# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 1 Introduction  
Supporting Internet of Things (IoT) services is emerging as one of the most important problems facing communication systems [1; 2]. There are many applications, such as public safety, e-health, fleet management, and smart metering [1], that will use machine-to-machine (M2M) communication technology, which ensures connectivity to the devices without human interaction  
Cellular networks are expected to play a major role in enabling M2M communications because they can provide seamless coverage over a large area, support mobility, and offer security. However, there are several problems with enabling M2M communications over cellular networks because these networks are optimized for human-to-human (H2H) communications and the characteristics of M2M communications are different from those of conventional H2H communications. For example, M2M communications are triggered by events, time-controlled, involve the transmission of small amounts of data, and are energy-limited [3]. The 3 rd Generation Partnership Project (3 GPP) has studied the issues facing M2M services in cellular networks. In [1], the 3 GPP studied potential system improvements for M2M communications, the authors organized the architectural considerations and defined the expected problems for M2M communications. One of the most critical problems is congestion due to the massive access by M2M devices. The control plane at the radio-access-network (RAN) level suffers during massive access by M2M devices due to their characteristics. M2M devicestry to establish a connection with an evolved node B (eNB) when they have data to transmit, causing a severe random access channel (RACH) congestion problem. The 3 GPP suggested some solutions [1], including an access class barring (ACB) scheme, a separate RACH resource-allocation scheme, a dynamic RACH resource-allocation scheme, and an M2M-specific back-off scheme. Because RACH congestion due to massive access causes the degradation of RA throughput, increasing the RA throughput has become a major research target. The ACB method efficiently solves the RA throughput problem by controlling the access probabilities of separate access classes. In [4], the authors suggested dynamic-ACB (D-ACB) to not only overcome the RACH congestion problem due to bursts of M2M traffic but also to reduce the total service time. The D-ACB determines the accurate ACB factor without backlog information. In [5], a cooperative ACB (C-ACB) scheme was proposed to reduce the congestion in multi-cell cases. In this study, the ACB factors of each eNB were used for the eNB selection by M2M devices. Furthermore, the ACB factors were jointly determined among the neighboring eNBs. The access load could be distributed using the cooperative method. In [6], timing-advance (TA) information was used to reduce the RA collision probability because the propagation delay between a stationary M2M device and the eNB is a constant. Hence, the authors assumed that the TA values in a random access response (Msg2) could be used as an identifier; therefore, some preamble collisions could be solved. Based on this, they proposed a TA-based ACB scheme for increasing RA throughput. In [7], an ACB scheme for delay-sensitive devices is proposed. The authors consider the scenario where delay-sensitive and delay-tolerant services coexist. In addition, a priority-based ACB scheme adjusts dynamically the access probability and the number of preambles to satisfy the requirements of delay-sensitive services. In addition, delay-tolerant services can also be highly utilized based-on preamble allocation. A preamble-separation method has also been proposed to increase the RA throughput. In [8], two preamble-allocation methods for M2M/H2H services were studied. One method split the preambles into disjointed sets for M2M and H2H separately, while the other split them into an H2H-only set and an H2H/M2M-hybrid set. The authors modeled these methods and analyzed them in terms of throughput.