Distributed consensus-based demodulation: algorithms and error analysis

Zhu, Hao; Cano, Alfonso; Giannakis, Georgios B.

doi:10.1109/twc.2010.06.090890

Cited by 53 publications

(48 citation statements)

References 12 publications

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“…Before proceeding, we deal with the approximation of E q j→k H H k,j . Then we have Now, we return to the approximation of x Putting the above relations (31), (33), (34), (37) and (38) together, we get AMP-RZFBF.…”

Section: Appendix A: Derivation For Amp-rzfbfmentioning

confidence: 99%

See 1 more Smart Citation

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

Wen

Chen

Wong

et al. 2014

IEEE Trans. Wireless Commun.

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Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding.However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communications is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

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Section: Appendix A: Derivation For Amp-rzfbfmentioning

confidence: 99%

“…Applications of ADMM to the concerned beamforming problem can be found in [34] (or [33,Section 8.3]). However, it is known that ADMM can be very slow to converge.…”

Section: Introductionmentioning

confidence: 99%

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

Wen

Chen

Wong

et al. 2014

IEEE Trans. Wireless Commun.

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“…Recently, we presented an distributed, iterative algorithm [1] that achieves this goal and establishes a consensus, i.e., that eventually, the centralized solution is available at all nodes. Compared to other algorithms from literature for consensusbased distributed estimation (e.g., [2], [3], [4]), our approach exhibits the advantage that every communication of nodes with its neighbors can be realized in a broadcast fashion only, resulting in relatively low communication effort, while still showing fast convergence. In this paper, we will investigate the algorithm's behavior in the presence of different classes of erroneous inter-node links; with additive errors as well as link failures.…”

Section: Introductionmentioning

confidence: 99%

In-Network-Processing: Distributed Consensus-Based Linear Estimation

2013

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Abstract-In a cooperative broadcast scenario, a group of nodes in a network aims to reconstruct a common message. We present a consensus-based linear estimation algorithm that shows improved convergence speed compared to state of the art techniques and whose signaling effort can be reduced without performance loss under certain conditions. We investigate its performance in the presence of erroneous inter-node links using different models for these error influences. Additionally, we propose a technique to mitigate the error influences and restore satisfactory overall system performance. All results are corroborated by computer simulations considering different system parameters and network setups.

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“…This algorithm establishes a scheme where nodes in a network exchange information with each other in order to reach the centralized solution iteratively. Other distributed consensusbased algorithms were proposed, e.g., in [4], [5]. Compared to [4], the DiCE algorithm showed a faster convergence and higher robustness against link failures while keeping the same estimation performance.…”

Section: Introductionmentioning

confidence: 99%

“…Further investigations of the DiCE algorithm concerning erroneous inter-node links were discussed in [7]. Both [4] and [7] describe the necessity of exchanging quantities in a unicast fashion between the nodes. Obviously, this exchange causes a high communication overhead in the network since each quantity depends on the transmitting and receiving node.…”

Section: Introductionmentioning

confidence: 99%

Reduced overhead distributed consensus-based estimation algorithm

Shin

Paul

Wübben

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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Abstract-We consider a cooperation among nodes in a network which aim to reconstruct a common broadcast message. Distributed estimation algorithms are highly suited for such a scenario. Nevertheless, communication overhead due to an exchange of variables among nodes can be problematic concerning energy efficiency and costs. In this paper, we present a Reduced Overhead Distributed Consensus-based Estimation (RO-DiCE) algorithm exhibiting a significantly reduced communication overhead in comparison to its unreduced version, the DiCE algorithm. However, by reducing the overhead a possible degradation in estimation performance of the algorithm is introduced. We investigate the RO-DiCE algorithm in terms of communication overhead, convergence behavior and error rate performance for different network topologies. We will show that for a full mesh topology the RO-DiCE algorithm is identical to the DiCE algorithm.

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Distributed consensus-based demodulation: algorithms and error analysis

Cited by 53 publications

References 12 publications

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

In-Network-Processing: Distributed Consensus-Based Linear Estimation

Reduced overhead distributed consensus-based estimation algorithm

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