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Energy‐efficient Link Scheduling in Time‐variant Dual‐Hop 60GHz Wireless Networks
Author(s) -
Wu Wenjia,
Xu Weihong,
Chen Zhouguo,
Yang Ming
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5903
Subject(s) - computer science , relay , scheduling (production processes) , energy consumption , round robin scheduling , fair share scheduling , mathematical optimization , wireless network , distributed computing , hop (telecommunications) , wireless , computer network , power (physics) , mathematics , engineering , telecommunications , physics , quality of service , quantum mechanics , electrical engineering
Summary Dual‐hop 60 GHz wireless networks which support relay‐assisted dual‐hop transmission have been widely adopted in the recent years, aiming to prolong communication distance and bypass obstacles in 60 GHz band. However, it is very challenging to perform link scheduling in such dual‐hop architecture while considering several factors, i.e., reducing network power consumption, avoiding overloaded APs/relays and adapting to network dynamics. To this end, we investigate the problem of energy‐efficient link scheduling with load constraints (ELL), and propose solutions to deal with network dynamics. First, we present a fine‐grained energy model for dual‐hop 60 GHz networks, and formulate the ELL problem as an integer linear programming model that aims to minimize the network power consumption, while satisfying AP/relay load constraints. Then, we propose a polynomial‐time global scheduling algorithm that obtains a near‐optimal link scheduling solution via iterative relaxation, and the load constraint at each AP/relay can only be violated by at most an additive constant of two. Moreover, we present a local adjustment algorithm to adjust link‐scheduling solutions efficiently, such as client arrival/departure and link blockage, and design a hybrid algorithm that combines global scheduling and local adjustment. Finally, we conduct simulation experiments that validate our algorithms' effectiveness and efficiency.