On low complexity robust beamforming with positive semidefinite constraints

This paper addresses the problem of robust beamforming for general-rank signal models with norm bounded uncertainties in the desired and received signal covariance matrices as well as positive semidefinite constraints on the covariance matrices. Two novel minimum variance robust beamformers are derived in closed-form. The first one basically is the closed-form version of an existing iterative algorithm, while the second one offers even better performance with respect to the first one. Both of them have the advantage of low complexity. The effectiveness and performance improvement of the proposed beamformers are verified by simulation results. © 2009 IEEE.published_or_final_versio

Rationality, Culture and Deterrence / September 2013

The views expressed herein are those of the author and do not necessarily reflect the official policy or position of the Naval Postgraduate School, the Defense Threat Reduction Agency, the Department of Defense, or the United States Government.Deterrence strategies involve trying to influence the decision-making of another actor. Because of this, efforts to determine whether to employ a strategy of deterrence or how to implement such a strategy require attempting to forecast what things will influence the other actor and how that influence will be exerted. There are several models or frameworks available that could assist with efforts to anticipate how another actor will be influenced. In practice, the most prominent public debates related to deterrence in the United States have tended to reflect two main approaches. People tend to assume either that the other side will behave like a rational actor or that it will be driven by a unique strategic culture. While both approaches have merit, extensive critiques have revealed that both also have significant limitations.U.S. Naval Postgraduate School (NPS) Center on Contemporary Conflict (CCC) Project on Advanced Systems and Concepts for Countering WMD (PASCC)Approved for public release; distribution is unlimited

Robust joint design of linear relay precoder and destination equalizer for dual-hop amplify-and-forward MIMO relay systems

This paper addresses the problem of robust linear relay precoder and destination equalizer design for a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, two robust design algorithms are proposed to minimize the mean-square error (MSE) of the output signal at the destination. One is an iterative algorithm with its convergence proved analytically. The other is an approximated closed-form solution with much lower complexity than the iterative algorithm. Although the closed-form solution involves a minor relaxation for the general case, when the column covariance matrix of the channel estimation error at the second hop is proportional to identity matrix, no relaxation is needed and the proposed closed-form solution is the optimal solution. Simulation results show that the proposed algorithms reduce the sensitivity of the AF MIMO relay systems to channel estimation errors, and perform better than the algorithm using estimated channels only. Furthermore, the closed-form solution provides a comparable performance to that of the iterative algorithm. © 2006 IEEE.published_or_final_versio

Rasta Resin-TBD-Catalyzed γ-Selective Morita–Baylis–Hillman Reactions of α,γ-Disubstituted Allenones

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Bayesian robust linear transceiver design for dual-hop amplify-and-forward MIMO relay systems

In this paper, we address the robust linear transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems, where both transmitters and receivers have imperfect channel state information (CSI). With the statistics of channel estimation errors in the two hops being Gaussian, we formulate the robust linear-minimum-mean-square-error (LMMSE) transceiver design problem using the Bayesian framework, and derive a closed-form solution. Simulation results show that the proposed algorithm reduces the sensitivity of the relay system to channel estimation errors, and performs better than the algorithm using estimated channel only.published_or_final_versionThe IEEE Global Telecommunications Conference (GLOBECOM 2009), Honolulu, HI., 30 November-4 December 2009. In Proceedings of GLOBECOM, 2009, p. 1-

Run Generation Revisited: What Goes Up May or May Not Come Down

In this paper, we revisit the classic problem of run generation. Run generation is the first phase of external-memory sorting, where the objective is to scan through the data, reorder elements using a small buffer of size M , and output runs (contiguously sorted chunks of elements) that are as long as possible. We develop algorithms for minimizing the total number of runs (or equivalently, maximizing the average run length) when the runs are allowed to be sorted or reverse sorted. We study the problem in the online setting, both with and without resource augmentation, and in the offline setting. (1) We analyze alternating-up-down replacement selection (runs alternate between sorted and reverse sorted), which was studied by Knuth as far back as 1963. We show that this simple policy is asymptotically optimal. Specifically, we show that alternating-up-down replacement selection is 2-competitive and no deterministic online algorithm can perform better. (2) We give online algorithms having smaller competitive ratios with resource augmentation. Specifically, we exhibit a deterministic algorithm that, when given a buffer of size 4M , is able to match or beat any optimal algorithm having a buffer of size M . Furthermore, we present a randomized online algorithm which is 7/4-competitive when given a buffer twice that of the optimal. (3) We demonstrate that performance can also be improved with a small amount of foresight. We give an algorithm, which is 3/2-competitive, with foreknowledge of the next 3M elements of the input stream. For the extreme case where all future elements are known, we design a PTAS for computing the optimal strategy a run generation algorithm must follow. (4) Finally, we present algorithms tailored for nearly sorted inputs which are guaranteed to have optimal solutions with sufficiently long runs

Semi-blind CFO, channel estimation and data detection for ofdm systems over doubly selective channels

Proceedings of the IEEE International Symposium on Circuits and Systems, 2010, p. 1887-1890Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 1887-189

Distributed Hybrid Power State Estimation under PMU Sampling Phase Errors

Phasor measurement units (PMUs) have the advan- tage 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, hy- brid power state estimation taking advantage of both systems is im- portant. 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 er- rors. The proposed distributed algorithm only involves local com- putations and limited information exchange between neighboring areas, thus alleviating the heavy communication burden compared to the centralized approach. Simulation results show that the per- formance of the proposed algorithm is very close to that of central- ized optimal hybrid state estimates without sampling phase error.published_or_final_versio

IQ imbalance compensation: A semi-blind method for OFDM systems in fast fading channels

Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems, 2010, p. 362-365In this paper, an orthogonal frequency division multiplexing (OFDM) system operating in a fast fading environment modeled by a doubly selective channel (DSC) is considered. The paper first reformulates a commonly adopted system model using the generalized complex exponential basis expansion technique. The resulting model enables the IQ imbalance and DSC to be estimated in the time domain with a small number of scattered pilots within an OFDM symbol. A joint estimation and compensation scheme is then proposed which compensates all the inter-carrier interference terms. Simulation results show that the proposed compensation method achieves better symbol error rate performance than previous proposed methods. © 2010 IEEE.published_or_final_versionThe IEEE Asia Pacific Conference on Circuits and Systems (APCCAS'2010), Kuala Lumpur, Malaysia, 6-9 December 2010. in Proceedings of APCCAS'2010, 2010, p. 362-36

Distributed Bayesian hybrid power state estimation with PMU synchronization errors

Signal Processing for Communications SymposiumThis paper presents a distributed hybrid power state estimator, with measurements from both the traditional supervisory control and data acquisition (SCADA) system and the newly invented phasor measurement units (PMUs). The proposed distributed algorithm, which jointly estimates the power states and PMU phase errors, 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.published_or_final_versio