Number Of Machine Type Communication Research Articles

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

AbstractThe exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.

A DNN-based estimate of the PRACH traffic load for massive IoT scenarios in 5G networks and beyond

The massive Internet of Things (IoT) scenario refers to a huge number of Machine Type Communications (MTC) characterized by sporadic transmissions of small-sized packets. In order to manage the uplink radio resource allocation in cellular networks, several access schemes have been proposed. These schemes are mainly based on a grant-based Random Access (RA) procedure and proper load-aware access controls, e.g., Access Class Barring (ACB) techniques, dynamic uplink radio resource allocation, and so on. The development of an efficient approach to estimate the traffic load is extremely important for the proper functioning of these access schemes. With the ever-increasing number of transmitting MTC devices, expected with Beyond 5G (B5G) and 6G networks, additional challenges to obtain a correct real-time traffic load estimation are posed. Deep learning techniques, in this context, offer learning ability and optimization capability to properly support this scenario. In this paper, we propose a current access attempts estimation, based on Deep Neural Network (DNN), which accepts as input only the information really available at the next generation Node B (gNB). The network was trained and tested with a dataset properly created and composed by more than 21 million points. The DNN-based traffic load estimation method is then compared with other benchmark schemes available in the literature, in terms of regression accuracy, both in a static analysis, which considers a stand-alone RA cycle, and through a long-term analysis with a time-varying offered load. The latter analysis was performed both by adopting a theoretical arrival process proposed by 3GPP, and by using traces of real traffic data.

Improved Resource Allocation in 5G MTC Networks

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices’ wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.

MIMO receivers considering preamble collisions for grant-free random access in machine type communication systems

One of the main requirements for next generation mobile or wireless communication systems is to effectively support a large number of machine type communication (MTC) devices for internet of things applications. In this paper, we consider grant-free random accesses from MTC devices, where a small-sized data can be transmitted concurrently with a preamble randomly selected from a pool of preambles. In order to support a huge number of MTC devices, large-scale multiple-input multiple-output (MIMO) techniques can be used. Concurrent transmissions from multiple devices to a single time–frequency resource can be handled by a large number of antennas at a base station if the number of preambles is considerably large compared to the number of the concurrent accesses. However, in practice, the number of preambles is limited and preamble collisions can deteriorate the reception performance. In this paper, we propose a MIMO receiver scheme for grant-free random access with taking care of preamble collisions. The proposed scheme attempts to decode all received packets even with preamble collisions, by exploiting the capture effect and interference cancellation techniques.

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.

Efficient Cluster-Based Sleep Scheduling for M2M Communication Network

Machine-to-machine (M2M) communication networks comprise a large number of machine-type communication (MTC) devices such as sensors, radio frequency identification readers, and smart meters. Thus, M2M networks are becoming popular in real-time monitoring, surveillance, and security applications. Scheduling the active and idle states of MTC devices is significantly important to achieve a longer network lifetime and reduce collision during data transmission. Existing node sleep scheduling schemes are mainly designed to reduce the energy consumption of MTC devices. These schemes do not support mobility of MTC devices and thus cannot be used in mobility-centric M2M applications. Thus, we propose a cluster-based energy-efficient, mobility-centric node scheduling scheme for M2M (CENM) communication networks. The proposed CENM scheduling scheme provides (i) network coverage by keeping a minimum number of MTC devices in active state and (ii) fault tolerance by selecting alternative cluster heads and member nodes. Simulation results show that the CENM scheduling scheme is more reliable than the existing CCNS scheduling scheme since the first node in CENM scheme fails much later than that in CCNS scheduling scheme. Simulation results also show that the CENM scheduling scheme outperforms the CCNS, LEACH-M and LEACH-ME scheduling schemes in terms of network energy consumptions, network lifetime, and total message transmissions.

Access Authentication of Mass Device Connections for MTC in LTE Networks

Machine Type Communication, also called Machine to Machine communication, is one of the next generations of sophisticated techniques for future wireless communications. It is attracting tremendous interest in standardization organizations such as the third Generation Partnership Project. Accommodating data and signaling traffic from massive Machine Type Communication devices is a challenging task to ensure the security of Machine Type Communication applications in Long Term Evolution networks, as the number of Machine Type Communication devices continues to increase. Since each Machine Type Communication device has to implement an independent access authentication procedure specified by the current third Generation Partnership Project standard, it will incur severe signaling congestions in the Long Term Evolution networks when a large number of Machine Type Communication devices simultaneously connect to the network. In this paper, we propose a new access authentication and key agreement scheme based on an aggregate signature to avoid signaling overload in Long Term Evolution network elements. Our scheme can greatly reduce the signal traffic for a multitude of Machine Type Communication devices connected to the network and also provide robust security protection with desirable efficiency. The experimental results and formal verification using the AVISPA and SPAN tools show that the proposed scheme is efficient and secure against various malicious attacks.

On the Achievable User Number of the Downlinks in Cellular-Based Machine-to-Machine Communications

In cellular-based machine-to-machine (M2M) networks, supporting large number of machine-type communications (MTC) users (devices) has become a critical challenge in both the uplink and downlink channels. In this paper, we focus on the downlink beamforming using zero-forcing dirty paper coding (DPC). In order to characterize the system's ability of user admission, we consider the achievable user number, which is defined as the number of users whose signal-to-interference plus-noise ratios exceed a target threshold. Due to the complexity of the optimal scheme, we propose two algorithms with random user scheduling and greedy user scheduling in maximizing the achievable user number by dynamical power assignment. Using the joint distribution of effective channel gains, we derive the achievable user number of both the scheduling schemes. An upper bound on the achievable user number of the greedy scheme is then derived which is shown to be tight when there are a large number of users. From numerical results, we show that both of the schemes enjoy a linear increase in the achievable user number as the number of transmitter antennas increases. The performance of the greedy scheduling scheme is close to the optimal scheduling scheme.