Dynamic RACH Distribution for M2M Massive Access in LTE-A David Arag˜ao
Dario Vieira
Miguel Franklin de Castro
Federal University of Cear´a (UFC) Fortaleza, Cear´a Email:
[email protected]
Engineering School of Information and Digital Technologies (EFREI) Paris, France Email:
[email protected]
Federal University of Cear´a (UFC) Fortaleza, Cear´a Email:
[email protected]
Abstract—Machine-to-machine communication (M2M) is a core element for the Internet of Things (IoT). The Long-Term Evolution-Advanced (LTE-A) is expected to become a potential access network for M2M devices. However, M2M communication poses some challenges to LTE-A, with highlight to the congestion and overload problems in the Radio Access Network (RAN) during the Random Access Channel (RACH) procedure. In this paper, we propose two solutions to mitigate the impact of M2M communication in the context of LTE-A network. More specifically, we model how the random access resources should be allocated to the different types of devices as a bankruptcy problem. In addition, we propose solutions for the bankruptcy problem using a game theory and an axiomatic strategy. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses and define priority among the different classes of devices. Index Terms—Internet of things; LTE-A Networks; Machine to machine communication; Bankruptcy problem;
I. I NTRODUCTION The Internet of Things (IoT) defines a paradigm where devices, also known as smart sensors, are highly connected and interacting through the Internet. The Machine-to-machine (M2M) communication model will enable devices to exchange information with one another with little or no human intervention. Until 2020, more than 20 billion devices are expected to be used in areas like smart metering, health care and smart cities. These higher number of devices using the LTE-A networks may cause congestion and overload problems. The overload and congestion problem in the RAN of LTEA occurs essentially during the RACH procedure. Aware of this problem, the Third-Generation Partnership Project (3GPP) has prepared some technical reports (e.g. [1]–[3]) aiming to describe and present some possible approaches to neutralize the overload and congestion problem. Some classical schemes presented by 3GPP are: Access class barring - ACB, specific backoff, slotted access, separate RACH, dynamic allocation of PRACH resource and pull access [1]. When analyzed separately, such approaches present some drawback (e.g., high access-delay for H2H and M2M devices, low number of accesses for H2H and M2M), but they are still being used c 2016 IEEE 978–1–5090–4671–3/16/$31.00
as a guideline for other approaches in the literature (e.g., [4], [5]). These problems have motivated the development of our proposals. In this paper, we propose two mechanisms to control the impact of the M2M on H2H devices under a overloaded scenarios. We also define different priority levels among the M2M devices. To achieve such objectives, we improve the separate RACH schema proposed by 3GPP. The separate RACH schema divides the RACH resources into two disjunction parts for H2H and M2M devices. However, there is no standard solution describing how the RACH resources should be allocated for each type of device. In our approaches, we model this issue as bankruptcy problem. To cope with the drawback of not setting priority between M2M devices, we also extend the proposed approaches to consider the existence of two types of M2M devices. The results obtained through exhaustive simulations using the network simulator (NS-3), presented and discussed in Section IV, show that our approaches meet the above requirements. The remainder of the paper is organized as follows. In Section II, we present the proposed approaches. In Section III, we describe the simulation scenario. The conclusions and future works are presented in Section V. II. P ROPOSED A PPROACHES During the RACH procedure, devices should randomly select a preamble code that will be transmitted to the eNodeB. However, with the increase of M2M devices, more collisions might occur, increasing the congestion and overload in the RAN. Thus, fewer devices will be able to get access to the network. The expected number of devices E[Y ] that will select a single preamble code, given that there are M preamble codes available for random access and K devices attempting to send a preamble request, can be expressed by the following equation: E[Y ] = K(1 − 1/M )K−1 . Based on the previous equation and knowing that the maximum performance obtained by a channel that follows a slotted aloha behavior is δ = 1e ≈ 0.37, where e is the Euler constant e = 2.71828, only 37% preambles are selected by a single device. Thus, for a given number of devices K, the expected number of preambles required to maximize the chances of successful transmissions is given by: F (Y ) = K × 1δ . This
means that the number of preambles M allocated for a class that contains K devices should be K × e. A. The Vector of Claims Consider N as the different classes of devices, for each i ∈ N , there are Xi preambles that should be allocated for each device class, where Xi = Li + Coli , Li is the number of preambles received with success by the base station, and Coli is the number of preambles selected by two or more devices (collisions). Instead of separating the resources between only H2H and M2M devices [1], [6], [7], we also divide the M2M into two classes which are: M2M with high priority (M2Mp ) and M2M with low priority (M2Mwp ). The general form of the vector of claims is given by: j
ci =
1X F (Xi,k−l ) j
(1)
l∈0
The Eq. 1 is a simple moving average (SMA) of the sum of successfully received and collided preambles by a device type (i) over the last j th RACH procedure. In order to associate with each device class a different priority, we weight the amount requested by each class with three different factors: α, β, γ. Thus, the total claimed by the n classes is: C = α(cH2H ) + β(cM 2Mp ) + γ(cM 2Mwp ). B. Preamble Code Allocation Approaches Based on the approach presented in [5], [8] we divide the devices in three classes: H2H; M2Mp and M2Mwp . Each one of these classes will be a player, such that N = {H2H, M 2Mp , M 2Mwp } indexed by i and n = |N |. The amount of resources (E) represents the total number of preamble codes available for the contention based RACH procedure (T ). Thus, we propose two different approaches, that can be summarized as follows: 1) Proportional proposal. Defines the amount of preamble codes available for each class of device (i) using P (E, c), where E = T , c is defined in Section II-A, and the available preambles (T ) are divided among the n types of devices, both defined in Table ??.
