Performance Analysis And Design Of Cell-free Massive MIMO Systems With Limited Multi-Domain Resources

Cell-free massive multiple-input multiple-output(MIMO)systems break through the limitation of inter-cell interference on the system performance by reforming the cellular architecture and increasing the macro-diversity gain,therefore,users’ rates can be uniformly improved.As a key technology to improve the system spectral efficiency,cell-free massive MIMO is considered as one of the technical solutions for the future Sixth Generation(6G)mobile communication system.However,faced with the diverse scenarios and performance requirements of 6G,cell-free massive MIMO systems still face some challenges such as scalability,finite blocklength codes,and limited fronthaul in the implementation of high connection density,ultra-reliable and low-latency communication,and high user rate.Based on this,the main research contents of this paper include:First,in order to solve the scalability issue in cell-free massive MIMO systems,scalable combining algorithms and a power control method are designed under the hierarchical signal processing architecture by reasonably distributing the degree of freedom between obtaining the antenna array gain and suppressing the inter-user interference.The closed-expression of the system spectral efficiency is derived by utilizing random matrix theory.The simulation results prove that the theoretical derivation is right,and reveal the internal relationship between the number of access points(APs),antenna distribution mode,and the number of pilots and the macro-diversity gain,the antenna array gain,and the capability of interference suppression.Besides,it also proves that the number of APs has a key influence on the system spectral efficiency.When the number of APs is doubled,the increase of the macro-diversity gain can increase the system spectral efficiency by 23%.Second,for using the finite blocklength codes in cell-free massive MIMO systems,the separate optimization problems and the joint optimization problem of the precoding and power allocation are set up respectively in order to maximize the minimum user spectral efficiency,and are solved by the path following algorithms.Simulation results show that the proposed precoding and power allocation optimization algorithm can increase the minimum user spectral efficiency by 60% and 12%,respectively,and the joint optimization can bring a gain of 71%.In addition,a distributed precoding algorithm based on the virtual signal-to-interference-and-noise ratio is designed to reduce the computational complexity by adjusting the size of AP clusters.The distributed precoding can achieve 80% of the performance of the centralized precoding with only 12% computational complexity.Third,an expanded compute-and-forward framework based on the nested lattice code is constructed for cell-free massive MIMO systems with the limited fronthaul.By compressing the data dimension at the APs,the fronthaul load is greatly reduced,which is proportional to the number of users.In addition,the system spectral efficiency can be increased 2.6 times by power control,AP selection,and the optimization of the decoding sequence of the linear combination of users’ codewords and users’ codewords.To sum up,through the performance analysis and design of the cell-free massive MIMO system,this paper preliminarily solves the problems of scalability,finite blocklength codes,and the limited fronthaul in the implementation of cell-free massive MIMO systems,and achieves the effect of improving the system spectral efficiency under limited resources.It also provides theoretical guidance for the implementation of the cell-free massive MIMO system.