1. Introduction
Machine-type communications (MTC), also known as machine-to-machine (M2M) communications, is an emerging technology that boosts the development of the Internet of Things (IoT) by providing ubiquitous connectivity and services [
1,
2]. Due to the diverse set of MTC applications and services [
3,
4], the current view on the 5G wireless system categorizes MTC into two: (1) massive MTC (mMTC) supplying a massive number of low-data rate and low-cost devices and (2) ultra-reliable low-latency MTC (uMTC) supporting message transmission with high reliability and low latency [
5]. According to Machina Research [
6], the number of machine-type communications devices (MTCDs) will reach 27 billion in 2024. Each MTCD performs a random access (RA) procedure for initial uplink access to connect and synchronize with its base station (BS) [
7]. However, a large number of MTCDs may be triggered and attempt to access the BS within a relatively short time, which leads to severe RA congestion, high power consumption, unexpected delay, and radio resource wastage [
8]. Therefore, alleviating RA congestion and supporting massive-connections have been deemed as the most important for IoT in the 5G network [
9].
There have been some solutions addressing the RA congestion problem. Access class barring (ACB) scheme is regarded as an efficient approach to control traffic load [
10,
11,
12,
13,
14]. The study in [
10,
11] investigated an analytical model and simulation model to evaluate the performance of the ACB scheme. The authors in [
12] proposed a dynamic ACB method to increase the access success probability. In [
13], the authors optimized ACB factor and uniform backoff window size to improve the success access. The authors in [
14] proposed a learning automata-based ACB scheme to control the massive M2M traffic under the interference of human-to-human (H2H) traffic. Reference [
15,
16,
17,
18,
19,
20,
21] tackled the RA congestion problem by efficiently utilizing the radio resources. In [
15], the authors proposed a novel pipelined contention resolution scheme based on distributed queuing to improve the utilization of preamble resources. In [
16], a collision-aware resource access scheme was proposed to reduce the collisions on the granted physical resource blocks (PRBs). In [
17], an enhanced spatial group-based RA scheme was presented to reduce collision probability. The authors in [
18] proposed a dynamic preamble grouping strategy based on delay-sensitive characteristics. While in [
19], the authors proposed an optimal scheme to dynamically adjust the number of random access channel resources between delay-sensitive devices and delay-tolerant ones. In [
20], an adaptive resource allocation scheme was introduced to allocate different amounts of random access resources to M2M applications with distinct delay requirements. Reference [
21] considered the resource allocation between physical random access channel (PRACH) and physical uplink shared channel (PUSCH) for orthogonal random access and data transmission to resolve the congestion problem in the RA procedure.
However, in the aforementioned schemes, only one MTCD was allowed to use the scheduled data channel, otherwise, the RA procedure failed. The objective of high spectral efficiency and massive connectivity needs to be further improved [
22]. As one of the key technologies for 5G cellular networks, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) technique permits multiple users sharing the same radio resources, which could achieve high spectral efficiency and massive connectivity compared with orthogonal multiple access (OMA) [
23,
24]. There are also some studies on the usage of M2M communications in NOMA systems [
25,
26,
27,
28,
29,
30,
31,
32,
33]. The works in [
25,
26,
27,
28] targeted sum rate maximization, and the energy efficiency maximization of NOMA for M2M communications were studied in [
29,
30,
31,
32,
33]. For a given set of NOMA clusters, standard convex optimization [
29], difference of convex programming [
30], and Lagrange duality methods [
25,
31,
32,
33] were employed, respectively, for power control. User clustering is also a key factor on the performance of uplink NOMA. In [
25], the users were grouped into the clusters based on the difference of channel conditions between users. In [
26], a novel MTCD pairing scheme was introduced based upon the distance between the BS and the MTCDs. In [
27], each strong channel gain device was allocated to the appropriate cluster as a cluster head. In [
28], location-based schemes were proposed to place MTCDs in a cluster.
