# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 2 System Model
Hence, we have the following assumptions to evaluate our scenario:  
* Unlicensed network: We assume only a reference link, which is fixed and associated with a specific gateway/base-station/aggregator. * Licensed network: This is not evaluated in this work. We only assume that at every transmission attempt by the unlicensed reference link, there is a random number of active users in the licensed network with a random relative position in relation to the aggregator node (thus, we can model the positions of the active transmitters of the licensed network in respect to the reference aggregator of the unlicensed as a Poisson Point Process). The power used by the unlicensed users for their transmission is limited. This limitation which is either imposed on them from the licensed network or comes from sensors own properties, restricts the maximum range that the unlicensed network transmitted signal can reach. This means that the  
Figure 1: An illustration of the dynamic spectrum access scenario, where licensed and unlicensed users share the up-link channel. The unlicensed transmitter is depicted by the smart meter, the aggregator (unlicensed receiver) by the red aggregator, the handsets are the mobile licensed nodes (interferers to the aggregator) and the big antenna is the cellular base-station. As the smart meter uses directional antennas with limited transmit power (bold arrow), its interference towards the base-station can be ignored. The thin black arrows represent the licensed nodes’ desired signal, while the dotted ones represent their interference towards the aggregator. unlicensed users radiation patterns can be considered as a line segment in which the starting points are these nodes, and the end point is determined by the power constraint. Moreover, it is assumed that in this model, there are no packet collisions between the transmitted packets by the sensors to the same aggregator. This is justifiable since the packet size in these transmissions is assumed to be small and the unlicensed network can take advantage of the multiple access solutions. Another important aspect that should be considered in this model is that before the unlicensed users use the licensed nodes channel, they first evaluate the channel usage and then do their transmission if they sense the channel is free; otherwise, they have to postpone their transmission or use another frequency band [42; 43]. Considering the above explanations, we can see that the interference in this model can stem from the following different sources, (i) from the mobile users to aggregators, (ii) from sensors to cellular base stations and (iii) from sensors to aggregators that they are not communicating with. Taking into account the aforementioned system model assumptions, it is possible to conclude that if the licensed and unlicensed nodes are implemented in explicit positions, it makes it possible to eliminate the interferences in cases (ii) and (iii). Following [18; 27], we assume that (iii) can be eliminated by a specific access policy since the links between sensors and aggregator are deterministic (each sensor is assumed to be connected with one specific and known aggregator). Therefore, the aggregator can allocate orthogonal channels or different time slots to each sensor. Besides, due to the narrow beam and limited transmit power, the interference from sensors related to different aggregators can be neglected. Even if it is assumed that the nodes are implemented in random locations, the probability of having the base station or aggregator in the same line segment as the unlicensed users signal is close to zero [18; 20; 28]. As follows, the only source of interference in this model is then (i). In order to be able to evaluate the impact of this interference on the system performance, we need to be able to have a good understanding of the uncertainty of the licensed nodes' locations.