TY - GEN
T1 - Progressive optimization for time-varying channel with one-way multiple Mechanical Relays
AU - Min, Byoung Yoon
AU - Jeong, Min Keun
AU - Kim, Dong Ku
AU - Huang, Kaibin
AU - Kountouris, Marios
PY - 2013
Y1 - 2013
N2 - Mechanical Relay (McR) is an architecture to enable data communications, where transmission delay and intermittent service are tolerated. Since each source and destination user uploads/downloads data when McRs are located in transmission range, we obtain more significant sum-throughput gain than fixed/no relay networks even if some packet delay might be burden for delay-sensitive cases. In this paper, we propose a couple of iterative algorithms: 1) water-filling power control (WFPC) for distance-based McR systems, 2) automatic initializing gradient algorithms (AIGA) that try to solve two optimization problems: 2-1) Sum-rate maximization, 2-2) Sum-MSE minimization. From the experimental results, utilizing McRs has much better sum-throughput performance than that of fixed/no relay cases, and WFBC also has the advantage in low SNR region. In addition, convergence points of general gradient methods highly depend on the step-size value, our proposed AIGA achieves lower complexity even if step-size is quite small.
AB - Mechanical Relay (McR) is an architecture to enable data communications, where transmission delay and intermittent service are tolerated. Since each source and destination user uploads/downloads data when McRs are located in transmission range, we obtain more significant sum-throughput gain than fixed/no relay networks even if some packet delay might be burden for delay-sensitive cases. In this paper, we propose a couple of iterative algorithms: 1) water-filling power control (WFPC) for distance-based McR systems, 2) automatic initializing gradient algorithms (AIGA) that try to solve two optimization problems: 2-1) Sum-rate maximization, 2-2) Sum-MSE minimization. From the experimental results, utilizing McRs has much better sum-throughput performance than that of fixed/no relay cases, and WFBC also has the advantage in low SNR region. In addition, convergence points of general gradient methods highly depend on the step-size value, our proposed AIGA achieves lower complexity even if step-size is quite small.
UR - http://www.scopus.com/inward/record.url?scp=84889575902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889575902&partnerID=8YFLogxK
U2 - 10.1109/ChinaSIP.2013.6625348
DO - 10.1109/ChinaSIP.2013.6625348
M3 - Conference contribution
AN - SCOPUS:84889575902
SN - 9781479910434
T3 - 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
SP - 298
EP - 302
BT - 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
T2 - 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
Y2 - 6 July 2013 through 10 July 2013
ER -