Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

  • Vijayandran L
  • Byun S
  • Øien G
  • et al.
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Abstract

We revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. We consider an interference-limited multiuser multi-channel environment, with a transmit sum-power constraint over all channels as well as an aggregate average interference constraint towards multiple primary users. Until very recently only sub-optimal algorithms were proposed due to the inherent non-convexity of the problem. Yet, the problem at hand has been neglected in the large-scale setting (i.e., number of nodes and channels) as usually encountered in practical scenarios. To tackle this issue, we first propose an exact mathematical adaptation of the well-known successive convex geometric programming with condensation approximations (SCVX) to better cope with large systems while keeping the convergence proof intact. Alternatively, we also propose a novel efficient low-complexity heuristic algorithm, ELCI. ELCI is an iterative approach, where the constraints are handled alternately based on the special property of the optimal solution, with a particular power update formulation based on the KKT conditions of the problem. In order to demonstrate ELCI's efficiency we compare it to two state-of-the-art algorithms, SCVX, and the recently proposed global optimum approach, MARL. The salient highlight of ELCI is the relatively fast and very good sub-optimal performance in large-scale CR systems.

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APA

Vijayandran, L., Byun, S.-S., Øien, G. E., & Ekman, T. (2012). Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation. EURASIP Journal on Wireless Communications and Networking, 2012(1). https://doi.org/10.1186/1687-1499-2012-362

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