Topology control for effective interference cancellation in multiuser MIMO networks

  • Authors:

  • Ece Gelal;Jianxia Ning;Konstantinos Pelechrinis;Tae-Suk Kim;Ioannis Broustis;Srikanth V. Krishnamurthy;Bhaskar D. Rao

  • Affiliations:

  • University of California, Riverside, Riverside, CA;University of California, Riverside, Riverside, CA;University of Pittsburgh, Pittsburgh, PA;University of California, Riverside, Riverside, CA;University of California, Riverside, Riverside, CA;University of California, Riverside, Riverside, CA;University of California, San Diego, La Jolla, CA

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2013

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Abstract

In multiuser multiple-input-multiple-output (MIMO) networks, receivers decode multiple concurrent signals using successive interference cancellation (SIC). With SIC, a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satisfies a signal-to-noise-plus-interference ratio (SINR) requirement. This necessitates the appropriate selection of a subset of links that can be concurrently active in each receiver's neighborhood; in other words, a subtopology consisting of links that can be simultaneously active in the network is to be formed. If the selected subtopologies are of small size, the delay between the transmission opportunities on a link increases. Thus, care should be taken to form a limited number of subtopologies. We find that the problem of constructing the minimum number of subtopologies such that SIC decoding is successful with a desired probability threshold is NP-hard. Given this, we propose MUSIC, a framework that greedily forms and activates subtopologies in a way that favors successful SIC decoding with a high probability. MUSIC also ensures that the number of selected subtopologies is kept small. We provide both a centralized and a distributed version of our framework. We prove that our centralized version approximates the optimal solution for the considered problem. We also perform extensive simulations to demonstrate that: 1) MUSIC forms a small number of subtopologies that enable efficient SIC operations; the number of subtopologies formed is at most 17% larger than the optimum number of topologies, discovered through exhaustive search (in small networks); 2) MUSIC outperforms approaches that simply consider the number of antennas as a measure for determining the links that can be simultaneously active. Specifically, MUSIC provides throughput improvements of up to four times, as compared to such an approach, in various topological settings. The improvements can be directly attributable to a significantly higher probability of correct SIC based decoding with MUSIC.