Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neighborhood search algorithms have been proposed for detection in large/massive multiple-input multiple-output (MIMO) systems. They iteratively search for the best vector in a fixed neighborhood. Hover, the ML solution may not lie in the searched space or the search may take a large number of intermediate vectors to converge. Instead of searching in a fixed neighborhood, a better way will be to look for an update which is not restricted to be in a fixed neighborhood. Motivated by this,  formulate an optimization problem to maximize the reduction in ML cost and use it to derive an expression for updating the solution.  use a metric based on the channel matrix and the error vector to determine the likelihood of a symbol being in error. Using this likelihood and the update,  propose a likelihood ascent search (LAS) algorithm to find an update which is not restricted to be in a fixed neighborhood and seeks to provide maximum reduction in ML cost. This process continues till there is a reduction in the ML cost. Compared to existing LAS based algorithms, it is found to provide better error performance, that too at a lor complexity.
['Abhay Kumar Sah', 'Ajit Kumar Chaturvedi']
I am writing a research paper on a idea Beyond fixed neighborhood search in the likelihood ascent algorithm for MIMO systems
[{"from": "human", "value": "I am writing a research paper on a idea Beyond fixed neighborhood search in the likelihood ascent algorithm for MIMO systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neighborhood search algorithms have been proposed for detection in large/massive multiple-input multiple-output (MIMO) systems. They iteratively search for the best vector in a fixed neighborhood. Hover, the ML solution may not lie in the searched space or the search may take a large number of intermediate vectors to converge. Instead of searching in a fixed neighborhood, a better way will be to look for an update which is not restricted to be in a fixed neighborhood. Motivated by this,  formulate an optimization problem to maximize the reduction in ML cost and use it to derive an expression for updating the solution.  use a metric based on the channel matrix and the error vector to determine the likelihood of a symbol being in error. Using this likelihood and the update,  propose a likelihood ascent search (LAS) algorithm to find an update which is not restricted to be in a fixed neighborhood and seeks to provide maximum reduction in ML cost. This process continues till there is a reduction in the ML cost. Compared to existing LAS based algorithms, it is found to provide better error performance, that too at a lor complexity."}]