The Analysis And Optimization Of Performance In 5G Ultra-dense Small Cell Networks

With the explosive growth of smart mobile devices and the rise of the Internet of Things,the current wireless communication network has been struggling with the overloading communication demands.Therefore,dras-tically increasing the system capacity has become the first priority in 5G.Among the key technologies of 5G,ultra-dense small cell networks and multi-antenna technology are considered the most promising solution.The ultra-dense small cell networks achieve the purpose of increasing the spec-trum reuse by deploying a large number of micro base stations using the same frequency to achieve several times of the capacity expansion;while the multi-antenna technology improves the system capacity through diver-sity and reuse.In order to explore the performance gain brought by the combination of these two technologies,this paper studies the influence of base station density,user density and number of base station antennas on the performance of wireless communication networks in the ultra-dense small cell networks using sleep mode.First of all,this paper proposes a more realistic path loss model that distinguishes line-of-sight/non-line-of-sight propagation and uses MRT/MRC beamforming scheme,then de-duces the expressions of coverage probability,ASE and AEE based on mathematical theory such as stochastic geometry theory and probability theory.Then,this paper studies the base station density,user density,the number of base station antennas and sleep mode on the above three per-formance indicators through simulation.The simulation results confirm the correctness of the theoretical derivation.At the same time,we can observe that increasing the base station density will greatly enhance the coverage probability at the beginning,but as the number of base stations increases,the interference under the line-of-sight propagation becomes larger and larger.The sleep mode weakens the interference caused by line-of-sight propagation,and the coverage probability first descends and then increases.The increase of the number of antennas is more effective for the area under the small base station density than under the large base station density.The enhancement effect can up to 37.8%;ASE under sleep mode is lower than that not used,but for AEE there is a significant improvement,up to 0.4 bps/J/Hz/km~2;the effect of increasing the number of antennas to enhance the ASE is general,much worse than increasing the base station density;too many antennas will significantly weaken AEE;when the ratio of the base station density to the user density is about 1/10,coverage probability and AEE are high,but ASE is low.The above observations provide some reference design principles for the actual deployment of a network of ul-tra-dense small cells.Although ultra-dense small cell networks and multi-antenna technol-ogy can bring many performance gains,they also have some shortcomings,such as energy consumption.With the increasing number of base stations and antennas,the energy consumption has greatly increased.With today's increasingly scarce resources,energy conservation and emission reduction have become important ways to achieve sustainable development.There-fore,we must pay attention to the AEE problem.However,at the same time,the scarcity of spectrum resources also determines that we can not ignore ASE in the same way when we focus on AEE.Therefore,there is a trade-off between AEE and ASE.Taking the base station density and the number of base station antennas as optimization variables,this paper stud-ies the maximization problem of AEE under the condition of given ASE in sleep mode using first-order and second-order optimization conditions and an iterative optimization algorithm.The simulation results show that the proposed algorithm converges fast and achieves the maximum AEE.ASE optimization can also be achieved while maximizing AEE.That is to say,the iterative optimization algorithm realize the joint optimization design of ASE and AEE.