In this paper, we investigate the downlink performance of frequency hopping Ad Hoc communication system with non-orthogonal multiple access (FH-NOMA Ad Hoc), in which the mobile users are divided into multiple groups defined as NOMA clusters. Each NOMA cluster works on a certain frequency resource block with frequency hopping. Each mobile user in a NOMA cluster can act as NOMA transmitter with equal access probability and serve the rest mobile users with differentiated power allocation. With the considered network model, a stochastic geometry framework is provided to analyze the coverage and data rate performance of the FH-NOMA Ad Hoc network. Expressions for the coverage probability and the average data rate of a typical mobile user are derived. Finally, numerical and Monte Carlo simulations are provided to validate the theoretical analysis. The results reveal that the coverage probability of the FH-NOMA Ad Hoc network can be improved by varying the radius of each NOMA cluster, the number of frequency points, the density of NOMA clusters and user power allocation. Importantly, there is a constraint relationship between the density of NOMA clusters and the number of frequency points.INDEX TERMS Ad Hoc, frequency hopping, non-orthogonal multiple access, stochastic geometry.
In the field of the automotive area as well as industrial control, real-time communication requires deterministic delivery with low delay and bounded jitter. Real-time communication in these networks requires transmission schedule and routing, which is an NP-hard problem. In this paper, we present an offline routing and scheduling method based on integer linear programming (ILP), with a flow preprocessing step to explore the period correlation of time-triggered (TT) traffic in time-sensitive networking (TSN). First, a multiperiod flow routing and scheduling algorithm based on flow classification is proposed to improve the scheduling success rate and reduce execution time. The flow classification technique obtained a more fine-grained TT traffic classification, which can be superimposed on any routing and scheduling algorithms. Second, an adaptive period compensation scheduling algorithm based on flow classification is proposed in simple network architecture conditions. The evaluations demonstrate that the proposed algorithms improve scheduling success rate and reduce execution time compared with baseline methods in all test cases. In addition, we can adapt our different proposed algorithms in different network architecture conditions to schedule various flows with different periods and sizes.
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