Alireza Shahan Behbahani

Optimizations of a MIMO Relay Network

Relay networks have received considerable attention recently, especially when limited size and power resources impose constraints on the number of antennas within a wireless sensor network. In this context, signal processing techniques play a fundamental role, and optimality within a given relay architecture can be achieved under several design criteria. In this paper, we extend recent optimal minimum-mean-square-error (MMSE) and signal-to-noise ratio (SNR) designs of relay networks to the corresponding multiple-input-multiple-output (MIMO) scenarios, whereby the source, relays and destination comprise multiple antennas. We investigate maximum SNR solutions subject to power constraints and zero-forcing (ZF) criteria, as well as approximate MMSE equalizers with specified target SNR and power constraint at the receiver. We also maximize the transmission rate between the source and destination subject to power constraint at the receiver.

Linear Decentralized Estimation of Correlated Data for Power-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of an unknown random vector by using wireless sensors and a fusion center (FC). We adopt a linear model for distributed estimation of a vector source where both observation models and sensor operations are linear and the multiple access channel (MAC) is coherent. Two cases are considered: Noiseless fusion center and Noisy fusion center. In the case of a Noiseless fusion center (where there is no noise at the fusion center), the sensor precoders are designed to minimize the mean square error (MSE) at the fusion center. A closed form solution is found and it is shown that the system performance approaches the benchmark as long as the number of messages transmitted by each sensor is equal to the length of the vector source. Subsequently, if there is noise at the fusion center and one is interested in a closed form solution, a filter is designed to cancel out the effect of noise at the fusion center. This separate design will come at the expense of losing performance. An alternative iterative solution can be found when considering noise at the fusion center where sensor precoders are designed to minimize the MSE at the fusion center subject to transmit power constraints at each sensor. It is shown that the proposed scheme always converges. Finally, simulations are provided to verify the analysis and present the performance of the proposed schemes.

Amplify-and-Forward Relay Networks Under Received Power Constraint

Relay networks have received considerable attention recently, especially when limited size and power resources impose constraints on the number of antennas at each node. While fixed and mobile relays can cooperate to improve reception at the desired destination, they also contribute to unintended interference for neighboring cells reusing the same frequency. In this paper, we propose and analyze a relay scheme to simultaneously maximize SNR and minimize MSE, for an amplify-and-forward (AF) relay network operating under a receive power constraint guaranteeing that the received signal power is bounded to control interference to neighboring cells. If the intended destination lies at the periphery of the cell, then the proposed scheme guarantees that the total power leaking into neighboring cells is bounded. The optimal relay factors are provided for both correlated and uncorrelated noise at the relays. Simulation results are presented to verify the analysis.

On Channel Estimation and Capacity for Amplify and Forward Relay Networks

Relay networks have received considerable attention recently, especially when limited size and power resources impose constraints on the number of antennas within a wireless sensor network. In this paper, we design and analyze a training based linear mean square error (LMMSE) channel estimator for time division multiplex amplify-and-forward (AF) relay networks. For the purpose of performance comparison we consider three distinct cases; In the first scenario, each relay estimates its backward and forward channels, in the second scenario each relay knows its backward and forward channels perfectly and finally in the third scenario relays have no knowledge of channels. Finally, we find a lower bound for the capacity considering the effect of training and estimation error.

Decentralized estimation under correlated noise

In this paper, we consider distributed estimation of an unknown random scalar by using wireless sensors and a fusion center (FC). We adopt a linear model for distributed estimation of a scalar source where both observation models and sensor operations are linear, and the multiple access channel (MAC) is coherent. We consider a fusion center with multiple antennas and single antenna. In order to estimate the source, best linear unbiased estimation (BLUE) is adopted. Two cases are considered: Minimization of the mean square error (MSE) of the BLUE estimator subject to network power constraint, and minimization of the network power subject to the quality of service (QOS). For a fusion center with multiple antennas, iterative solutions are provided and it is shown that the proposed algorithms always converge. For a fusion center with single antenna, closed-form solutions are provided, and it is shown that the iterative solutions will reduce to the closed-form solutions. Furthermore, the effect of noise correlation at the sensors and fusion center is investigated. It is shown that knowledge of noise correlation at the sensors will help to improve the system performance. Moreover, if correlation exists and not factored in, the system performance might improve depending on the correlation structure. We also show, by simulations, that when noise at the fusion center is correlated, even with knowing the correlation structure, the system performance degrades. Finally, simulations are provided to verify the analysis and present the performance of the proposed schemes.

