Mid Term frequency domain scheduler for resource allocation in wireless mobile communications systemsMid Term frequency domain scheduler for resource allocation in wireless mobile communications systems

 The expansionary monetary policy measures have had a negative impact on net interest margins both via the reduction in interest rates and –less powerfully- the flattening of the

Jointly estimate this entry game with several outcome equations (fees/rates, credit limits) for bank accounts, credit cards and lines of credit. Use simulation methods to

In our sample, 2890 deals were issued by less reputable underwriters (i.e. a weighted syndication underwriting reputation share below the share of the 7 th largest underwriter

• We apply the proposed TES-RNN model to two practical anticipatory NI use cases, i.e., (i) anticipatory allocation of network resources, and (ii) prediction of anomalies in the

utilized in practice [11], in the mobile network domain, the vast majority of AI/ML applications (e.g., routing, load balancing, and resource allocation) use much more complex AI

We observe that the utility of the honest players does not change with the history length H 1 , whereas that of the dishonest player decreases with the history length for both QPQ

The video stream broadcasted by the base station is encoded into L scalability layers using a scalable video encoder, i.e., one base layer, containing the most im- portant video

In second 15, the scheduler node changes the transmission frequency values (frequency set F 1 ) based on control of frequencies; in time interval 15-30 s., the system is stable;