The aforementioned studies mainly focus on the theoretical analysis of the power allocation scheme in the data transmission process, and few studies have considered the realization of NOMA for alleviating the RA congestion problem. Motivated by the idea of NOMA and SIC, the authors in [
34] proposed a SIC-based non-orthogonal random access (NORA) scheme to alleviate the access congestion problem, in which multiple users can transmit preamble on the same RBs. Based on [
34], the authors in [
35] proposed a resource allocation scheme, which can allocate uplink resources between PRACH and PUSCH reasonably for non-orthogonal random access and data transmission (NORA-DT). While the total number of RBs allocated to PRACH and PUSCH was fixed, even the MTCDs may not be able to process over the whole RBs. In [
34,
35], the BS utilized the difference of time of arrival to identify collided MTCDs with the identical preamble and performed SIC based on the channel conditions obtained through preamble detection. As the number of collided MTCDs at the BS increases, both the preamble collision detection and the user separation complexity increase. In addition, the power control schemes in [
34,
35] just ensured that collided MTCDs have diverse arrived power, while the limited energy of MTCDs was not considered.
Given an SIC receiver cannot perfectly cancel co-channel interferences, the performance of NOMA will be degraded, and NOMA may not satisfy the quality of service (QoS) of user equipment in some scenarios, while OMA techniques can significantly reduce the inter-user interference. Hence, in a practical scenario, it becomes more beneficial to consider a combination of NOMA and the existing access technology, for example, orthogonal frequency division multiple access (OFDMA), to support a larger number of MTCDs. However, a combination of NOMA and OFDMA for M2M communication over cellular networks faces some challenges. One of these challenges is the management of uplink radio resources. Therefore, we propose a traffic-aware resource allocation scheme for hybrid NOMA-OFDMA based cellular M2M communications. The main contribution of this work are listed below:
We propose a NOMA-based congestion-alleviating access scheme (NCAS) to improve the access capacity and resource efficiency accompanied with OFDMA-based congestion-alleviating access scheme (OCAS) for the hybrid NOMA-OFDMA based cellular M2M communication systems. The MTCDs in NCAS and OCAS are allowed to send data with optimal power allocation solution right after preamble transmission without explicitly establishing a connection, which could reduce the scheduling signaling overhead and simplify the access process. Different from OCAS, a device called cluster head (CH) transmit preambles in NCAS, so that the number of MTCDs that directly transmit preambles to the BS can be greatly reduced.
We propose a traffic-aware resource blocks (RBs) allocation scheme consisting of two sub-problems for the hybrid multiple access systems. The first sub-problem is used to optimize RBs allocation between PRACH and PUSCH given the sum of RBs allocated to PRACH and PUSCH for NCAS, while the second one optimizes the RBs allocation between NCAS and OCAS. NOMA based MTCDs and OFDMA based MTCDs, respectively, compete for uplink resources allocated for NCAS and OCAS to transmit preamble and data packets. To alleviate the RA congestion problem, the number of MTCDs that compete for uplink resources allocated for NCAS and OCAS are restricted by the traffic-aware access barring schemes.
We formulate an energy efficiency maximization problem such that the MTCDs’ power allocation can be optimized under the maximum transmit power constraint and QoS requirement of the MTCD. The feasibility conditions of the power allocation solution are determined as linear constraints of the CH. The original non-convex optimization problem is transformed into the pseudo-concave function of the CH by using the difference of received power. Then the transformed problem is solved under the feasibility conditions by two iterative algorithms. The first algorithm is used to optimize the energy efficiency by using the Dinkelbach method, while the second one optimizes the power allocation solution under the energy efficiency optimized in the first algorithm.
We design a device clustering scheme to group devices into different clusters. The proposed scheme provides the range of channel gain differences as the condition of grouping, which exploits the channel gain and maximum transmit power constraint of the devices. If the channel gain differences among devices can satisfy the condition of grouping, then these devices are treated as NOMA based MTCDs, which compete for uplink resources allocated for NCAS. Otherwise, these devices are treated as OFDMA based MTCDs, which compete for uplink resources allocated for OCAS.
