## 2 Literature Review  
Providing massive access to MTCDs is a challenging task within itself and network congestion is the most likely occurrence when a large number of MTCDs access the base station simultaneously. Existing scheduling designs for MTCDs can be categorized as [8]: (i) channel-based, that schedules devices with the highest SINR to maximize throughput [9; 10; 11]; (ii) delay-based, that schedules according to delay budget of user devices for resources [10; 12]; (iii) fairness-based, that guarantees a fair distribution of resources to devices [13]; and (iv) hybrid design, that considers a combination of the aforementioned and other metrics such as buffer status along with power consumption [9; 14]. The need for a scalable connectivity and energy-efficient scheduling design is a key requirement for the efficient cellular operation of MTCDs. MTC metric design has been based on throughput, propagation delay, transmit power, and impact on QoS of HTC devices [8]. Uplink scheduling with hybrid access for MTC along with HTC nodes over cellular networks was proposed [14]. A rather simplistic energy consumption model can be designed for MTC, considering only the transmit power for reliable data transmission and neglecting any other source of energy consumption. The energy efficient operation of MTC over LTE networks is studied [15], and it was shown that an energy efficient design is not possible with the LTE physical layer. A power-efficient uplink scheduler design utilizing LTE system specification for delay-sensitive traffic over was investigated [16]; however, the considered traffic characteristics and delay models are not applicable in the case of MTC. Narrowband-IoT (NB-IoT) has seen considerable efforts in the domain of massive connectivity as well as coverage and battery lifetime maximization [17; 18]. However, the battery consumption model proposed in these works for mMTC devices needs to be modified to depict the diversity in service requirements of NB-IoT devices. There is also a need to address the problem of battery lifetime improvement in coexisting environments which has been addressed in this work. Game theory helps by determining strategic interactions as well as by providing a depiction of the rational behavior of users and the design of a distributed approach for a reliable solution. Resource management can be implemented efficiently by modeling a given network scenario as a game, with the players being the users in contention for different resources. A hierarchal game model for optimal resource allocation in a heterogeneous network was proposed [19]. Two non-cooperative sub-games were played sequentially in an attempt to overcome the problems of interference management and user rate maximization, respectively. Users optimized their connectivity with either a femtocell or macrocell based on a tradeoff between experienced interference and minimum rate requirement. Optimal power allocation schemes using game theory design to maximized user utilities for two-tier femtocell, ultra-dense and NOMA networks were proposed [20; 21; 22]. Interference coordination was achieved for device-to-device (D2D) links underlying cellular network using a distributed power control scheme [23]. A power control design with Nash equilibrium (NE) was designed for each link to guarantee interference minimization. Utility function modeling and reaching a stable equilibrium for a large number of users becomes increasingly difficult. Conventional game theory models cannot be used for massive user networks and advanced game models are required to efficiently design and model user behavior to reach a converged solution strategy. MFG is an advanced game theory framework a mean field characterizing the space-time dynamics, i.e., a player can make an optimal decision by only conforming with the mean field, instead of consulting the strategies of every other player in the game. MFG decision making is distributed with reduced information and control overheads [24; 25; 26; 27].