Robust Power Allocation Algorithms for Wireless Relay Networks
Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers / IEEE Communications Society
2011
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| Online Access: | http://hdl.handle.net/1721.1/66183 https://orcid.org/0000-0002-8573-0488 |
| Summary: | Resource allocation promises significant benefits in
wireless networks. In order to fully reap these benefits, it is
important to design efficient resource allocation algorithms. Here,
we develop relay power allocation (RPA) algorithms for coherent
and noncoherent amplify-and-forward (AF) relay networks. The
goal is to maximize the output signal-to-noise ratio under
individual as well as aggregate relay power constraints. We show
that these RPA problems, in the presence of perfect global channel
state information (CSI), can be formulated as quasiconvex
optimization problems. In such settings, the optimal solutions
can be efficiently obtained via a sequence of convex feasibility
problems, in the form of second-order cone programs. The
benefits of our RPA algorithms, however, depend on the quality
of the global CSI, which is rarely perfect in practice. To address
this issue, we introduce the robust optimization methodology
that accounts for uncertainties in the global CSI. We show
that the robust counterparts of our convex feasibility problems
with ellipsoidal uncertainty sets are semi-definite programs. Our
results reveal that ignoring uncertainties associated with global
CSI often leads to poor performance, highlighting the importance
of robust algorithm designs in practical wireless networks. |
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