We evaluate the energy efficiency and access capacity performance of the resource allocation for the hybrid NOMA-OFDMA based cellular M2M communication systems. Simulation results show that the proposed resource allocation scheme can efficiently improve the system access capacity and energy efficiency compared with NORA-DT.
The rest of this paper is organized as follows. The system model and NOMA based congestion-alleviating access scheme is presented in
Section 2. The optimization of the number of RBs for the hybrid NOMA-OFDMA based M2M communication systems is focused in
Section 3. The channel model and problem formulation is proposed in
Section 4. The power allocation for the hybrid NOMA-OFDMA based M2M communications is solved in
Section 5. The MTCD clustering algorithm is presented in
Section 6. Numerical results are provided in
Section 7, and concluding remarks are given in
Section 8.
Notations: Lowercase boldface letters denote vectors. denotes the absolute value. is reserved for complexity estimates. denotes that component x is not included in the set , and denotes the m-th element of set .
7. Performance Evaluation
In this section, we present the energy efficiency and access capacity performance of the resource allocation for the hybrid NOMA-OFDMA based cellular M2M communication systems. The average EE is quantitatively measured by the bits of information reliably transferred to a receiver per unit consumed energy per unit bandwidth at the transmitter. For simulation, the radius of BS is 1 km.
, and
, where
v is the speed of light and
. The values of the main simulation parameters are summarized in
Table 1.
Figure 3 presents the comparison between
and
for a different number of NOMA clusters.
is the sum of RBs allocated to PRACH and PUSCH for NCAS, and
is the number of RBs allocated to PRACH for NCAS. As
Figure 3 shows,
is 24, 36, 42, 48, 54, 60 and
is 12, 18, 24, 24, 24, 24 when the number of NOMA CHs belongs to
,
,
,
,
,
, respectively. The reason is as follows:
(1) Given , we can get the traffic load ranges , , , , by Algorithm 1. The optimal number of RBs allocated to PRACH is 48, 42, 36, 30, 24 corresponding to different traffic load range.
(2) Given , we can get the traffic load ranges , , , by Algorithm 1. The optimal number of RBs allocated to PRACH is 42, 36, 30, 24 corresponding to different traffic load range.
(3) Given , we can get the traffic load ranges , , , by Algorithm 1. The optimal number of RBs allocated to PRACH is 36, 30, 24, 18 corresponding to different traffic load range.
By analogy, we can also get the traffic load intervals and corresponding number of RBs allocated to PRACH when is 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1. By Algorithm 2, we can get the optimal RBs allocation factor through comparing the performance of access capacity for different . Then the optimal number of RBs allocated for NCAS can be obtained by , and the optimal number of RBs allocated to PRACH can be obtained by for different number of NOMA clusters.
Figure 4 shows the comparison of the number of success clusters among NORA-DT and NCAS for different values of NOMA clusters. In NORA-DT, the total number of RBs allocated to PRACH and PUSCH was fixed even though the MTCDs may not be able to process over the whole RBs. While in the hybrid NOMA-OFDMA systems, the total number of RBs allocated for NCAS can dynamically change according to the number of NOMA clusters. By Algorithm 2, we can get the optimal number of RBs allocated for NCAS (i.e.,
). As
Figure 4 shows, the optimal
is 0.4, 0.6, 0.7, 0.8, 0.9, 1 when the number of NOMA clusters belongs to
,
,
,
,
,
, respectively. It means that 40%, 60%, 70%, 80%, 90%, 100% of uplink available RBs are allocated to NCAS for different values of NOMA clusters. We can observe that the number of success clusters in NCAS is similar to that for NORA-DT, which proves the feasibility of Algorithm 2.
