| 1. |
- Brandt, Rasmus, 1985-, et al.
(författare)
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Fast-Convergent Distributed Coordinated Precoding for TDD Multicell MIMO Systems
- 2015
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Ingår i: Proc. IEEE Int. Workshop Computational Advances in Multi-Sensor Adaptive Process. (CAMSAP'15).
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Konferensbidrag (refereegranskat)abstract
- Several distributed coordinated precoding methods relying on over-the-air (OTA) iterations in time-division duplex (TDD) networks have recently been proposed. Each OTA iteration incurs overhead, which reduces the time available for data transmission. In this work, we therefore propose an algorithm which reaches good sum rate performance within just a few number of OTA iterations, partially due to non-overhead-incurring local iterations at the receivers. We formulate a scalarized multi-objective optimization problem where a linear combination of the weighted sum rate and the multiplexing gain is maximized. Using a well-known heuristic for smoothing the optimization problem together with a linearization step, the distributed algorithm is derived. When numerically compared to the state-of-the-art in a scenario with 1 to 3 OTA iterations allowed, the algorithm shows significant sum rate gains at high signal-to-noise ratios.
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| 2. |
- Brandt, Rasmus, 1985-, et al.
(författare)
-
Interference Alignment-Aided Base Station Clustering using Coalition Formation
- 2015
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Ingår i: Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers. - Pacific Grove, CA, USA : IEEE conference proceedings.
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Konferensbidrag (refereegranskat)abstract
- Base station clustering is necessary in large interference networks, where the channel state information (CSI) acquisition overhead otherwise would be overwhelming. In this paper, we propose a novel long-term throughput model for the clustered users which addresses the balance between interference mitigation capability and CSI acquisition overhead. The model only depends on statistical CSI, thus enabling long-term clustering. Based on notions from coalitional game theory, we propose a low-complexity distributed clustering method. The algorithm converges in a couple of iterations, and only requires limited communication between base stations. Numerical simulations show the viability of the proposed approach.
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