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.
From another view, the works in @cite_21 @cite_1 aim to increase resource utilization considering the coarse granularity of resources allocated for individual node. The authors in @cite_21 propose an adaptive SPS scheme to adjust the resources allocated in the next transmission via buffer reports. To further utilize unused resources, resource delegation as a promising technique can increase overall throughput by leveraging from D2D @cite_1 , where partially unused scheduling grants that were originally assigned in a semi-persistent manner could be granted to other devices. However, additional access delays may be brought in with extra D2D and buffer information exchange. Moreover, due to the small data transmissions feature in factory, BS can support an unique small resource size for MTC devices so that packets can be transmitted within a RB @cite_4 .
@cite_21 @cite_1 @cite_4
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We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users, a goal requiring load balancing among multiple cells. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level cell coverage based on load, throughput, and channel measurements. In this framework, an opportunistic scheduling algorithm--the weighted Alpha-Rule--exploits the gain of multiuser diversity in each cell independently, trading aggregate (mean) down-link throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility or their arrival and departure, by initiating load-aware handoff and cell-site selection. The central server adjusts schedulers of all cells to coordinate their coverage by prompting cell breathing or distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination. Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed weighted Alpha-Rule scheduling, the load-aware MS handoff cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve global resource utilization and load balancing, resulting in a smaller blocking rate of MS arrivals without extra resources while the aggregate throughput remains roughly the same or improved at the hot-spots. Our simulation tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both the system dimension and the size of MS population. Coverage enhancement and low complexity are quite important aspects to support and broaden the application of machine type communication (MTC) in long term evolution (LTE). Repeating the same data across multiple subframes is widely considered as basic solution to improve the coverage. Low complexity target is mainly achieved by reducing the supported bandwidth and restricting the data processing size. Repetitions can be regarded as a new dimension of link adaptation besides the modulation and coding scheme (MCS) selection and the low complexity features are new factors impacting the link adaption as well. However, existing link adaptation mechanism in LTE system is unable to support such two-dimension link adaptation. Straightforward method suffers serious spectral efficiency loss and more power consumption and there is little other research on this issue, either. To save power and improve the spectral efficiency when performing the link adaptation for MTC, a smart method of coordinating the MCS selection and the repetition level determination with the consideration of new low complexity features is proposed in this paper. Extensive link level simulations are carried out to verify the effectiveness of proposed method. Simulation results show that proposed method could improve the spectral efficiency by around 42.1 and reduce the power consumption by around 36.8 In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. Nowadays, Voice of LTE (VoLTE) is used to support voice communications in LTE network. As one of the key techniques for VoLTE, semi-persistent scheduling (SPS) is important to improve the capacity of VoLTE users. However with SPS, the resource and modulation and coding scheme (MCS) are usually decided by the initial scheduling and keep unchanged during the active state. Considering the continuously changing channel, it is hard to ensure the performance of the voice packet. In order to solve this problem, a resource allocation algorithm is proposed based on the prediction of channel quality change. Furthermore, the data rate of Adaptive multi Rate(AMR) codec is adaptively adjusted in the resource allocation algorithm to reduce VoLTE's impact on other data services. Simulation results prove that the proposed algorithm can reduce the retransmission of VoLTE users and improve the throughput performance of other traffic businesses.