Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource per-allocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.
5G has received increasing attention in industrial automation due to deterministic medium access. To provide critical QoS guarantees based on SPS, a series of massive access management (MAM) based on clustering are proposed in @cite_4 @cite_13 @cite_2 . MTC devices in the same cluster are only allowed to access within an allocated access grant time interval (AGTI). @cite_4 , the service rate of AGTIs is computed to ensure enormous QoS characteristics of different constant-rate MTC clusters. To guarantee statistical delay requests, a scheme is proposed in @cite_13 to get the minimum constant service rate through effective bandwidth. Due to the unpredictable nature of sporadic traffic in factory, an adaptive MAM in @cite_2 utilizes historical observations to allocate AGTIs thus avoiding any reliance on prior stochastic assumptions. However, previous AGTI-based works overlook the fact that AGTIs are too scarce to allocate for each device. Also, we cannot ensure that devices in a cluster will be triggered concurrently within an AGTI.
@cite_13 @cite_4 @cite_2
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Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. With the fast development of Internet of Things (IoT), the fifth generation (5G) wireless networks need to provide massive connectivity of IoT devices and meet the demand for low latency. To satisfy these requirements, nonorthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. In parallel with the development of NOMA techniques, mobile edge computing (MEC) is becoming one of the key emerging technologies to reduce the latency and improve the quality of service (QoS) for 5G networks. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing aware NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users’ uplink energy consumption. To this end, we formulate an NOMA-based optimization framework which minimizes the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit powers. In particular, similar to frequency resource blocks (RBs), we divide the computing capacity available at the cloudlet to computing RBs. Accordingly, we explore the joint allocation of the frequency and computing RBs to the users that are assigned to different order indices within the NOMA clusters. We also design an efficient heuristic algorithm for user clustering and RBs allocation, and formulate a convex optimization problem for the power control to be solved independently per NOMA cluster. The performance of the proposed NOMA scheme is evaluated via simulations. Future 5G cellular networks supporting delay-sensitive, low-latency communications could employ random access communication to reduce the overhead compared to scheduled access techniques used in 4G networks. We consider a wireless communication system where multiple devices transmit payloads of a given fixed size in a random access fashion over shared radio resources to a common receiver. We allow retransmissions and assume Chase combining at the receiver. The radio resources are partitioned in the time and frequency dimensions, and we determine the optimal partition granularity to maximize throughput, subject to given constraints on latency and outage. In the regime of high and low signal-to-noise ratio (SNR), we derive explicit expressions for the granularity and throughput, first using a Shannon capacity approximation and then using finite block length analysis. Numerical results show that the throughput scaling results are applicable over a range of SNRs. The proposed analytical framework can provide insights for resource allocation strategies in reliable and delay-sensitive random access systems and in specific 5G use cases for massive, short packet uplink access. We propose a novel approach to QoS for real-time traffic over wireless mesh networks, in which application layer characteristics are exploited or shaped in the design of medium access control. Specifically, we consider the problem of efficiently supporting a mix of Voice over IP (VoIP) and delay-insensitive traffic, assuming a narrowband physical layer with CSMA CA capabilities. The VoIP call carrying capacity of wireless mesh networks based on classical CSMA CA (e.g., the IEEE 802.11 standard) is low compared to the raw available bandwidth, due to lack of bandwidth and delay guarantees. Time Division Multiplexing (TDM) could potentially provide such guarantees, but it requires fine-grained network-wide synchronization and scheduling, which are difficult to implement. In this paper, we introduce Sticky CSMA CA, a new medium access mechanism that provides TDM-like performance to real-time flows without requiring explicit synchronization. We exploit the natural periodicity of VoIP flows to obtain implicit synchronization and multiplexing gains. Nodes monitor the medium using the standard CSMA CA mechanism, except that they remember the recent history of activity in the medium. A newly arriving VoIP flow uses this information to grab the medium at the first available opportunity, and then sticks to a periodic schedule, providing delay and bandwidth guarantees. Delay-insensitive traffic fills the gaps left by the real-time flows using novel contention mechanisms to ensure efficient use of the leftover bandwidth. Large gains over IEEE 802.11 networks are demonstrated in terms of increased voice call carrying capacity (more than 100 in some cases). We briefly discuss extensions of these ideas to a broader class of real-time applications, in which artificially imposing periodicity (or some other form of regularity) at the application layer can lead to significant enhancements of QoS due to improved medium access.