Figure 5 shows the comparison of the access capacity among the hybrid NOMA-OFDMA, NCAS, and OCAS for different number of NOMA CHs. With the increase of the number of NOMA CHs, the access capacity in NCAS drastically increases. From
Figure 3, we can know
is 24, 36, 42, 48, 54, 60 when the number of NOMA CHs belongs to
,
,
,
,
,
, respectively. Since the NOMA based MTCDs are not able to process over the whole available RBs. Therefore, the remaining RBs can be provided for OFDMA based MTCDs. We can get the number of RBs allocated for OCAS, denoted by
, is 36, 24, 18, 12, 6, 0 as the number of NOMA CHs belongs to
,
,
,
,
,
, respectively. The maximum number of OFDMA based MTCDs allowed to access the network and the optimal number of PRACH RBs given
are also obtained by running the proposed Algorithm 1. Then we can get the maximum access capacity supported in OCAS. As shown in
Figure 5, the access capacity in OCAS is non-increasing since the number of RBs allocated for OCAS is reduced for a different range of the number of NOMA CHs. Furthermore, it can be seen that the access capacity in hybrid NOMA-OFDMA is improved compared with that in NCAS until the number of NOMA CHs exceeds 28.
Figure 6 shows the comparison of the access capacity among the hybrid NOMA-OFDMA, NCAS, and OCAS for a different number of OFDMA based MTCDs. With the increase of the number of OFDMA based MTCDs, the access capacity in OCAS drastically increases.
is 24, 36, 42, 48, 54, 60 when the number of OFDMA based MTCDs belongs to
,
,
,
,
,
, respectively. Since the OFDMA based MTCDs are not able to process over the whole available RBs, the remaining RBs can be provided for NOMA based MTCDs. The number of RBs allocated for NCAS is 36, 24, 18, 12, 6, 0 as the number of OFDMA based MTCDs belongs to
,
,
,
,
,
, respectively. The optimal number of NOMA CHs and the optimal number of PRACH RBs given
are obtained by running the proposed Algorithm 1. As shown in
Figure 5, the access capacity in NCAS is non-increasing. Furthermore, we can observe that the access capacity in hybrid NOMA-OFDMA in
Figure 6 is less than that in
Figure 5. Therefore, in the proposed RBs allocation scheme, sufficient resources allocated for NCAS is ensured compared to OCAS since NOMA can support more MTCDs than OCAS.
Figure 7 shows the comparison of the access capacity among the hybrid NOMA-OFDMA, NCAS, and OCAS,
, 5, and 7 dB. The number
U of uniformly distributed MTCDs is 60. With the increase of the SNR of CH, the access capacity of NCAS first increases then achieves a stable level, while the access capacity of OCAS drops greatly. It means that NCAS can effectively improve the access capacity by supporting more and more NOMA clusters, which contributes the most compared with OCAS. The performance of the hybrid NOMA-OFDMA is obviously better than the performance of NCAS with a lower SNR. We can observe that the performance gaps between the hybrid NOMA-OFDMA and NCAS are lower when the SNR of CH is increasing. The performance gap between the hybrid NOMA-OFDMA and NCAS stands at about 40.7% when the SNR of CH is 0 dB and dramatically deteriorates to 6.9% when the SNR of CH is 8 dB. When the SNR of NOMA CH exceeds 12 dB, the performance of the hybrid NOMA-OFDMA and NCAS are completely closed. That is, the access capacity is mainly contributed by NCAS. As the SNR of CH increases, by decreasing
, the access capacity of NCAS increases, while the access capacity of OCAS decreases. Furthermore, the access capacity of the hybrid NOMA-OFDMA can reach at least 50 for different values of SNR of CH, accounting for 84% of all MTCDs.
