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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Geraci, G. Wildemeersch, M. Quek, T.Q.S. |
| Copyright Year | 1991 |
| Abstract | In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: 1) hybrid, where the signal processing operations are shared between nodes and APs; 2) centralized, where signal processing is entirely implemented at the APs; and 3) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 1034 |
| Ending Page | 1047 |
| Page Count | 14 |
| File Size | 2909823 |
| File Format | |
| ISSN | 1053587X |
| Volume Number | 64 |
| Issue Number | 4 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2016-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Sensors Signal processing Media Access Protocol Decoding Wireless networks Energy consumption Interference successive interference cancellation Cross-layer design energy efficiency random topology spectrum sensing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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