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Featured researches published by Yue Rong.


IEEE Transactions on Signal Processing | 2009

A Unified Framework for Optimizing Linear Nonregenerative Multicarrier MIMO Relay Communication Systems

Yue Rong; Xiaojun Tang; Yingbo Hua

In this paper, we develop a unified framework for linear nonregenerative multicarrier multiple-input multiple-output (MIMO) relay communications in the absence of the direct source-destination link. This unified framework classifies most commonly used design objectives such as the minimal mean-square error and the maximal mutual information into two categories: Schur-concave and Schur-convex functions. We prove that for Schur-concave objective functions, the optimal source precoding matrix and relay amplifying matrix jointly diagonalize the source-relay-destination channel matrix and convert the multicarrier MIMO relay channel into parallel single-input single-output (SISO) relay channels. While for Schur-convex objectives, such joint diagonalization occurs after a specific rotation of the source precoding matrix. After the optimal structure of the source and relay matrices is determined, the linear nonregenerative relay design problem boils down to the issue of power loading among the resulting SISO relay channels. We show that this power loading problem can be efficiently solved by an alternating technique. Numerical examples demonstrate the effectiveness of the proposed framework.


IEEE Journal on Selected Areas in Communications | 2012

A Tutorial on the Optimization of Amplify-and-Forward MIMO Relay Systems

Luca Sanguinetti; Antonio Alberto D'Amico; Yue Rong

The remarkable promise of multiple-input multiple-output (MIMO) wireless channels has motivated an intense research activity to characterize the theoretical and practical issues associated with the design of transmit (source) and receive (destination) processing matrices under different operating conditions. This activity was primarily focused on point-to-point (single-hop) communications but more recently there has been an extensive work on two-hop or multi-hop settings in which single or multiple relays are used to deliver the information from the source to the destination. The aim of this tutorial is to provide an up-to-date overview of the fundamental results and practical implementation issues in designing amplify-and-forward MIMO relay systems.


IEEE Transactions on Wireless Communications | 2009

Optimality of diagonalization of multi-hop MIMO relays

Yue Rong; Yingbo Hua

For a two-hop linear non-regenerative multiple-input multiple-output (MIMO) relay system where the direct link between source and destination is negligible, the optimal design of the source and relay matrices has been recently established for a broad class of objective functions. The optimal source and relay matrices jointly diagonalize the MIMO relay system into a set of parallel scalar channels. In this paper, we show that this diagonalization is also optimal for a multihop MIMO relay system with any number of hops, which is a further generalization of several previously established results. Specifically, for Schur-concave objective functions, the optimal source precoding matrix, the optimal relay amplifying matrices and the optimal receiving matrix jointly diagonalize the multihop MIMO relay channel. And for Schur-convex objectives, such joint diagonalization along with a rotation of the source precoding matrix is also shown to be optimal. We also analyze the system performance when each node has the same transmission power budget and the same asymptotically large number of antennas. The asymptotic analysis shows a good agreement with numerical results under a finite number of antennas.


IEEE Transactions on Signal Processing | 2005

Robust iterative fitting of multilinear models

Sergiy A. Vorobyov; Yue Rong; Nicholas D. Sidiropoulos; Alex B. Gershman

Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher way arrays, also referred to as tensors. It decomposes a given array in a sum of multilinear terms, analogous to the familiar bilinear vector outer products that appear in matrix decomposition. PARAFAC analysis generalizes and unifies common array processing models, like joint diagonalization and ESPRIT; it has found numerous applications from blind multiuser detection and multidimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on (alternating) least squares, which is optimal for Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop two iterative algorithms for the least absolute error fitting of general multilinear models. The first is based on efficient interior point methods for linear programming, employed in an alternating fashion. The second is based on a weighted median filtering iteration, which is particularly appealing from a simplicity viewpoint. Both are guaranteed to converge in terms of absolute error. Performance is illustrated by means of simulations, and compared to the pertinent Crame/spl acute/r-Rao bounds (CRBs).


IEEE Journal on Selected Areas in Communications | 2006

Robust linear receivers for multiaccess space-time block-coded MIMO systems: a probabilistically constrained approach

Yue Rong; Sergiy A. Vorobyov; Alex B. Gershman

Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs


IEEE Transactions on Signal Processing | 2014

Achievable Rates of Full-Duplex MIMO Radios in Fast Fading Channels With Imperfect Channel Estimation

Ali Cagatay Cirik; Yue Rong; Yingbo Hua

We study the theoretical performance of two full-duplex multiple-input multiple-output (MIMO) radio systems: a full-duplex bi-directional communication system and a full-duplex relay system. We focus on the effect of a (digitally manageable) residual self-interference due to imperfect channel estimation (with independent and identically distributed (i.i.d.) Gaussian channel estimation error) and transmitter noise. We assume that the instantaneous channel state information (CSI) is not available the transmitters. To maximize the system ergodic mutual information, which is a nonconvex function of power allocation vectors at the nodes, a gradient projection algorithm is developed to optimize the power allocation vectors. This algorithm exploits both spatial and temporal freedoms of the source covariance matrices of the MIMO links between transmitters and receivers to achieve higher sum ergodic mutual information. It is observed through simulations that the full-duplex mode is optimal when the nominal self-interference is low, and the half-duplex mode is optimal when the nominal self-interference is high. In addition to an exact closed-form ergodic mutual information expression, we introduce a much simpler asymptotic closed-form ergodic mutual information expression, which in turn simplifies the computation of the power allocation vectors.


IEEE Communications Letters | 2010

Optimal joint source and relay beamforming for MIMO relays with direct link

Yue Rong

In this letter, we investigate the optimal structure of the source precoding matrix and the relay amplifying matrix for non-regenerative multiple-input multiple-output (MIMO) relay communication systems with the direct source-destination link. We show that both the optimal source precoding matrix and the optimal relay amplifying matrix have a beamforming structure. Based on this structure, an iterative joint source and relay beamforming algorithm is developed to minimize the mean-squared error (MSE) of the signal waveform estimation. Numerical example demonstrates an improved performance of the proposed algorithm.


IEEE Transactions on Signal Processing | 2005

Blind spatial signature estimation via time-varying user power loading and parallel factor analysis

Yue Rong; Sergiy A. Vorobyov; Alex B. Gershman; Nicholas D. Sidiropoulos

In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Crame/spl acute/r-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensor array manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation.


IEEE Transactions on Wireless Communications | 2012

Channel Estimation of Dual-Hop MIMO Relay System via Parallel Factor Analysis

Yue Rong; Muhammad R. A. Khandaker; Yong Xiang

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.


IEEE Transactions on Signal Processing | 2011

Robust Design for Linear Non-Regenerative MIMO Relays With Imperfect Channel State Information

Yue Rong

In this correspondence, we address statistically robust multiple-input multiple-output (MIMO) relay design problems under two imperfect channel state information (CSI) scenarios: (1) a ll nodes have imperfect CSI and (2) the destination node knows the exact CSI, while the other nodes have imperfect CSI. For each scenario, we develop robust source and relay matrices by considering a broad class of frequently used objective functions in MIMO system design and the averaged transmission power constraints. Simulation results demonstrate the improved robustness of the proposed algorithms against CSI errors.

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Alex B. Gershman

Technische Universität Darmstadt

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Zhiqiang He

Beijing University of Posts and Telecommunications

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Yingbo Hua

University of California

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