The performance of a multiuser wireless network using orthogonal frequency-division modulation (OFDM), combined with power control and adaptive beamforming for uplink transmission is presented here. A network-wide adaptive power control algorithm is used to achieve the desired signal-to-interference-and-noise-ratio at each OFDM subcarrier and increase the power efficiency of the network. As a result, we can achieve a better overall error probability for a fixed total transmit power. With the assumption of fixed-modulation for all subcarriers, transmit powers and beamforming weight vectors at each subcarrier are updated jointly, using an iterative algorithm that converges to the optimal solution for the entire network. Unlike most of the loading algorithms, this approach considers fixed bit allocation and optimizes the power allocation and reduces the interference for the entire network, rather than a single transmitter. We also propose joint time-domain beamforming and power control to reduce the complexity resulting from the number of beamformers and fast Fourier transformed blocks. The proposed algorithm is also extended to COFDM and we show that it improves the performance of those systems.

In this paper, we investigate scheduling in multiuser OFDMA systems. The objective is to maximize the resource utilization in a network, while a required quality of service (QoS) per user is satisfied. A notion of revenue maximization is used to determine the optimal OFDMA subcarrier allocation to different users based on their required QoS. The optimal solution for the problem is presented and its performance and complexity are studied. An iterative low complexity algorithm is also proposed to achieve near optimal performance.

We consider downlink transmission in a multi-user environment, where each base station transmits multiple data streams to its corresponding mobiles using multiple input multiple output (MIMO) OFDM. Using distributed schemes, we find the optimal transmit and receive weight vectors to maximize the total data rate for each user. We consider two scenarios, where in one the total power per user and in the other total power per subcarrier is fixed. In each case, we propose iterative algorithms to maximize the allocated rate per user. We discuss the convergence of our schemes and compare their performances with those of previous work.

The objective of scheduling algorithms in wireless networks is to provide quality of service (QoS) for mobile users in a shared environment and at the same time utilize the system resources efficiently. We have introduced a notion of income maximization to increase throughput for multimedia wireless systems and to maintain the QoS for each user above an agreed level. We have proposed greedy and dynamic programming approaches to solve the optimization problem. The simulation results reveal that our scheduling algorithms provide high network throughput, support QoS even under heavy network loads, and generate high income for service provider.

Capacity optimization in a multicell OFDMA system where each cell has multiple users is investigated in this work. The objective is to find an assignment of users to the sets of sub-carrier, their transmission rates for the sub-carrier, and power allocation such that the total system capacity is increased, while users meet a minimum total rate requirement and a power constraint. Since the optimal solution involves an exhaustive search or complex nonlinear integer programming, we develop sub-optimal low complexity algorithms. We propose a two-step scheme: First an initial channel and data rate allocations are determined by two initialization algorithms. Then we refine the assigned rates by an iterative algorithm. From simulation results, the proposed algorithms can efficiently allocate resources to increase the overall system capacity and reduce the allocation outages.

The beamforming problem is studied in wireless networks where both the transmitters and receivers have linear adaptive antenna arrays. Algorithms are proposed that find the antenna array weight vectors at both the transmitters and receivers as well as the transmitter powers with one of the following two objectives: (1) to maximize the minimum signal-to-interference-and-noise ratio (SINR) over all receivers and (2) to minimize the sum of the total transmitted power satisfying the SINR requirements at all links. A numerical study is performed to compare the network capacity and the power consumption among systems having a different number of antenna array elements in a code division multiple access network.

The ever growing need for higher capacity in wireless systems has fueled the interest in exploiting the spatial dimension-through the use of antennas arrays-to improve the utilization of the available radio spectrum. As a result, a large number of space-time techniques have been proposed wherein arrays are used to mitigate interference and enhance signal levels. More recently, information theory has shown that, with spatial data multiplexing, very large spectral efficiencies can be attained in multipath channels using transmit and receive antenna arrays. In this paper, the system benefit of using transmit and receive arrays in multicell scenarios is evaluated as a function of both the propagation environment and the number of antennas. Our results confirm the potential for very large system spectral efficiencies associated with the use of transmit and receive arrays, in particular in interference-limited rich-multipath conditions wherein the ability to perform interference mitigation-leading to tighter spectral reuse-and spatial data multiplexing grows with the number of antennas. In environments free of multipath, the potential is smaller but still very significant, associated with interference mitigation and signal enhancement only, since spatial data multiplexing is no longer possible.