On Signal Processing Methods for MIMO Relay Architectures

Relay networks have received considerable attention recently, especially when limited size and power resources impose constraints on the number of antennas within a wireless sensor network. In this context, signal processing techniques play a fundamental role, and optimality within a given relay architecture can be achieved under several design criteria. In this paper, we extend recent optimal minimum-mean-square-error (MMSE) and SNR designs of relay networks to the corresponding multiple- input-multiple-output (MIMO) scenarios, whereby the source, relays and destination comprise multiple antennas. We shall investigate maximum SNR solutions subject to power constraints and zero-forcing (ZF) criteria, as well as approximate MMSE equalizers with specified target SNR and global power constraint.

Linear Estimation of Correlated Vector Sources for Wireless Sensor Networks with Fusion Center

In this letter, we consider decentralized estimation of unknown random vectors by using wireless sensors and a fusion center (FC). We adopt a linear model for decentralized estimation of vector sources where both observation models and sensor operations are linear and the multiple access channel (MAC) is coherent. Each sensor observes a different vector source, where vector sources can be correlated. Sensors are designed to minimize the total mean square error (MSE) at the fusion center subject to transmit power constraints at the sensors. While a closed form solution cannot be found, an iterative algorithm is proposed. Simulations are provided to verify the analysis and present the performance of the proposed scheme.

On optimizing the performance of interference-limited cellular systems

Multi Input Multi Output (MIMO) technology has seen prolific use to achieve higher data rates and an improved communication experience for cellular systems. However, one of the challenging problems in MIMO systems is interference. Interference limits the system performance in terms of rate and reliability. In this paper, we analyze a novel method that provides high performance over interference-limited cellular networks such as Long Term Evolution (LTE). Our proposed algorithm includes an optimized solution that models the interference as correlated noise, and uses its statistical information to jointly optimize the base station precoding and user receiver design of LTE systems. We study the benefits of exploiting interference in terms of both probability of error and signal-to-noise ratio (SNR). In addition, we compare the proposed method with the conventional beamforming and maximum ratio combining (MRC).

High SNR Linear Estimation of Vector Sources

In this letter, we extend our prior work and consider decentralized estimation of unknown random vectors under high observation signal-to-noise ratio (SNR). A linear model is considered for decentralized estimation of vector sources. Observation models and sensor operations are both linear. Furthermore, the channel between the wireless sensors and fusion center (FC) is a coherent multiple access channel (MAC). Each sensor observes a different vector source. Sensors are designed to minimize the total mean square error (MSE) at the FC subject to the individual transmit power constraints at the sensors. We first provide the solution for scalar sources under high observation SNR regime. Then, we use the provided solution for scalar sources and extend it to the case of vector sources.

Amplify-and-Forward Relay Networks under Received Power Constraint with Imperfect CSI

The mobility of relay stations within a cell creates an interesting scenario where multiple sources, transmitting correlated data, can co-operate to satisfy power constraints at the receiving node. While beneficial to the receive node, this approach creates un-intended interference for neighboring cells reusing the same frequency. Previously, a relay scheme was proposed to simultaneously maximize SNR and minimize MSE, for an amplify-and-forward (AF) relay network operating under a receive power constraint guaranteeing that the received signal power is bounded to control interference to neighboring cells. In this paper, we investigate the effect of channel uncertainties on system performance. A modified solution for the proposed scheme under imperfect channel state information (CSI) is provided. Furthermore, we investigate the diversity order of the proposed scheme under perfect CSI. Simulations are provided to verify the analysis for both perfect and imperfect CSI assumptions.