channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems subject to pilot contamination. By exploiting the orthogonality of signal subspace, the non-overlapping interference caused by pilot contamination is identified and mitigated in the coarse channel estimate via subspace projection. Thanks to the independence of transmitted data between users, the overlapping interference is suppressed through alternating iterative refinement of the channel estimate and detected data. Additionally, to initially address the under-determined estimation problem arisen from hybrid beamforming (HBF) structures, an improved matching pursuit algorithm is proposed for coarse sparse beamspace channel estimation by appropriately selecting the scaling factor and adjusting the step size in a piecewise manner, followed by enhancement via subspace projection. Furthermore, by accurately detecting the overlap level of interference in the beamspace, the proposed channel estimation scheme selects an appropriate channel enhancement or refinement strategy to address both non-overlapping and overlapping interference subject to several typical channels without significantly increasing computational complexity. Simulation results demonstrate that the proposed channel estimation scheme achieves higher channel estimation accuracy and exhibits stronger resilience to both interference intensity and the number of interference compared to existing channel estimation schemes.

In this paper, a novel dual-space (DS) two-stage channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems with pilot contamination. This is the first work in m-MIMO which takes into account both HBF and pilot contamination. The proposed channel estimation scheme consists of two stages. In Stage I, a coarse sparse channel estimation is conducted in the beamspace based on the proposed variable-stepsize adaptive matching pursuit (VS-AMP) algorithm, which enhances the channel recovery accuracy by carefully selecting and setting the scaling factor and step size. In Stage II, the impact of pilot contamination from interfering users is initially mitigated through the beamspace separability, while coarse channel estimate is further refined into a quasi-sparse format by subspace projection. Simulation results show that the proposed channel estimation scheme outperforms the state-of-the-art schemes in terms of normalized mean square error (NMSE) of channel estimation and bit error rate. The NMSE of the proposed channel estimation scheme also exhibits higher resilience to interference intensity while maintaining comparable computational complexity.