Cross-layer optimization with model-based parameter exchange

Cross-layer optimization (CLO) promises significant gains in comparison to a conventional system design, which does not allow for information exchange across layers. One of the key challenges in CLO is the exchange of parameters between optimizer and layers. In this paper a model-based approach is presented that drastically reduces the amount of parameters that are to be exchanged. The optimizer employs models of the respective layers that emulate the communication system within the optimizer. The layers then only need to pass a small number of model parameters to the optimizer. This general concept is applied to CLO between application (APP) layer and medium access control (MAC) layer of a radio communications system. Our proposed model for the MAC layer is suitable for a transmitter without instantaneous channel state information (CSI). Simulation results demonstrate that the proposed modelbased CLO is able to exploit the available diversity to enhance the system capacity. Dependent on the application characteristics, the same perceived quality in terms of mean opinion score (MOS) is maintained, while increasing the number of served users by up to 25%. Compared to known CLO approaches, much fewer parameters need to be exchanged.