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.

Network densification is a promising technology to increase 5G system capacity, including large scale multipleinput multiple-output (MIMO) and ultra-dense network. Energy saving and resource utility are the key issues for the densified OFDM system. However, most of previous studies do not taking account of the effect of resource utility in the SINR model, e.g. the load factor. In this paper, we firstly formulate the SINR using more realistic model considering load factor in OFDM systems. Based on this model the energy saving problem is formulated as a joint optimization of beamforming and power subject to users' QoS and load constraint of each cell. To sovle this problem, we propose a two-stage scheme to decouple this problem into the beamforming and power control. In the beamforming stage, we resort to a suboptimal beamformer using the idea of best linear unbiased estimator (BLUE); For the power control stage, we try to minimize the transmit sum-power while fulfilling the user's QoS and load constraints based on theory of standard interference mapping. The convergence behavior of the proposed scheme is rigorously proved. Simulation results show that the proposed scheme converges faster in multiple-antenna cases comparing with the single-antenna counterpart. Moreover the proposed beamformer owns advantage against the traditional ones in term of power saving.