Transmit Power Allocation with Connectivity Probability for Multi-QoS in Cluster Flight Spacecraft Network

Abstract

In this paper, we investigate the transmit power allocation problem to minimize the average packet error rate at the access point in the cluster flight spacecraft network, which adopts the CSMA/CA channel access mechanism. First, the node mobility, nodal distance distribution, and probabilistic adjacency matrix were formulated for cluster flight spacecraft network based on twinsatellite mode. en, the optimization-theoretic model described the optimized transmit power allocation strategy and its implementation algorithm was proposed. And the problem of minimizing the packet error rate of the cluster flight spacecraft network system can be converted into maximizing the expectation of the binary probabilistic adjacency matrix, i.e., maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix. Due to discreteness of nodal distance distribution, Monte Carlo method was applied to solve the transmit power allocation problem. Yet importantly, the influence of node transmit power on the QoS performance of cluster flight spacecraft network was simulated and analyzed under the assumption of finite overall network transmit power and low traffic load. Finally, the results show that the pocket error rate increases with the provided traffic load, but the pocket error rate hardly changes with the same traffic load in different sequential time slots of any orbital hyperperiod or in the same time slot of different orbital hyperperiods, and by maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix, the pocket error rate minimum is achieved for a given total network transmit power at any time slot for cluster flight spacecraft network.

Research on Transmission Capacity Characteristics of the Cluster Flight Spacecraft Network

“…e empirical distribution function of the distance between nodes is from [21], and the orbital data of the CFSN composed of 5 are from [24].…”

“…Gaussian approximation and power constraints are based on orbital parameters and distance distribution between nodes in previous work [21,24]. We assume that B i � 4 kHz, R bi � 1 kbps, P si � P k � P r � P l , and β � 12.07. e noise power is 0.01 W.…”