Ultra-dense cell-free Massive MIMO (CF-MM) is a promising technology for future 6 G networks. Unlike previous research, we focus on the challenges of CF-MM in the control plane. Specifically, we investigate the initial synchronization processing for CF-MM, which is fundamental for user equipment (UE) to access the wireless network. Regardless of whether LTE- or New Radio (NR)-based synchronization signals are used, the existing initial access procedures are incompatible with CF-MM, leading to severe interference on the broadcast channel and thus a high initial access failure rate. To address this issue, we design a new synchronization scheme for CF-MM based on the latest NR standard. With this new scheme, the initial synchronization interference problem is then transformed into a transmission and reception points (TRP) ID partition problem. To solve problem, we then propose an approach that combines the latest multiple operator heuristic (MOH) method with a parameterized local search(PLS) method. Simulation results show that the proposed method can achieve the optimal solution for scenarios with a low number of TRPs. Furthermore, the gap between the proposed method and the optimal solution is less than 2% for high number TRPs cases, demonstrating its effectiveness.

The future B5G/6G wireless networks are required to intelligently operate and optimize in all ranges of scenarios, however, most of the functions in self-organizing network (SON) architectures are rule-based and reactive, these rigid functions can not keep up with the dynamic nature of the future wireless networks. In this article, we propose an active load balancing framework considering mobility load balancing (MLB) and mobility robust optimization (MRO), which are two essential functions to ensure a seamless user experience for the future B5G/6G. The proposed method is based on random subspace Bayesian additive regression tree (RS-BART) and interior point policy optimization (IPO). Specifically, user trajectory is firstly forecasted by utilizing RS-BART, and then the joint problem of MLB and MRO is further solved by IPO. Simulations based on open radio access network (O-RAN) reveal that the proposed method reduces load deviation and radio link failure ratio by 40% and 50% compared to the previous ones.

To meet the further explosive capacity demand, network densification is a promising technology. The load imbalance between cells in an ultra-dense heterogeneous network is a major challenge, which seriously affects the performance of the system. Existing mobility load balancing (MLB) methods usually operate in reactive mode. The parameters of the cells are adjusted reactively according to the dynamic changes of network load. The inherent reactivity of existing LB schemes undermines the quality of experience in 5G and beyond. To solve this problem, we propose a mobility management framework for load balancing, which firstly uses the Bayesian additive regression trees to predict the users’ temporal and spatial mobility based on a weekly cycle and then formulate the MLB optimization problem based on the soft load. Moreover, one solution is proposed to solve the 01 MLB problem. The simulation results show that the proposed method can better optimize the network performance and realize the intelligent mobile management for the future network.

To meet the further explosive capacity demand, network densification is a promising technology. The load imbalance between cells in an ultra-dense heterogeneous network is a major challenge, which seriously affects the performance of the system. Existing load balancing (LB) methods usually operate in reactive mode. The parameters of the cells are adjusted reactively according to the dynamic changes of network load. The inherent reactivity of existing LB schemes undermines the quality of experience (QoE) in 5G and beyond. To solve this problem, we propose a mobility management framework for load balancing, which turns the original reactive load balancing into forwardaware and active. The simulation results show that the proposed method can better optimize the network performance and realize the intelligent mobile management for the future network.

To fulfill the high capacity requirements of 5G and B5G, ultra-dense network (UDN) is a promising technology. As the network becomes denser, energy consumption becomes one of the primary concerns of the operators. To reduce the power consumption of UDN, in this paper, we propose a hierarchical energy optimization approach with two energy-efficient strategies: one is dynamic coverage for large time-scale scenario; the other is individual power adjustment based on weakly standard interference mapping for small time-scale scenario. Considering a more realistic power consumption model, the optimization problem is formulated as adapting network capacity to users' demands, so as to improve energy consumption and other network performances e.g., load balancing. The proposed approach owns the following advantages: First, it can obtain the optimal solution with low computational complexity; second, they are distributed and thus the considered problem can be solved in a parallel manner; all of these make the proposed methods suitable for UDN. Simulation results verify that the proposed approach can obtain the optimal solution in few iterations and the complexity of computation is low.