Dynamic Scheduling Algorithm for Delay-Sensitive Vehicular Safety Applications in Cellular Network

Abstract

The vehicular safety applications disseminate the burst messages during an emergency scenario, but effort to reduce delay of communication are hampered by wireless access technology. As conventional VANET (Vehicular ad-hoc network) connected intermittently, the LTE-based framework has been established for the vehicular communication environment. However, resource allocation which affected by many factors, such as power, PRB (physical resource block), channel quality, are challenging to guarantee the safety services QoS in LTE downlink for OFDM. In order to solve the problem of safety message dissemination in LTE vehicular network, we proposed a delay-aware control policy by leveraging cross-layer approach to maximize the system throughput. First, we model the resource allocation problem using the queuing theory based on the MISO. Second, the method casts the problem of throughput and latency for dynamic communication system into a stochastic network optimization problem, and then makes tradeoffs between them by Lyapunov optimization technique. Finally, we use the improved the branch and bound algorithm to search optimal solution in system capacity region for these decomposed subproblems. The simulation results show that our algorithm can guarantee the delay while maximum system throughput

Minimal Delay Violation-Based Cross-Layer Scheduler and Resource Allocation for DSL Networks

“…In [1] a cross-layer scheduling algorithm is developed that minimizes the delay in vehicular networks. It adds a parameter V that allows for a trade-off between throughput and latency.…”

“…A taxonomy of cross-layer schedulers can be found in [17]. Some of the schedulers listed above are also used in the simulations in Section VI and are listed in Table 3 on Page 11 together with the expression used by each scheduler to calculate the user weights ω in the weighted sum rate maximization problem (1).…”

“…The HOL is the time spent in the system by the packet at the head of the queue, and is denoted by Γ n i (t). Every flow has a utility function u n i (R n i , S n i (t)), which quantifies the usefulness to the flow of receiving a service rate R n i , given state S n i (t) 1 . At the start of slot t the cross-…”

“…A cross-layer scheduler then translates the upper layer preference information into utility functions u n that express the usefulness to user n of receiving a service rate R n . There are many available cross-layer schedulers that focus on wireless networks, and optimize in function of different metrics such as delay [1] or power usage [2], or joint optimizing of several metrics [3]. These schedulers assume that the solution to the (1) can be calculated and applied immediately.…”

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

“…UPMSPs are employed in various applications, such as various manufacturing industries, including food processing plants, and car factories [10], semiconductor [11,12], tobacco [13], textile [14], petroleum [15], and tire [16]. Additionally, they have been applied in multiprocessor computer [17][18][19], for multithreading [20], in hospital operating rooms [21], human resources management [22], mail facilities [23], printing [24], pharmacy automation [25], vehicular networks [26], and heterogeneous systems that include GPU and CPU [27].…”