# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems  
[PERSON]  
1 Department of Information and Communication System, Inje University, 197, Inje-ro, Gimhae-si, Gyeongsangnam-do 50834, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electrical and Computer Engineering, One Shields Avenue, Kemper Hall, University of California, Davis, CA 95616, USA; [EMAIL_ADDRESS]  
[PERSON]  
3 High Safety Core Technology Research Center, Inje University, 197, Inje-ro, Gimhae-si, Gyeongsangnam-do 50834, Korea; [EMAIL_ADDRESS]  
[PERSON]  
4 Department of Electronic, Telecommunications, Mechanical Automotive Engineering, Inje University, 197, Inje-ro, Gimhae-si, Gyeongsangnam-do 50834, Korea4 Correspondence: [EMAIL_ADDRESS]; Tel. : +82-55-320-3847  
The main challenges of the IoT are the low-power, the shortage of spectrum resources, and the short range of coverage. Many wireless technologies have already been developed for IoT networks such as ZigBee, SigFox, and Long Range (LoRa) [1; 2; 3]. However, network providers prefer to develop technologies that could be compatible with the current cellular networks, e.g., LTE-M, NB-IoT. Moreover, cellular networks can predict and control interference effectively and shall remain useful in dense networks such as an IoT scenario [4]. For these reasons, there are a lot of studies on cellular network to respond to the IoT's needs. Since the cellular networks have limited bandwidth, new technologies such as massive MIMO transceivers have been proposed to improve the spectral efficiency [5; 6]. Massive MIMO can easily operate in time-division duplexing (TDD) mode [7], where channel estimation can be more easily available based on the principle of uplink-downlink reciprocity. By using uplink pilots for uplink channel estimation at the massive MIMO transmitter, downlink channel state information (CSI) can be approximately obtained [8]. Nevertheless, in dense MTC device (MTCD) environments, channel estimation faces critical issues that can arise from specific characteristics of mMTC. First, the number of available orthogonal pilot sequences is limited by the dimensionality of the coherence interval of the channel. This leads to a critical shortage of pilots when the number of devices becomes very large. Therefore, the problem of pilot allocation is important in massive MIMO systems [9]. Second, not all of the devices are active simultaneously and frequently. However, each device transmits data to its base station (BS) spontaneously at random, driven by events. Hence, the network should allocate pilots to MTCDs according to a dynamic and adaptive mechanism. One common mechanism is to let each device randomly select one pilot in its uplink transmission to the BS, which however leads to pilot collision [10]. In [11], a belief propagation algorithm is proposed to decontaminate pilot signals and increase system throughput at the cost of access delays. Another interesting protocol called the strongest-user collision resolution (SUCR) has also been proposed this issue, in which the SUCR protocol selects the MTCD with the strongest channel gain as the contention winner in a distributed manner [12]. This protocol can enhance the probability of collision resolution with lower access delay. However, the improvement would be minor in very crowded scenario since the shortage of orthogonal pilot sequences. Various solutions are proposed by 3 GPP to alleviate congestions, such as the access class barring (ACB) scheme, slotted access scheme, dynamic allocation of random access (RA) resources, clustering of MTCDs, and back-off scheme. In addition, these also control the random access of MTC devices [13; 14]. Among these methods, ACB is one of the most efficient solutions to reduce traffic overload [15; 16; 17; 18]. The ACB scheme with the fixed barring factor is proposed in [19]. Using the barring factor as a constant simplifies the system and reduces computation cost. However, it cannot achieve maximum performance.