Numerical results show that minimal Hamming neighborhoods offer an attractive trade-off between scalability and exploration capability in grap-based optimization and GBSE outperforms existing techniques.
Abstract
This letter develops a radio access network (RAN) framework for mixed discrete-continuous optimization problems that arise in user-centric cell=free massive multiple-antenna networks. The novel framework exploits the structural decomposition between discrete clustering decisions and continuous resource allocation variables by modeling the space of feasible serving states as a graph with Hamming-topology neighborhoods. A serving-state graph abstraction is introduced to enable topology-aware search-and-evaluate optimization procedures and a graph-based search-and-evaluate (GBSE) algorithm is devised along with their complexity analysis. Energy efficiency maximization at the RAN level is presented as an application of considered alongside the proposed framework and GBSE algorithm. Numerical results show that minimal Hamming neighborhoods offer an attractive trade-off between scalability and exploration capability in grap-based optimization and GBSE outperforms existing techniques.
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