# Power Efficient Random Access for Massive NB-IoT Connectivity
## 2 Literature Survey Random Access in NB-IoT
### Related Work  
Since its introduction in 3 GPP Rel-13, NB-IoT has gained increasing research interest for its ability to address low complexity, high coverage, long device battery life and massive capacity objectives [1; 2]. Performances and design considerations of NB-IoT, spanning over its evolutions, technologies and issues are summarized in recent works [1; 2; 21; 22]. More specifically, the works on random access in NB-IoT are focused on either the detection of superimposed NPRACH preambles or the optimistic resource allocation [4; 7; 9; 14; 23; 24; 25; 26].  
In Ref. [7], the NB-IoT random access procedure is probabilistically modelled using Markov chain for the evaluation of system throughput in terms of number of devices, re-transmission number, packet generation rate and the length of the queue. Research works in [9; 14] consider non-overlapping sub-carriers reserved by the eNB for the three different CE levels to derive success probability for devices located in the different levels. Authors in [9] present an analytical model to estimate success probability and delay in RAP while considering three CE levels of NB-IoT deployment. [PERSON] et al.  
Figure 2: Novel narrowband Internet-of things (NB-IoT) physical random access channel (NPRACH) design for NB-IoT.  
in [23] propose a joint optimization technique under a target delay constraint. The work presents optimal configuration of NPRACH parameters in order to maximize the access success probability [23]. Access reservation protocol with partial preamble transmission is presented in [24]. The partitioning of preamble facilitates reuse of the same preamble sequence by several devices. While the proposal mitigates collision probability, it is accomplished by the degradation of detection performance [24]. A novel random access model that considers the NB-IoT traffic characteristics is presented in [25]. For Beta and uniform arrivals throughput and delay are analyzed. When multiple devices attempt RAP simultaneously, the eNB receives superimposed NPRACH preambles [4]. In such an environment detection of all users is a challenge and thus the work in [4] formulates a framework for multi user detection problem. The capacity gain is estimated in [26] while considering that massive number of NB-IoT devices access network with delayed back-off value. Trade off between reattempts of random access and repetition of preambles is investigated in [14]. The results show that even though the large repetition values increase the detection probability at physical layer, they do not necessarily mean higher success of RAP at MAC-layer [14]. It also highlights that the average energy consumption increases with the increase in number of repetitions. It is not only intuitive but also clear from recent works [14; 27] that repetitions increases energy expenditure. Moreover, all the aforesaid works have highlighted the increase in collisions when NB-IoT devices perform access in massive connectivity. Thus we believe that is it important to analyse power consumption while devices perform RAP in high contention and high repetition NB-IoT environment. Table 1 summarises the related work section.