Jiaheng Wang

Worst-Case Robust MIMO Transmission With Imperfect Channel Knowledge

In this paper, we consider robust transmit strategies, against the imperfectness of the channel state information at the transmitter (CSIT), for multi-input multi-output (MIMO) communication systems. Following a worst-case deterministic model, the actual channel is assumed to be inside an ellipsoid centered at a nominal channel. The objective is to maximize the worst-case received signal-to-noise ratio (SNR), or to minimize the worst-case Chernoff bound of the error probability, thus leading to a maximin problem. Moreover, we also consider the QoS problem, as a complement of the maximin design, which minimizes the transmit power consumption and meanwhile keeps the received SNR above a given threshold for any channel realization in the ellipsoid. It is shown that, for a general class of power constraints, both the maximin and QoS problems can be equivalently transformed into convex problems, or even further into semidefinite programs (SDPs), thus efficiently solvable by the numerical methods. The most interesting result is that the optimal transmit directions, i.e., the eigenvectors of the transmit covariance, are just the right singular vectors of the nominal channel under some mild conditions. This result leads to a channel-diagonalizing structure, as in the cases of perfect CSIT and statistical CSIT with mean or covariance feedback, and reduces the complicated matrix-valued problem to a scalar power allocation problem. Then we provide the closed-form solution to the resulting power allocation problem.

Robust MIMO Cognitive Radio Via Game Theory

Cognitive radio (CR) systems improve the spectral efficiency by allowing the coexistence in harmony of primary users (PUs), the legacy users, with secondary users (SUs). This coexistence is built on the premises that no SU can generate interference higher than some prescribed limits against PUs. The system design based on perfect channel state information (CSI) can easily end up violating the interference limits in a realistic situation where CSI may be imperfect. In this paper, we propose a robust design of CR systems, composed of multiple PUs and multiple noncooperative SUs, in either single-input single-output (SISO) frequency-selective channels or more general multiple-input multiple-output (MIMO) channels. We formulate the design of the SU network as a noncooperative game, where the SUs compete with each other over the resources made available by the PUs, by maximizing their own information rates subject to the transmit power and robust interference constraints. Following the philosophy of the worst-case robustness, we take explicitly into account the imperfectness of SU-to-PU CSI by adopting proper interference constraints that are robust with respect to the worst channel errors. Relying on the variational inequality theory, we study the existence and uniqueness properties of the Nash equilibria of the resulting robust games, and devise totally asynchronous and distributed algorithms along with their convergency properties. We also propose efficient numerical methods, based on decomposition techniques, to compute the robust transmit strategy for each SU.

Resource Sharing of Underlaying Device-to-Device and Uplink Cellular Communications

The benefit of device-to-device (D2D) communication hinges on intelligent resource sharing between cellular and D2D users. This letter aims to optimize resource sharing for D2D communication to better utilize uplink resources in a multi-user cellular system with guaranteed quality of normal cellular communications. Despite the nonconvex difficulty, we provide an analytical characterization of the globally optimal resource sharing strategy, and furthermore propose two suboptimal strategies with less complexity. The superiority of the proposed resource sharing strategies is demonstrated through numerical examples.

Downlink Resource Reuse for Device-to-Device Communications Underlaying Cellular Networks

The full potential of Device-to-device (D2D) communication relies on efficient resource reuse strategies including power control and matching of D2D links and cellular users (CUs). This letter investigates downlink resource reuse between multiple D2D links and multiple CUs. Our goal is to achieve a network utility enhancement for D2D communication while ensuring the QoS of the CUs. Despite the combinatorial nature of the problem and the coupled power constraints, we characterize the optimal D2D-CU matching as well as their power coordination, and propose an efficient algorithm to jointly optimize all D2D links and CUs. The proposed downlink resource reuse strategy shows a superiority over existing D2D schemes.

Visible light communications in heterogeneous networks: Paving the way for user-centric design

At the time of this writing, there is substantial research interest in the subject of visible light communications (VLC) owing to its ability to offer significant traffic offloading potential in highly crowded radio frequency (RF) scenarios. We introduce the user-centric design of VLC for heterogeneous networks (HetNet), where three key aspects are identified and elaborated on: signal coverage quality, system control, and service provision aspects. More explicitly, the concepts of amorphous cell formation as well as separated up-link (UL) and down-link (DL), decoupled data and control, dynamic load balancing (LB), etc., all demand radically new thinking. The advocated user-centric VLC design is of key significance in the small-cell scenarios of the emerging 5G design philosophy.

