Institute for Systems Research
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Item On the True Cramer-Rao Lower Bound for the DA Joint Estimation of Carrier Phase and Timing Offsets(2000) Jiang, Yimin; Sun, F. W.; Baras, John S.; Baras, John S.; ISR; CSHCNThe Cramer-Rao lower bound (CRLB) plays a pivotal role in parameter estimation theory, such as timing, frequency and phase synchronization. Therefore, it receives considerable attention in the literature. This paper concerns the CRLB for data-aided (DA) timing and/or phase recovery, i.e. the parameter synchronization is aided by a training sequence known to the receiver. For DA parameter synchronization, the CRLB typically varies with the training sequence. This indicates that different training sequences offer fundamental different performance. Therefore, it is very important to be able to compute the CRLB for any particular training sequence to understand the fundamental limit that a particular training sequence has. However, in the literature, the closed-form CRLB for an arbitrary training sequence is not available. In principle, it is possible to use brute-force numerical approach to compute CRLB for any given training sequence. Such brute-force computation involves evaluation of derivatives numerically and matrix inversion. Besides the computational complexity, brute-force approach does not provide any insight on the interaction between training sequence and the resultant CRLB. In the literature, the widely cited close-form data-aided CRLB for timing and phase recovering was derived under the assumption that the training sequence is independently identical distributed (i.i.d.) and the length of the training sequence is sufficiently long. We found that the CRLB for a particular training sequence can be significantly lower than that with the long i.i.d. assumption. Therefore, the widely cited data-aided CRLB actually does not give the fundamental limit for a particular training sequence. In this manuscript, we derive a closed-form formula for data-aided CRLB for timing and phase synchronization with respect to arbitrary training sequence. The bound illustrates the close relation between the training sequence and the fundamental limit on timing and phase synchronization. This bound provides additional insights on the sequence design. 2000 IEEE International Conference on CommunicationsItem Dynamic ElGamal Public Key Generation with Tight Binding(1999) Poovendran, R.; Corson, M. Scott; Baras, John S.; ISR; CSHCNWe present a new distributed, group ElGamal public key generation algorithm which also permits maintenance of a group-specific, dynamic,individual ElGamal public key infrastructure.We parameterize the group with a time-varying quantity that servesas a distributed mechanism for controlling key generation privilege.
Our scheme can be viewed as an alternative to polynomial schemes where, at the time of the secret construction step, there has to be a third party or a black box to combine the shares. Also, in polynomial schemes, at the time of combining, the individual shares of the secret have to be revealed to the third party. In our scheme, the common secret can be generated without ever exposing the individual shares constructing it.
We note that many of the recently proposed distributed key management~[2-4] schemes need such group keys for certification and signing purposes.
Item VLSI Implemented ML Joint Carrier Phase and Timing Offsets Joint Estimator for QPSK/QQPSK Burst Modems(1999) Jiang, Yimin; Verahrami, F.B.; Richmond, R.L.; Baras, John S.; Baras, John S.; ISR; CSHCNA high performance ASIC supporting multiple modulation, error correction, and frame formats is under development at Hughes Network Systems, Inc. Powerful and generic data-aided (DA) estimators are needed to accommodate operation in the required modes. In this paper, a simplified DA maximum likelihood (ML) joint estimator for carrier phase and symbol timing offset for QPSK/OQPSK burst modems and a sample systolic VLSI implementation for the estimator are presented.Furthermore, the Cramer-Rao lower bound (CRLB) for DA case is investigated. The performance of the estimator is shown through simulation to meet the CRLB even at low signal-to-noise ratios (SNR). Compared with theoretical solutions, the proposed estimator is less computationally intensive and is therefore easier to implement using current VLSI technology. IEEE Wireless Communications and Networking Conference: WCNC'99
Item Data-Aided ML Parameter Estimators of PSK Burst Modems and Their Systolic VLSI Implementations(1999) Jiang, Yimin; Ting, W-C.; Verahrami, F.B.; Richmond, R.L.; Baras, John S.; Baras, John S.; ISR; CSHCNA high performance Universal Modem ASIC that supports several modulation types and burst mode frame formats is under development. Powerful and generic data-aided (DA) parameter estimators are necessary to accommodate many modes.In this paper we present an approximated maximum likelihood (ML) carrier frequency offset estimator, ML joint carrier phase and timing offsets estimator and their systolic VLSI implementations for PSK burst modems. The performances are close to the Cramer-Rao lower bounds (CRLB) at low SNRs.
Compared with theoretical solutions, the estimators proposed here are much simpler and easier to implement by the current VLSI technology. The CRLB for DA estimations is discussed in some depth, some issues on training sequence design is also addressed in this work.
Globecomm99
Item VLSI Implemented Data-Aided ML Parameter Estimators of PSK Burst Modems(1999) Jiang, Yimin; Ting, W.C.; Verahrami, F.B.; Richmond, R.L.; Baras, John S.; Baras, John S.; ISR; CSHCNA high performance Universal Modem ASIC that supports several modulation types and burst mode frame formats is under development. The ASIC is designed to work under stringent conditions such as large carrier frequency offset (up to 13 percent symbol rate) and low signal-to-noise ratio (SNR). Powerful and generic data-aided (DA) parameter estimators are necessary to accommodate many modes.In this paper we present an approximated maximum likelihood (ML) carrier frequency offset estimator, ML joint carrier phase and timing offsets estimator and their systolic VLSI implementations for PSK burst modems. The performances are close to the Cramer-Rao lower bounds (CRLB) at low SNRs. Compared with theoretical solutions the estimators proposed here are much simpler and easier to implement by the current VLSI technology.
VTC'99
Item Optimal Multilevel Feedback Policies for ABR Flow Control using Two Timescale SPSA(1999) Bhatnagar, Shalabh; Fu, Michael C.; Marcus, Steven I.; ISROptimal multilevel feedback control policies for rate based flow controlin available bit rate (ABR) service in asynchronous transfer mode (ATM)networks are obtained in the presence of information and propagationdelays, using a numerically efficient two timescale simultaneousperturbation stochastic approximation (SPSA) algorithm. Convergenceanalysis of the algorithm is presented. Numerical experiments demonstratefast convergence even in the presence of significant delays and largenumber of parametrized policy levels.Item On the Use of Integer Programming Models in AI Planning(1999) Vossen, Thomas; Ball, Michael O.; Lotem, Amnon; Nau, Dana; ISRRecent research has shown the promise of using propositional reasoning and search to solve AI planning problems. In this paper, we further explore this area by applying Integer Programming to solve AI planning problems. The application of Integer Programming to AI planning has a potentially significant advantage, as it allows quite naturally for the incorporation of numerical constraints and objectives into the planning domain. Moreover, the application of Integer Programming to AI planning addresses one of the challenges in propositional reasoning posed by Kautz and Selman, who conjectured that the principal technique used to solve Integer Programs---the linear programming (LP) relaxation---is not useful when applied to propositional search. We discuss various IP formulations for the class of planning problems based on the STRIPS paradigm. Our main objective is to show that a carefully chosen IP formulation significantly improves the "strength" of the LP relaxation, and that the resultant LPs are useful in solving the IP and the associated planning problems. Our results clearly show the importance of choosing the "right" representation, and more generally the promise of using Integer Programming techniques in the AI planning domain.