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, R/sub 0/<R/sub 1/</spl middot//spl middot//spl middot/

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 with the 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 network 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.

A cellular wireless network with beamforming capabilities at the receiver is considered which allows two or more transmitters to share the same channel for communicating with the base station. A novel approach is introduced which combines the effects of the digital signal processing at the physical layer along with the network layer protocol on the overall queuing model of a cell. From this effective cell model, we find the closed form solutions for blocking probabilities of the calls and total carried traffic in the system. 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.

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. I.

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, a rank deficient Hankel approximation of the prediction matrix is used. The performance of the new algorithm is significantly improved by structured low rank approximation of the prediction matrix. Computer simulations show that the noise threshold of the new algorithm is significantly better than the existing algorithms.