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Marijanovic, L., Schwarz, S., & Rupp, M. (2019). A Novel Optimization Method for Resource Allocation based on Mixed Numerology. In L. Marijanovic, S. Schwarz, & M. Rupp (Eds.), ICC 2019. IEEE. http://hdl.handle.net/20.500.12708/75880
In this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integ...
In this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integer linear program. To reduce the
computational complexity of the optimization, we also propose a
linear relaxation of the problem. We investigate the performance
of our methods by numerical simulations, demonstrating significant
gains over an LTE-compliant scenario with fixed numerology
and a heuristic approach.
de
In this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integ...
In this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integer linear program. To reduce the
computational complexity of the optimization, we also propose a
linear relaxation of the problem. We investigate the performance
of our methods by numerical simulations, demonstrating significant
gains over an LTE-compliant scenario with fixed numerology
and a heuristic approach.
