Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a numerical method for finding packings of multiple-input and multiple-output (MIMO) semi-unitary precoding matrices in Grassmannian manifolds with different metrics. The proposed expansion-compression algorithm (ECA) is practical, simple and produces efficient packings without the need for a sophisticated initialization. With chordal distance metric, the algorithm tends to converge into a degenerated point constellation, where two points contain identical as ll as orthogonal columns and distance beten them cannot increase further along geodesic. Therefore,  alternate beten max-min and min-max clustering parts of ECA algorithm, where the latter prevents degenerated constellations. With Fubini-Study distance metric, the algorithm converges to best known packings without extra min-max processing.
['Karol Schober', 'Pekka Janis', 'Risto Wichman']
I am writing a research paper on a idea Geodesical codebook design for precoded MIMO systems
[{"from": "human", "value": "I am writing a research paper on a idea Geodesical codebook design for precoded 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:  propose a numerical method for finding packings of multiple-input and multiple-output (MIMO) semi-unitary precoding matrices in Grassmannian manifolds with different metrics. The proposed expansion-compression algorithm (ECA) is practical, simple and produces efficient packings without the need for a sophisticated initialization. With chordal distance metric, the algorithm tends to converge into a degenerated point constellation, where two points contain identical as ll as orthogonal columns and distance beten them cannot increase further along geodesic. Therefore,  alternate beten max-min and min-max clustering parts of ECA algorithm, where the latter prevents degenerated constellations. With Fubini-Study distance metric, the algorithm converges to best known packings without extra min-max processing."}]