References of "Wang, J"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailCodebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems
He, S.; Wang, J.; Huang, Y. et al

in IEEE Transactions on Signal Processing (2017), 65(20), 5289-5304

Detailed reference viewed: 59 (3 UL)
Full Text
Peer Reviewed
See detailPer-antenna constant envelope precoding and antenna subset selection: A geometric approach
Zhang, J.; Huang, Y.; Wang, J. et al

in IEEE Transactions on Signal Processing (2016), 64(23), 6089-6104

Constant envelope (CE) precoding can efficiently control the peak-to-average power ratio (PAPR) and improve the power efficiency of power amplifiers in large-scale antenna array systems. Antenna subset ... [more ▼]

Constant envelope (CE) precoding can efficiently control the peak-to-average power ratio (PAPR) and improve the power efficiency of power amplifiers in large-scale antenna array systems. Antenna subset selection (ASS), combined with CE precoding, can further improve power efficiency by using a part of antennas to combine the desired signal. However, due to the inherent nonlinearity, the joint optimization of CE precoding and ASS is very challenging and satisfactory solutions are yet not available. In this paper, we present new methods for CE precoding and ASS optimization from a geometric perspective. First, we show the equivalence between the CE precoder design and a polygon construction problem in the complex plane, thus transforming the algebraic problem into a geometric problem. Aiming to minimize the computational complexity, we further transform the CE precoder design into a triangle construction problem, and propose a novel algorithm to achieve the optimal CE precoder with only linear complexity in the number of used antennas. Then, we investigate the joint optimization of ASS and CE precoding to minimize the total transmit power while satisfying the QoS requirement. Based on the geometric interpretation, we develop an efficient ASS algorithm, which, using only addition and comparison operations, is guaranteed to find the globally optimal solution and provides robustness to channel uncertainty. The complexity of the proposed ASS algorithm is at most quadratic in the number of antennas in the worst case. The optimality and superiority of the proposed geometric methods are demonstrated via numerical results. [less ▲]

Detailed reference viewed: 98 (0 UL)
See detailAmber 14
Case, D. A.; Babin, V.; Berryman, Josh UL et al

Book published by University of California (2014)

Detailed reference viewed: 1848 (8 UL)
Full Text
Peer Reviewed
See detailRobust MIMO precoding for several classes of channel uncertainty
Wang, J.; Bengtsson, M.; Ottersten, Björn UL et al

in IEEE Transactions on Signal Processing (2013), 12(61), 3056-3070

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty ... [more ▼]

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated. [less ▲]

Detailed reference viewed: 68 (0 UL)
Full Text
Peer Reviewed
See detailRobust MIMO Precoding for the Schatten Norm Based Channel Uncertainty Set
Wang, J.; Bengtsson, M.; Ottersten, Björn UL et al

in Signal Processing, IEEE Transactions on (2013)

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty ... [more ▼]

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated. [less ▲]

Detailed reference viewed: 73 (1 UL)