Recent information-theory results have shown the enormous capacity potential of wireless techniques that use transmit and receive antenna arrays. As a result, a number of layered space-time (BLAST) architectures have been proposed wherein multiple data streams are transmitted in parallel and separated at the receiver on account of their distinct spatial signatures. While extremely promising, all analysis of BLAST to date were restricted to the context of a single-user link. In this paper, the system-level benefit of using BLAST in multicell scenarios is evaluated in comparison with other directive- and adaptive-array techniques.

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.

In this paper, the performance of a multiuser wireless network using OFDM, combined with power control and adaptive beamforming for uplink transmission is presented. An adaptive power control algorithm is exploited to achieve the desired signal to noise and interference ratio (SINR) at each subchannel and increase the power efficiency of the mobile transmitter. Therefore, we can achieve a better overall error probability with a fixed total power. A distributed iterative algorithm is used to jointly update the transmission power and the beamformer weights at each subchannel so that it can converge to the optimal solution for both power and beamforming vectors at each subchannel. Another algorithm has been proposed to decrease the complexity resulting from the number of beamformers and FFT blocks. The algorithms use only the interference measured locally by the transmitter. Unlike most of the loading algorithms which optimize the bit distribution and subchannel power allocation for a single transmitter, this approach tries to optimize the power allocation and decrease the interference for the whole network.

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.

Joint power control and beamforming schemes are proposed for cellular systems where adaptive arrays are used only at base stations. In the uplink, mobile power and receiver diversity combining vectors at the base stations are calculated jointly. The mobile transmitted power is minimized, while the signal-to-interference-and-noise ratio (SINR) at each link is maintained above a threshold. A transmit diversity scheme for the downlink is also proposed where the transmit weight vectors and downlink power allocations are jointly calculated such that the SINR at each mobile is above a target value. The proposed algorithm achieves a feasible solution for the downlink if there is one and minimizes the total transmitted power in the network. In a reciprocal network it can be implemented in a decentralized system, and it does not require global channel response measurements. In a nonreciprocal network, where the uplink and downlink channel responses are different, the proposed transmit beamforming algorithm needs to be implemented in a centralized system, and it requires a knowledge of the downlink channel responses. The performances of these algorithms are compared with previously proposed algorithms through numerical studies.

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The minimum variance distortionless response beamformer maximizes the signal-to-interference-and-noise ratio (SINR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize the SINR as well. We consider a system with beamforming capabilities in the receiver, and power control. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converges to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario, it is shown how base assignment can be incorporated in addition to beamforming and power control, such that a globally optimum solution is obtained. The network capacity and the saving in mobile power are evaluated through numerical study.

We develop an algorithm to reconstruct the wavelet coefficients of an image from the Radon transform data. The proposed method uses the properties of wavelets to localize the Radon transform and can be used to reconstruct a local region of the cross section of a body, using almost completely local data that significantly reduces the amount of exposure and computations in X-ray tomography. The property that distinguishes our algorithm from the previous algorithms is based on the observation that for some wavelet bases with sufficiently many vanishing moments, the ramp-filtered version of the scaling function as well as the wavelet function has extremely rapid decay. We show that the variance of the elements of the null-space is negligible in the locally reconstructed image. Also, we find an upper bound for the reconstruction error in terms of the amount of data used in the algorithm. To reconstruct a local region 16 pixels in radius in a 256x256 image, we require 22% of full exposure data.

An algorithm is developed for wavelet based multiresolution reconstruction of a function directly from its fan beam projections. The algorithm uses the locality of wavelet and scaling functions to reconstruct a local region of interest with reduced exposure. In other words, our algorithm saves the radiation exposure in the cases where only a local region of the cross section of a body is to be reconstructed. For example, in order to reconstruct a region of 16 pixels in radius in a 256/spl times/255 image, our method can reduce the X-ray dosage down to 22% of full exposure.

We develop an algorithm to reconstruct the wavelet coefficients of an image from the Radon transform data. The proposed method uses the properties of wavelets to localize the Radon transform and can be used to reconstruct a local region of the cross section of a body, using almost completely local data which significantly reduces the amount of exposure and computations in X-ray tomography. For example, for a local region of radius 20 pixels in a 256/spl times/256 image the proposed method can reduce the exposure to 12.5% of the conventional filtered backprojection method. Compared to the existing schemes, which can only reduce to 40%.