Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising technology for sixth-generation mobile communication systems. Building upon conventional CF-mMIMO, the cell-free radio access network (CF-RAN) architecture distributes physical-layer functionalities among access points (APs), edge distributed units (EDUs), and user-centric distributed units (UCDUs), striking a balance between complexity and performance. However, prior studies on CF-RANs have primarily focused on the physical-layer, while the scalability of the medium access control (MAC) layer remains insufficiently explored. This paper proposes a novel hierarchical scalable CF-RAN architecture that fully exploits the functional potential of distributed UCDUs, enabling a comprehensive decentralized paradigm spanning from the physical-layer to the MAC-layer. Closed-form uplink spectral efficiency (SE) expressions are derived for maximum ratio (MR), distributed full-pilot zero-forcing (FZF), and distributed joint partial zero-forcing (JP-ZF) combining, explicitly accounting for imperfect channel state information and pilot contamination. The analytical insights reveal the improved scalability and how distributed processing and partial information availability affect system performance. To support large-scale deployments, we further develop a structured massive access scheme, including UCDU-EDU deployment, UE-UCDU association, distributed pilot assignment and AP-UE association, centralized refinement, and distributed power control. Simulation results verify the accuracy of the theoretical analysis and demonstrate the superior SE, favorable fairness, and enhanced scalability of the proposed schemes.

In this work, a distributed non-orthogonal pilot design approach is proposed to tackle the pilot contamination problem in multi-cell massive multiple input multiple output (MIMO) systems. The pilot signals are designed under power constraints by minimizing the total mean square errors (MSEs) of the minimum mean square error (MMSE) channel estimators of all base stations (BSs). In order to solve the above non-convex pilot design problem, the stochastic variance reduced gradient (SVRG) projection algorithm is introduced, where the pilots signals are optimized in a distributed way at individual BSs. The SVRG projection algorithm preserves the randomness of the transient gradient, which makes the solution more likely jump out of the local minima. Moreover, only part of the BSs are activated to perform the gradient descent operation during each iteration, producing a green and low-cost infrastructure. Numerical simulations demonstrate the superiority of the proposed approach in terms of the channel estimation accuracy and uplink achievable sum rate.

In this work, we propose a novel non-orthogonal pilot design framework to tackle the pilot contamination problem in multi-cell massive multiple input multiple output (MIMO) systems. Assuming the minimum mean square error (MMSE) estimators are adopted at the base stations (BSs), we design pilot signals under power constraints by minimizing the total mean square errors (MSEs) of the MMSE estimators of all BSs. The pilot design problem is difficult to solve due to the non-convex objective function. A linearized alternating direction method of multipliers (L-ADMM) algorithm is introduced to solve the above non-convex optimization problem. The L-ADMM algorithm could approximate the objective function in a linear form, which makes the original problem solvable. In addition, a new non-orthogonal pilot design problem that maximizes the total spectral efficiency of all users in the cellular network is established. We show that the proposed L-ADMM-based pilot design method can be directly extended to solve it. Finally, numerical simulations validate that the proposed pilot design framework can achieve higher channel estimation accuracy and uplink achievable sum rate compared to the state-of-the-art approaches with less computational complexity.

Millimeter wave (mmWave) communication is a promising technology for future outdoor cellular systems. However, the abrupt channel change is more prone to occur over mmWave band compared to conventional frequencies, which will degrade the system performance. Hence, it is important to detect the channel change accurately for preparation of re-estimating the channel state information (CSI). In this paper, we attempt to solve the channel change problem from the perspective of change-point detection. By exploiting the low-rank characteristic of mmWave massive multiple-input multiple-output (MIMO) channel, the received signals can be viewed as data points in a subspace superimposed with noises. Then a cumulative sum (CUSUM)-based subspace change-point detection algorithm is designed to detect the mmWave channel change. The method can work without the aid of pilot signals and is of low computational complexity and memory requirement. Results are provided to validate the satisfactory of the proposed method.