Abstract
The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously.
| Original language | English |
|---|---|
| Pages (from-to) | 1339-1351 |
| Number of pages | 13 |
| Journal | Computer Networks |
| Volume | 129 |
| DOIs | |
| Publication status | Published - 2017 Dec 24 |
Fingerprint
Keywords
- Energy efficiency
- M2M communications
- Multi-cellular networks
- Noncooperative game theory
- Random access
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Energy-efficient game-theoretical random access for M2M communications in overlapped cellular networks. / Zhou, Zhenyu; Feng, Junhao; Jia, Yunjian; Mumtaz, Shahid; Huq, Kazi Mohammed Saidul; Rodriguez, Jonathan; Zhang, Di.
In: Computer Networks, Vol. 129, 24.12.2017, p. 1339-1351.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Energy-efficient game-theoretical random access for M2M communications in overlapped cellular networks
AU - Zhou, Zhenyu
AU - Feng, Junhao
AU - Jia, Yunjian
AU - Mumtaz, Shahid
AU - Huq, Kazi Mohammed Saidul
AU - Rodriguez, Jonathan
AU - Zhang, Di
PY - 2017/12/24
Y1 - 2017/12/24
N2 - The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously.
AB - The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously.
KW - Energy efficiency
KW - M2M communications
KW - Multi-cellular networks
KW - Noncooperative game theory
KW - Random access
UR - http://www.scopus.com/inward/record.url?scp=85026270513&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026270513&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2017.06.019
DO - 10.1016/j.comnet.2017.06.019
M3 - Article
AN - SCOPUS:85026270513
VL - 129
SP - 1339
EP - 1351
JO - Computer Networks
JF - Computer Networks
SN - 1389-1286
ER -