Research On Massive Access Technology In Smart Grid Based On Congestion Control And Multi-User Detection

With the development of modern communication technology,the communication industry has gradually produced an important development direction,namely the Internet of Things.In the future,a large number of terminal devices will be connected to the smart grid.The number of machine-type devices accessing the smart grid has increased dramatically,but network resources are limited,the existing access technologies are unable to effectively guarantee the requirements of access success rate,latency,and system complexity in smart grid scenarios.Consequently,the congestion issue arising from the simultaneous access of a large number of devices and the problem of differentiated access requirements have become urgent challenges to be addressed in the context of massive access in smart grid scenarios.To address the above issues,this thesis proposes a massive access scheme that addresses differentiated access requirements for single base stations and multi-base station collaboration.The proposed scheme is based on congestion control and multi-user detection approaches,starting from the access layer and physical layer.Additionally,to further enhance system capacity,a multi-user detection scheme based on compressive sensing is introduced.This scheme achieves massive access in smart grid scenarios and effectively improves access performance.The specific research contents are as follows:In the massive access scenario of a single base station in smart grid,different access requirements for differentiated services can lead to challenges in ensuring the access latency demands of various devices.To address this issue,a congestion control scheme based on priority and clustering is proposed in this thesis.Specifically,a channel access control scheme based on priority is proposed,which includes dynamic allocation of random accessing channel resources and back-off scheme.Dedicated channel resources are dynamically allocated to devices that are sensitive to latency,with the number of dedicated resources optimized dynamically based on the number of activated devices,and a back-off mechanism is used to delay access requests from devices with latency tolerance.Secondly,devices are clustered based on distance,and devices within the same cluster cooperate locally to select idle resources during access,which effectively reduces conflicts.Finally,to further reduce access blocking probability,a clustering-based congestion control algorithm is proposed,and simulation results demonstrate that the proposed scheme reduces the average access delay of delay sensitive devices by 89.9%and improves the access success rate of delay sensitive devices by 8.67%.Next,in the context of large-scale multi-base station smart grid access,this thesis proposes a congestion control scheme based on multi-base station collaboration to address the problem of base station selection for devices in overlapping coverage areas and the issue of access conflicts for all devices.Specifically,the thesis first addresses devices located in overlapping coverage areas of multiple base stations,and based on factors such as base station load and number of access devices,enables base stations to make joint decisions and design a reasonable base station selection scheme.Secondly,considering the impact of access algorithms on access success rate and delay,the optimal congestion control scheme is designed.Simulation results show that the proposed scheme achieves better resource allocation,and reduces average blocking rate by 17.07%.Finally,in order to further enhance the capacity of the system and remove the limitation on the number of access due to available channel resources,this thesis proposes a multi-user detection algorithm based on temporal correlation and sparsity estimation for active device detection and precise signal reconstruction in the large-scale grant-free access scenario of smart grid.Specifically,first,based on the sparsity and temporal correlation of activated devices in the actual access scenario of the smart grid,a scheme for improving detection accuracy based on temporal correlation is proposed.The reconstructed estimation value of the signal from the previous time is used for the current multi-user detection algorithm.Secondly,in order to address the issue of unknown sparsity of activated devices in actual scenarios,a cross-validation-assisted multi-user detection scheme is proposed.Simulation results show that the proposed scheme in this thesis achieves multi-user detection with unknown sparsity while effectively improving detection accuracy without significantly increasing algorithm complexity.