Robust MMSE Precoding in MIMO Channels With Pre-Fixed Receivers

In this paper, we design robust precoders, under the minimum mean square error (MMSE) criterion, for different types of channel state information (CSI) in multiple-input multiple-output (MIMO) channels. We consider low-complexity pre-fixed receivers that may adapt to the channel but are oblivious to the existence of a precoder at the transmitter. In particular, three types of CSI are taken into account: i) perfect CSI, ii) statistical CSI in the form of mean feedback, and iii) deterministic imperfect CSI assuming that the actual channel is within the neighborhood of a nominal channel, which leads to the worst-case robust design that is the focus of this paper. Interestingly, it is found that, under some mild conditions, the optimal transmit directions, i.e., the left singular vectors of the precoder, are equal to the right singular vectors of the channel, the channel mean, and the nominal channel for perfect CSI, statistical CSI, and the worst-case design, respectively. Consequently, the matrix-valued problems can be simplified to scalar power allocation problems that either admit closed-form solutions or can be efficiently solved by the proposed algorithm.

Robust MIMO precoding for the schatten norm based channel uncertainty sets

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.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.

Performance of Secure Communications Over Correlated Fading Channels

We study the performance of secure communications over correlated fading channels in the presence of an eavesdropper, where the main and eavesdropper channels are correlated. We derive exact expressions for both the average secrecy capacity and the outage probability in the form of infinite series. Moreover, the truncated error of the infinite series representations involved in the analytical results is also investigated. The accuracy of our performance analysis is verified by simulation results.

Multiuser MISO Transceiver Design for Indoor Downlink Visible Light Communication Under Per-LED Optical Power Constraints

Light-emitting diode (LED)-based visible light communication (VLC), combining illumination and communication, is a promising technique for providing high-speed, low-cost indoor wireless services. In indoor environments, multiple LEDs routinely used as lighting sources may also be concomitantly invoked to support wireless services for multiple users, thus forming a multiuser multiple-input-single-output (MU-MISO) system. Since the user terminals detect all the light rays impinging from multiple LEDs, inter-user interference may severely degrade the attainable system performance. Hence, we conceive a transceiver design for indoor VLC MU-MISO systems to suppress the multiuser interference (MUI). In contrast to classic radio-frequency (RF) communication, in VLC, the signals transmitted from the LEDs are restricted by optical constraints, such as the real-valued nonnegativity of the optical signal, the maximum permissible optical intensity, and the constant brightness requirements of the LEDs. Given these practical constraints, we design the optimal transceiver relying on the objective function of minimizing the maximum mean square error (MMSE) between the legitimate transmitted and received signals of the users and show that it can be readily found by solving a convex second-order cone program. Then, we also propose a simplified transceiver design by incorporating zero-forcing (ZF) transmit precoding (TPC) and show that the TPC coefficients can be efficiently found by solving a linear program. The performance of both the optimal and the simplified transceiver is characterized by comprehensive numerical results under diverse practical VLC system setups.

Energy Efficient Spectrum Sharing Strategy Selection for Cognitive MIMO Interference Channels

In this paper, we propose a promising discrete game-theoretic framework for distributed energy efficient discrete spectrum sharing strategy selection (i.e., joint discrete power control and multimode precoding strategy selection) with limited feedback for cognitive MIMO interference channels. Given the competitive nature, the secondary users are assumed to be selfish and noncooperative, each of whom attempts to maximize its individual energy efficiency under a minimum data rate constraint and an interference power constraint. A mechanism for shutting down links is proposed to reduce interference and save energy. A payoff function is designed to guarantee the feasibility of the pure strategy Nash equilibrium with no need to know the infeasible strategy profiles (a spectrum sharing strategy profile is said to be feasible if the stated constraints are satisfied; otherwise, the spectrum sharing strategy profile is said to be infeasible, i.e., they may not satisfy the interference power constraint and minimum data rate constraint) in advance. We then investigate the existence and the feasibility of the pure strategy Nash equilibrium, and further devise a pricing-based distributed algorithm for spectrum strategy selection. The proposed algorithm is proved to converge to a feasible pure strategy Nash equilibrium under specific conditions. Moreover, by studying the relationship between our proposed game and the social optimum, we find that the pricing mechanism can result in Pareto improvement and lead to better convergence for the proposed distributed algorithm. Numerical results show that our designed algorithm significantly outperforms the random selection algorithm and the pricing mechanism has a dramatic effect in improving the system performance.