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Adaptive frequency‐hopping schemes for CR‐based multi‐link satellite networks
Author(s) -
Aykin Irmak,
Krunz Marwan,
Xiao Yong
Publication year - 2018
Publication title -
international journal of satellite communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 39
eISSN - 1542-0981
pISSN - 1542-0973
DOI - 10.1002/sat.1235
Subject(s) - geostationary orbit , computer science , satellite , frequency hopping spread spectrum , communications satellite , overhead (engineering) , transmitter power output , cognitive radio , medium earth orbit , interference (communication) , network packet , topology (electrical circuits) , real time computing , channel (broadcasting) , telecommunications , computer network , electronic engineering , transmitter , wireless , electrical engineering , physics , astronomy , engineering , operating system
Summary In this paper, we study two dynamic frequency hopping (DFH)–based interference mitigation approaches for satellite communications. These techniques exploit the sensing capabilities of a cognitive radio to predict future interference on the upcoming frequency hops. We consider a topology where multiple low Earth orbit satellites transmit packets to a common geostationary equatorial orbit satellite. The FH sequence of each low Earth orbit–geostationary equatorial orbit link is adjusted according to the outcome of out‐of‐band proactive sensing scheme, performed by a cognitive radio module in the geostationary equatorial orbit satellite. On the basis of sensing results, new frequency assignments are made for the upcoming slots, taking into account the transmit powers, achievable rates, and overhead of modifying the FH sequences. In addition, we ensure that all satellite links are assigned channels such that their minimum signal‐to‐interference‐plus‐noise ratio requirements are met, if such an assignment is possible. We formulate two multi‐objective optimization problems: DFH‐Power and DFH‐Rate. Discrete‐time Markov chain analysis is used to predict future channel conditions, where the number of states are inferred using k ‐means clustering, and the state transition probabilities are computed using maximum likelihood estimation. Finally, simulation results are presented to evaluate the effects of different system parameters on the performance of the proposed designs.