We study a distributed link scheduling problem for device-to-device (D2D) communication considering the quality-of-service (QoS) requirements and time-varying channel conditions of D2D links. To this end, we first study an optimal centralized link scheduling problem maximizing the total average sum-rate while satisfying the QoS requirements of D2D links. We then abstract the important scheduling principles of the optimal link scheduling, i.e., giving more chance to be scheduled to the links which have a good channel condition and do not satisfy the QoS requirement, in order to utilize them to develop distributed link scheduling algorithms. With the scheduling principles, we develop a procedure with which D2D links can share their degree of QoS unsatisfaction and channel condition with each other and generate their scheduling priorities according to the shared information in a distributed manner. We also develop a novel distributed link scheduling criterion with which D2D links determine their link scheduling. By using them, we propose distributed link scheduling algorithms, QCLinQ and QC2 LinQ, which have significantly smaller signaling overhead and low computational complexity compared with the centralized optimal link scheduling algorithm. Moreover, they closely meet the QoS requirements of D2D links while achieving significant sum-rate improvement over conventional distributed algorithms.
|Number of pages||15|
|Journal||IEEE Transactions on Wireless Communications|
|Publication status||Published - 2016 Dec|
Bibliographical noteFunding Information:
This work was supported in part by the Institute for Information and Communications Technology Promotion Grant funded by the Korean Government (MSIP) through the development on the core technologies of transmission, modulation and coding with low-power and low complexity for massive connectivity in the IoT environment under Grant B0717-16-0024 and in part by the Midcareer Researcher Program through NRF Grant funded by MSIP, Korea, under Grant 2013R1A2A2A01069053.
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics