Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

In this paper, we propose a power back-off based non-orthogonal random access (NORA) scheme for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PA), multiple machine-type communication devices (MTCD) choosing the same PA can be distinguished and regarded as a non- orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel (PUSCH) for transmissions by multiplexing in power domain. Then, we adopt the Sukhatme's classic theory and characteristic function to formulate the optimization problem that aims at maximizing the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. By using the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. We further adjust the access class barring (ACB) factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity solution is also developed, which can achieve near PSO-based performance under high data rate requirement. Simulation results show that our proposed scheme can efficiently improve the network performance as compared with the existing schemes.