Abstract-In this paper, a scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presented. multidimensional NMR data can be modeled as the sum of several multidimensional damped sinusoids. The estimated frequencies and damping factors of multidimensional NMR data play important roles in determining protein structures. In this paper we present a high-resolution subspace method for estimating the parameters of NMR data. Unlike other methods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data. Hence, it can estimate the signal parameters under low signal-to-noise ratio (SNR) by using a few data points. The effectiveness of the new algorithm is confirmed by computer simulations and it is tested by experimental data.

Abstract-In this paper, we consider the downlink rate control problem in a wireless channel. A dynamic programming optimization method is introduced to obtain the optimal bit-rate/delay control policy in the downlink for packet transmission in wireless networks with fading channels. We assume that the base station is capable of transmitting data packets in the downlink with different bit rates, 0 1 1 . It is assumed that the symbol rate is fixed in the system, and different bit rates are achieved by choosing the transmitted symbols from the appropriate signal constellation (adaptive modulation). The derived optimal rate control policy, in each time slot, selects the highest possible bit rate which minimizes the delay and at the same time minimizes the number of rate switchings in the network. The optimal bit-rate control problem is an important issue, especially in packet data networks, where we need to guarantee a quality of service (QoS) in the network. Our analytical as well as simulation results confirm that there is an optimal threshold policy to switch between different rates.Index Terms-Adaptive modulation, dynamic programming, optimal bit-rate control, wireless packet networks.

Abstract-Recently, there has been considerable interest in using antenna arrays in wireless communication networks to increase the capacity and decrease the cochannel interference. Adaptive beamforming with smart antennas at the receiver increases the carrier-to-interference ratio (CIR) in a wireless link. This paper considers a wireless network with beamforming capabilities at the receiver which allows two or more transmitters to share the same channel to communicate with the base station. The concrete computational complexity and algorithm structure of a base station are considered in terms of a software radio system model, initially with an omnidirectional antenna. The software radio computational model is then expanded to characterize a network with smart antennas. The application of the software radio smart antenna is demonstrated through two examples. First, traffic improvement in a network with a smart antenna is considered, and the implementation of a hand-off algorithm in the software radio is presented. The blocking probabilities of the calls and total carried traffic in the system under different traffic policies are derived. The analytical and numerical results show that adaptive beamforming at the receiver reduces the probability of blocking and forced termination of the calls and increases the total carried traffic in the system. Then, a joint beamforming and power control algorithm is implemented in a software radio smart antenna in a CDMA network. This shows that, by using smart antennas, each user can transmit with much lower power, and therefore the system capacity increases significantly.Index Terms-Adaptive beamforming, handoff, power control, smart antennas, software radio.

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Abstract-A wireless network with beamforming capabilities at the receiver is considered that allows two or more transmitters to share the same channel to communicate with the base station. A novel approach is introduced, which combines the effects of the digital signal processing (adaptive beamforming) at the physical layer withthe traffic policies at the network layer on the overall queuing model of a cell. The effect of signal processing on the queuing model of the cell is represented by a parameter in the final cell model. Each cell is modeled by a multiuser/multiserver service facility, where each server is a beamformed channel formed by the cell's base station. From this effective cell model, we find the closed form solutions for blocking probabilities of the calls and total carried traffic in a wireless netwrok with adaptive arrays. Our analytical as well as numerical results show that adaptive beamforming at the receiver reduces the blocking probability of the calls and increases the total carried traffic in the system. Index Terms-Adaptive beamforming, antenna arrays, handoff queuing, wireless networks.

A dynarnic programming optimization method is used to obtain the optimal rate control policy in a wireless network with fading channel. In a wireless network it is assumed that the base station is capable of transceiving data packets at two rates, either Rh or RI (Rh > R1). An optimal policy is derived which jointly minimizes the transmission delay and the number of rate switchings in the network. Numerical results indicate that by sacrificing only 1% of transmission quality in terms of the average delay one can achieve more than 50% reduction in switching load of the network. Our analytical as well as numerical results confirm that the optimal policy is a threshold policy.

This paper proposes a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interest in many signal processing applications, like analysis of NMR data and system identification. The new algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, rank deficient Hankel approximation of prediction matrix is used. The performance of the new algorithm is significantly improved by structured low rank approximation of prediction matrix. Computer simulations show that the noise threshold of the new algorithm is significantly better than the existing algorithms.

Most of the existing algorithms for parameter estimation of damped sinusoidal signals are based only on the low-rank approximation of prediction matrix and ignore the Hankel property of the prediction matrix. In this correspondence, we propose a modified KT (MKT) algorithm exploiting both rank-deficient and Hankel properties of the prediction matrix. Computer simulation results demonstrate that compared with the original KT algorithm and the matrix pencil algorithm, the MKT algorithm has lower noise threshold and can estimate the parameters of signal with larger damping factors.