Energy-Efficient Power Allocation in URLLC Enabled Wireless Control for Factory Automation Applications

The coming fifth-generation (5G) cellular networks encourage to support several innovations and services, some of which will demand Ultra-reliable and Low-latency Communications (URLLC). For instance, URLLC can support real-time control to facilitate several emerging applications, such as robotic arms in industrial applications, and remote surgery for healthcare applications. However, URLLC is expected to be supported without considering the resources usage efficiency in wireless control systems due to the challenging to satisfy Quality of Service (QoS) requirements at the expense of diminishing energy efficiency. In this paper, we analyze uplink energy efficiency in URLLC utilizing multiple antennas in the transmitter and the receiver as well (MIMO) in real-time wireless control systems. We firstly formulate an optimization problem to maximize energy efficiency concerning the effect of the control convergence rate constraint. Then, we develop an exhaustive search method to obtain the maximum energy efficiency. Finally, simulation results are provided to demonstrate the performance of our proposed method

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Dynamic communication QoS design for real-time wireless control systems

In the coming fifth-generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) is treated as an indispensable service to enable real-time wireless control systems. However, the extremely high quality-of-service (QoS) in URLLC causes significant wireless resource consumption. Moreover, to obtain good control performance may not always require extremely high communication QoS. In this paper, we propose a communication-control co-design scheme to reduce wireless resource consumption, where we obtain a dynamic communication QoS design method to reduce the energy consumption by jointly using extremely high QoS and a relatively low QoS. In this scheme, we first explore the control process served by different communication QoS levels and find that the whole control process can be divided into two phases, where different QoS levels have their advantages in different phases. Then, we obtain a threshold to decide when the extremely high QoS or relatively low QoS should be provided by communications. Simulation results demonstrate that our method can effectively reduce communication energy consumption while maintaining good control performance

Optimized resource allocation techniques for critical machine-type communications in mixed LTE networks

To implement the revolutionary Internet of Things (IoT) paradigm, the evolution of the communication networks to incorporate machine-type communications (MTC), in addition to conventional human-type communications (HTC) has become inevitable. Critical MTC, in contrast to massive MTC, represents that type of communications that requires high network availability, ultra-high reliability, very low latency, and high security, to enable what is known as mission-critical IoT. Due to the fact that cellular networks are considered one of the most promising wireless technologies to serve critical MTC, the International Telecommunication Union (ITU) targets critical MTC as a major use case, along with the enhanced mobile broadband (eMBB) and massive MTC, in the design of the upcoming generation of cellular networks. Therefore, the Third Generation Partnership Project (3GPP) is evolving the current Long-Term Evolution (LTE) standard to efficiently serve critical MTC to fulfill the fifth-generation (5G) requirements using the evolved LTE (eLTE) in addition to the new radio (NR). In this regard, 3GPP has introduced several enhancements in the latest releases to support critical MTC in LTE, which is designed mainly for HTC. However, guaranteeing stringent quality-of-service (QoS) for critical MTC while not sacrificing that of conventional HTC is a challenging task from the radio resource management perspective. In this dissertation, we optimize the resource allocation and scheduling process for critical MTC in mixed LTE networks in different operational and implementation cases. We target maximizing the overall system utility while providing accurate guarantees for the QoS requirements of critical MTC, through a cross-layer design, and that of HTC as well. For this purpose, we utilize advanced techniques from the queueing theory and mathematical optimization. In addition, we adopt heuristic approaches and matching-based techniques to design computationally-efficient resource allocation schemes to be used in practice. In this regard, we analyze the proposed methods from a practical perspective. Furthermore, we run extensive simulations to evaluate the performance of the proposed techniques, validate the theoretical analysis, and compare the performance with other schemes. The simulation results reveal a close-to-optimal performance for the proposed algorithms while outperforming other techniques from the literature

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早大学位記番号:新8550早稲田大