# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 1 Introduction
Moreover, in [18; 19], the previously mentioned unlicensed spectrum access model is studied, where the unlicensed users use the licensed nodes uplink channel too. In there, they show that the positions of the unlicensed users are fixed, which makes it reasonable to use highly directional antennas and limited transmit power in the unlicensed network in order to avoid interference from this network affecting the licensed network. However, this does not prevent the licensed nodes from causing interference on the unlicensed users. Since these works are limited to smart grids applications, later on we expanded these works in [20], by following the work done in [21; 22], to make the model more generalized and compatible with other wireless networks. In [23], authors propose a spectrum sharing method for a MTC network in LTE with large number of nodes where simultaneous transmissions are possible. In this model, dynamic spectrum access is possible for a dense network with low data rates. The advantage of this model is that there is no need for signaling procedures that are going to need channel reservation. Moreover, in [24], authors introduce interesting new schemes which make implementing cognitive radio and spectrum sharing methods in smart gird applications easier. They also study a number of routing protocols and interference mitigation schemes related to smart grids. A valuable study has been done in [25] regarding the resource allocation in different spectrum sharing networks such as SINR-based, transmission power-based, and centralized and distributed methods of decision making. In [26], [PERSON] et al. propose a novel multichannel IoT scheme for dynamic spectrum sharing in 5G networks. In their work, an IoT node is able to do two different types of communications at the same time, both 5G and IoT communications. This is done by allocating different sub-channels for these two communication modes. All the above mentioned works have done great works with regards to addressing spectrum sharing in IoT and machine type communications; however, they do not aim at optimizing the performance of the system while having different reliability constraints, which is one of the main pillars of future 5G. In this paper, we expand one of the scenarios in our previously published work [27] and follow the same model as in [18; 20; 28] to prove that the approximation used in those papers for the average number of retransmission attempts is in fact a tight approximation. We show why it is worth to use limited number of retransmission, and optimize the throughput as a function of the SIR threshold and number of retransmissions. Moreover, we also study the energy efficiency of the proposed model in this paper. Energy efficiency is one of the most important issues that needs to be considered in wireless networks, specifically in ultra dense networks [29]. As was previously mentioned, mMTC networks are designed to support massive connectivity between billions of sensors and IoT devices with minimum human interactions [5], most of which are low powered. Most of theses devices are battery supplied, and hence, they have a limited energy supply (wireless sensor networks for instance). It also happens often that these mMTC networks are deployed in critical or hard to access locations, which makes changing the batteries and renewing the energy resources very difficult [30; 31; 32]. All these reinforce the importance of the energy efficiency in mMTC in general. Valuable works have been done in the field of energy efficiency. In [33], authors study different challenges and metrics with regards to reducing the total power consumption of the network while in [34; 35], maximizing the energy efficiency by optimizing the packet size and constrained by an outage threshold in non-cooperative and cooperative wireless networks is studied respectively. In [36], a new scheduling algorithm based on frame aggregation is proposed in order to achieve energy efficiency in IEEE 802.11n wireless networks.