2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2015
DOI: 10.1109/globalsip.2015.7418393
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Energy efficient power allocation and relay location for asymmetric bi-directional relaying

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Cited by 5 publications
(6 citation statements)
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“…The authors of [5] took a BDRS using an AF relay into consideration and proposed a joint power adaptation and relay selection technique, minimizing the total transmit power. By using the statistical channel knowledge, a joint power adaptation and relay location algorithm was developed in [6] to reduce the total transmit power of an AF relay-assisted BDRS while guaranteeing the QoS requirement of the system. In [7], a DF relay-assisted BDRS with a fixed packet rate was investigated, leading to three power adaptation algorithms, minimizing the system, the relay and the source energy consumption, respectively.…”
Section: Related Workmentioning
confidence: 99%
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“…The authors of [5] took a BDRS using an AF relay into consideration and proposed a joint power adaptation and relay selection technique, minimizing the total transmit power. By using the statistical channel knowledge, a joint power adaptation and relay location algorithm was developed in [6] to reduce the total transmit power of an AF relay-assisted BDRS while guaranteeing the QoS requirement of the system. In [7], a DF relay-assisted BDRS with a fixed packet rate was investigated, leading to three power adaptation algorithms, minimizing the system, the relay and the source energy consumption, respectively.…”
Section: Related Workmentioning
confidence: 99%
“…One valid physical-layer method for enhancing EE of wireless communications is the utilization of power adaptation techniques [5][6][7], whereby a transmitter can frequently adapt its transmit-power level in the light of the wireless channel states as well as the quality-of-service (QoS) requirements of receivers. It is well known that a transmitter should use the channel knowledge so as to implement power adaptation.…”
mentioning
confidence: 99%
“…where c(k), k ∈ {1, 2, · · · , K − 1} is defined in (17). It is clear that linear system (23) can be solved by calculating the inverse matrix of X.…”
Section: Appendix 2: Proof Of Propositionmentioning
confidence: 99%
“…where d ij and g ij represent the distance and the large-scale behavior of the path gain between nodes N i and N j , respectively, and α is the corresponding path-loss coefficient [12,17]. Without loss of generality, we assume that g ij is a complex Gaussian random variable and obeys CN (0, 1), and the channels are reciprocal, i.e., H ij = H ji and quasi-static.…”
Section: Network Modelmentioning
confidence: 99%
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