devices. These factor values were increased or decreased based on the simulation. The network configuration used is based on the 3GPP reference material [1], same used to simulate the approaches proposed in this work. We kept the number of H2H devices fixed in 200 and the number of M2Mp and M2Mwp devices (ranging from 100, 400, 700, . . . , 1900). Thus, our most overloaded scenario counts with 4000 devices (200 H2H, 1900 M2Mp and 1900 M2Mwp ). The result obtained to α, β, γ were 3, 2, 1 and 3, 2, 0.265 for the proportional and shapley approaches, respectively. We have implemented a mechanism to represent the approaches proposed in [1], [6], [7] that defines a fixed amount of preamble code for each device class (H2H, M2Mp and M2Mwp ). The mechanism equally divides the preambles among the three classes of devices regardless the congestion level on the RAN. In the remainder of this paper, we will refer to this mechanism as ’Fixed’. The simulation will evaluate the performance of our approaches (Proportional and Shapley) and the ’Fixed’ previously explained. III. S IMULATION E NVIROMENT To evaluate our approach, we choose the simulator NS-3 [9]. The official version of NS-3 has an LTE-A module, but some key features of the RACH procedure were not implemented at the time this paper was written. Thus, to evaluate the approaches presented in this paper, we have implemented some feature of the LTE-A module related to the RACH procedure. Among the features implemented there is the possibility to simulate others PRACH Configuration Index and to simulate scenario with more than 320 nodes [10]. IV. S IMULATION R ESULTS Due to lack of space, we show only the result regarding the impact in the H2H Devices. A. Impact In The H2H Devices
2) Shapley proposal. Defines the amount of preamble codes available for each class of device (i)Papplying G(E, v), where E = T , v(S) = max{T − ci , 0} i∈N \S
and the amount received by each class of device (i) is given by the Shapley value φi (v). The weight values α, β, γ for each class were obtained through simulation, where the H2H devices present a priority higher than M2M devices. We also define a different priority among the devices belonging to the M2M classes, with M2Mp , M2Mwp representing those devices with high and low priority, respectively. Our goal was to identify values for each factor, so that: (i) the impact over H2H should be as minor as possible, regardless the overloaded level of the network; (ii) the average number of M2Mwp devices that successfully accesses the network should be at least equal to half of the number of M2Mp
Fig. 1. Successful Access and Access Probability of H2H Devices
1) Successful Access and Access Probability: The impact in the numbers of H2H devices that successfully access the
network is very low. Regardless the total number of M2M devices, less than one H2H device is not able to get access, as illustrated in Fig. 1. Since the H2H devices have the highest priority, the total resources claimed by H2H devices are more likely granted by the three approaches (Proportional, Shapley and Fixed). In addition, there is also the priority given by eNodeB to the H2H class, as presented in Section. 1. The high priority defined for H2H device is also reflected in the access probability, which sustains the results within the interval of 1.0 to 0.995. Indeed, as shown in Fig. 1, the three simulated approaches control the impact of M2M in the H2H devices. The strategies used to prioritize the different classes of devices, combined with the fixed number of H2H devices, provide a comfort zone for the H2H devices. Even under an overloaded scenario, the proposed approaches (Proportional and Shapley) provide enough resources for the H2H devices. However, a minor difference among the Shapley and Proportional approaches can be noticed in terms of access delay, as shown in Fig. 2. The Fixed approach allocates a fix amount of preambles for H2H devices. Thus, the amount of resources is enough to keep the H2H access stable regardless the number of M2M devices being simulated. Such stability is also presented in the Shapley and Proportional approach, as shown in Fig. 1.
Fig. 3. Preamble Sent - H2H Devices
Fixed ones is sustained. V. C ONCLUSION AND F UTURE W ORKS The results obtained after exhaustive (30) simulations show that our approaches accomplish to control the impact over H2H, respect the priority among the three different types of devices, and present good results in terms of access delay. In addition, our approaches are also energy efficient and improve the random access procedure of the LTE-A network to deal with devices (H2H, M2Mp , M2Mwp ) that present different priority levels. When compared with one another, our Proportional approach presents a performance better than our Shapley approach for H2H. As a future work we aim to improve the strategy used to define the vector of claims. R EFERENCES
Fig. 2. Access Delay - H2H Devices
2) Access Delay: The access delay grows with the number of devices, as depicted in Fig. 2. Among the three simulated strategies, the Shapley and Fixed approaches present better results than the Proportional approach. Nevertheless, it is important to notice that in the worst case the access delay presented by the Proportional approach is below 0.02 s. 3) Preambles Transmission: The overall number of preambles sent by each approach is shown in Fig. 3. For the scenario with 3050 devices, the Proportional approach presents the worst result, where approximately 450 preambles are sent. Better results are presented by the Fixed and Shapley strategies, where around 290 and 300 preambles are sent, respectively. With exception to the first scenarios, where there are less than 500 devices requesting access, the improvement in the Proportional approach presented by the Shapley and
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