Opportunistic cophasing transmission in MISO systems

Xia, Minghua; Wen, Wenkun; Kim, Soo-Chang

doi:10.1109/tcomm.2009.12.080414

Cited by 10 publications

(13 citation statements)

References 17 publications

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“…In this case, approximate schemes need to be resorted to. One example is the Markov Chain Monte Carlo (MCMC) method, which approximates the distributions and integration operations using a large number of random samples [31], [34]. However, sampling methods can be computationally demanding, often limiting their use to small-scale problems.…”

Section: State Estimation Under Sampling Phase Errormentioning

confidence: 99%

“…Since the goal is to derive a distributed algorithm, it is also assumed that each bus has access only to the mean and variance of its own state from SCADA estimates, i.e., , with and . Then, the optimal variational distributions and can be obtained through minimizing the KL divergence in (31). Similar to (21) and (22), the and that minimize (31) are given by (32) and (33) at the bottom of the next page.…”

Section: B Distributed Estimationmentioning

confidence: 99%

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Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

et al. 2014

IEEE Trans. Signal Process.

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Phasor measurement units (PMUs) have the advantage of providing direct measurements of power states. However, as the number of PMUs in a power system is limited, the traditional supervisory control and data acquisition (SCADA) system cannot be replaced by the PMU-based system overnight. Therefore, hybrid power state estimation taking advantage of both systems is important. As experiments show that sampling phase errors among PMUs are inevitable in practical deployment, this paper proposes a distributed power state estimation algorithm under PMU phase errors. The proposed distributed algorithm only involves local computations and limited information exchange between neighboring areas, thus alleviating the heavy communication burden compared to the centralized approach. Simulation results show that the performance of the proposed algorithm is very close to that of centralized optimal hybrid state estimates without sampling phase error.

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Section: State Estimation Under Sampling Phase Errormentioning

confidence: 99%

Section: B Distributed Estimationmentioning

confidence: 99%

Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

et al. 2014

IEEE Trans. Signal Process.

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show abstract

“…In general, for the ( ) round of message exchange, factor node receive messages from its neighboring variable nodes and then compute messages using (7). After some derivations, it can be obtained that (11) where the inverse of covariance matrix is (12) and the mean vector is (13) On the other hand, using (8), the messages passed from variable nodes to factor nodes can be computed as (14) where (15) and (16) Furthermore, during each round of message passing, each node can compute the belief for using (9), which can be easily shown to be , with the inverse of covariance matrix (17) and mean vector (18) When the algorithm converges or the maximum number of message exchange is reached, each node computes the CFOs according to (10) as (19) The iterative algorithm based on BP is summarized as follows.…”

Section: B Message Computationmentioning

confidence: 99%

“…For example, Distributed beamforming: As shown in Fig. 1(a), to improve the range of communications and save battery power Manuscript during the transmission, multiple mobile terminals form a virtual antenna array and cooperatively direct a beam in the desired direction of transmission [6], [7]. Since each source node in the distributed beamformer has an independent local oscillator, common carrier frequency among all transmitters is crucial to ensure that a beam is aimed in the desired direction.…”

Section: Introductionmentioning

confidence: 99%

Network-Wide Distributed Carrier Frequency Offsets Estimation and Compensation via Belief Propagation

Du¹,

Wu²

2013

IEEE Trans. Signal Process.

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In this paper, we propose a fully distributed algorithm for frequency offsets estimation in decentralized systems. The idea is based on belief propagation, resulting in that each node estimates its own frequency offsets by local computations and limited exchange of information with its direct neighbors. Such algorithm does not require any centralized information processing or knowledge of global network topology, thus is scalable with network size. It is shown analytically that the proposed algorithm always converges to the optimal estimates regardless of network topology. Simulation results demonstrate the fast convergence of the algorithm and show that estimation mean-squared-error at each node approaches the centralized Cramér-Rao bound within a few iterations of message exchange.

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“…On the contrary, opportunistic scheduling exploits multi-user diversity gain and achieves the sum-rate capacity as the number of users K approaches infinity [3]. When the base station (BS) is equipped with N t > 1 transmit antennas, opportunistic scheduling can be implemented by opportunistic beamforming (OBF), which generates N = N t beams and serves up to N t users for each channel use [4], [5]. While it achieves the sum-rate capacity, OBF can result in significant scheduling delay over the users, especially in densely populated networks.…”

Section: and By King Abdullah University Of Science Andmentioning

confidence: 99%

Non-Orthogonal Opportunistic Beamforming: Performance Analysis and Implementation

Xia

Wu²,

Aı̈ssa

2012

IEEE Trans. Wireless Commun.

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Abstract-Aiming to achieve the sum-rate capacity in multiuser multi-antenna systems where Nt antennas are implemented at the transmitter, opportunistic beamforming (OBF) generates Nt orthonormal beams and serves Nt users during each channel use, which results in high scheduling delay over the users, especially in densely populated networks. Non-orthogonal OBF with more than Nt transmit beams can be exploited to serve more users simultaneously and further decrease scheduling delay. However, the inter-beam interference will inevitably deteriorate the sum-rate. Therefore, there is a tradeoff between sum-rate and scheduling delay for non-orthogonal OBF. In this context, system performance and implementation of non-orthogonal OBF with N > Nt beams are investigated in this paper. Specifically, it is analytically shown that non-orthogonal OBF is an interferencelimited system as the number of users K → ∞. When the interbeam interference reaches its minimum for fixed Nt and N , the sum-rate scales as N ln

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Opportunistic cophasing transmission in MISO systems

Cited by 10 publications

References 17 publications

Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

Network-Wide Distributed Carrier Frequency Offsets Estimation and Compensation via Belief Propagation

Non-Orthogonal Opportunistic Beamforming: Performance Analysis and Implementation

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