Figure 8 shows the comparison among
,
,
, and
for different values of the SNR of NOMA CH,
dB,
.
is the number of RBs allocated to PRACH for OCAS. We can observe that with the increase of the SNR of NOMA CH,
and
gradually increase, while
and
gradually drop. This is because as the SNR of NOMA CH increases, the number of MTCDs that compete for uplink resources allocated for OCAS drops greatly, while the number of MTCDs that compete for uplink resources allocated for NCAS increases. Therefore, the more the resources allocated to NCAS, the less the resources are available for OCAS. In addition,
as the SNR of NOMA CH exceeds 12 dB, and
is relatively small. This is because the number of MTCDs that compete for uplink resources allocated for OCAS is relatively small as the SNR of NOMA CH exceeds 12 dB.
Figure 9 shows the comparison of average EE among the hybrid NOMA-OFDMA, NCAS, and OCAS for different values of the SNR of CH,
, 5, and 7 dB,
. As seen in the figure, as the SNR of NOMA CH increases, the performance of average EE in OCAS decreases, and the average EE of NCAS first increases to its maximum and then decreases. However, OCAS plays a key role compared with NCAS when the SNR of NOMA CH is small. While with the increase of the SNR of NOMA CH, NCAS plays a key role, and we also observe that when the SNR of NOMA CH is small, by increasing
, the performance of the average EE decreases. When the value of the SNR of NOMA CH becomes larger, by increasing
, the performance of average EE increases. The reason is that for uplink, the power allocation strategy needs to ensure the difference of received power at BS among multiplex MTCDs. As the difference (i.e.,
) increases, the accuracy of SIC in decoding multiple MTCDs increases.
Figure 10 compares the achievable sum-rate of the proposed algorithm and the sub-optimal user clustering algorithm [
25], the greedy algorithm (GA) based user grouping [
42], random user grouping algorithm [
43]. As can be seen from
Figure 10, with the increase of the SNR of NOMA CH, the performance of the achievable sum-rate increases for all algorithms. In addition, we can notice that the performance of the proposed algorithm outperforms that of the reference [
25,
42,
43] with different SNR of NOMA CH. Although the principle of reference [
25,
42,
43] algorithms is to keep the difference of channel conditions between users in the group, the reference [
25,
42,
43] algorithms cannot promise enough channel difference, which degrades the SIC decoding performance and the achievable sum-rate performance. While in the proposed algorithm, the range of channel gain differences is provided as the condition of grouping, which exploits the channel gain and maximum transmit power constraint of the devices. If the channel gain differences among devices can satisfy the condition of grouping, then these devices can be divided into the same cluster. In addition, when the achievable sum-rate of the proposed algorithm is the same as reference [
25,
42,
43] algorithms (taking 150 as a target achievable sum-rate), the SNR of NOMA CH of the proposed algorithm is smaller than that of the reference [
25,
42,
43] algorithms. Based on Proposition 3, the SNR of NOMA CH is derived as
dB. As
decreases, the SNR of NOMA CH decreases. Therefore, the proposed algorithm can realize the same achievable sum-rate of the reference [
25,
42,
43] algorithms at a smaller transmission power.
Figure 11 shows the comparison of average EE of the proposed algorithm and the sub-optimal user clustering algorithm [
25], the GA based user grouping [
42], random user grouping algorithm [
43]. As can be seen from
Figure 11, the performance of the proposed algorithm is improved comparing with the performance of reference [
25,
42,
43] algorithms. Both the proposed method and reference [
25,
42,
43] algorithms have the maximum energy efficiency with a lower SNR of NOMA CH, and the energy efficiency decrease at a higher SNR of NOMA CH. The reason for the decrease of average EE is that with the increase of the SNR of NOMA CH, user grouping and power allocation strategy are more difficult to meet the minimum data rate requirements and the maximum transmission power of all multiplexing user. From
Figure 10, we can get the SNR of NOMA CH of the proposed algorithm and reference [
25,
42,
43] algorithms when the target achievable sum-rate is 150. Then from
Figure 11, the average EE of the proposed algorithm and reference [
25,
42,
43] algorithms can be obtained under the target achievable sum-rate. It can be seen that the performance of the proposed algorithm is obviously better than the performance of reference [
25,
42,
43] algorithms.