arXiv subjects
Publications and source records attributed to Xiaodong Wang.
We discuss a sharp lower bound for the first positive eigenvalue of the sublaplacian on a closed, strictly pseudoconvex pseudo-hermitian manifold of dimension $2m+1\geq 5$. We prove that the equality holds iff the manifold is equivalent to the CR sphere up to a scaling. The essential step is a characterization of the CR sphere when there is a nonzero function satisfying a certain overdetermined system.
In this paper we study a single player game consisting of $n$ black checkers and $m$ white checkers, called shifting the checkers. We have proved that the minimum number of steps needed to play the game for general $n$ and $m$ is $nm + n + m$. We have also presented an optimal algorithm to generate an optimal move sequence of the game consisting of $n$ black checkers and $m$ white checkers, and finally, we present an explicit solution for the general game.
We investigate the upper and lower bounds on the quantization distortions for independent and identically distributed sources in the finite block-length regime. Based on the convex optimization framework of the rate-distortion theory, we derive a lower bound on the quantization distortion under finite block-length, which is shown to be greater than the asymptotic distortion given by the rate-distortion theory. We also derive two upper bounds on the quantization distortion based on random quantization codebooks, which can achieve any distortion above the asymptotic one. Moreover, we apply the new upper and lower bounds to two types of sources, the discrete binary symmetric source and the continuous Gaussian source. For the binary symmetric source, we obtain the closed-form expressions of the upper and lower bounds. For the Gaussian source, we propose a computational tractable method to numerically compute the upper and lower bounds, for both bounded and unbounded quantization codebooks.Numerical results show that the gap between the upper and lower bounds is small for reasonable block length and hence the bounds are tight.
We propose a scalable video broadcasting scheme over MIMO-OFDM systems. The scalable video source layers are channel encoded and modulated into independent signal streams, which are then transmitted from the allocated antennas in certain time-frequency blocks. Each receiver employs the successive group decoder to decode the signal streams of interest by treating other signal streams as interference. The transmitter performs adaptive coding and modulation, and transmission antenna and subcarrier allocation, based on the rate feedback from the receivers. We also propose a hybrid receiver that switches between the successive group decoder and the MMSE decoder depending on the rate. Extensive simulations are provided to demonstrate the performance gain of the proposed group-decoding-based scalable video broadcasting scheme over the one based on the conventional MMSE decoding.
This paper has been withdrawn by the authors. Please see arXiv:1302.6058. We consider the sequential joint detection and estimation problem. Minimizing the average stopping time subject to a combination of detection and estimation constraints we obtain the optimal triplet of stopping time, detector and estimator. In the joint detection and estimation problem the primary goal is to detect and estimate together, as opposed to the conventional testing of composite hypotheses where the primary goal is to detect only. For the first time in the literature we develop optimal solution to the sequential joint detection and estimation problem. In the sequential version of the problem, different from the fixed sample size version, optimal stopping time is also sought, complicating the solution considerably.
We consider power allocations in downlink cellular wireless systems where the basestations are equipped with multiple transmit antennas and the mobile users are equipped with single receive antennas. Such systems can be modeled as multiuser MISO systems. We assume that the multi-antenna transmitters employ some fixed beamformers to transmit data, and the objective is to optimize the power allocation for different users to satisfy certain QoS constraints, with imperfect transmitter-side channel state information (CSI). Specifically, for MISO interference channels, we consider the transmit power minimization problem and the max-min SINR problem. For MISO broadcast channels, we consider the MSE-constrained transmit power minimization problem. All these problems are formulated as probability-constrained optimization problems. We make use of the Bernstein approximation to conservatively transform the probabilistic constraints into deterministic ones, and consequently convert the original stochastic optimization problems into convex optimization problems. However, the transformed problems cannot be straightforwardly solved using standard solver, since one of the constraints is itself an optimization problem. We employ the long-step logarithmic barrier cutting plane (LLBCP) algorithm to overcome difficulty. Extensive simulation results are provided to demonstrate the effectiveness of the proposed method, and the performance advantage over some existing methods.
In this paper, we consider a generalized longest common subsequence problem with multiple substring exclusion constrains. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constrains $P=\{P_1,...,P_d\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ excluding each of constrain string in $P$ as a substring and the length of $Z$ is maximized. The problem was declared to be NP-hard\cite{1}, but we finally found that this is not true. A new dynamic programming solution for this problem is presented in this paper. The correctness of the new algorithm is proved. The time complexity of our algorithm is $O(nmr)$.
In this paper, we consider a generalized longest common subsequence problem, the string-excluding constrained LCS problem. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a constraint string $P$ of length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ excluding $P$ as a substring and the length of $Z$ is maximized. The problem and its solution were first proposed by Chen and Chao\cite{1}, but we found that their algorithm can not solve the problem correctly. A new dynamic programming solution for the STR-EC-LCS problem is then presented in this paper. The correctness of the new algorithm is proved. The time complexity of the new algorithm is $O(nmr)$.
We study the degrees of freedom (DOF) of a multiple-input multiple-output (MIMO) two-way X relay channel, where there are two groups of source nodes and one relay node, each equipped with multiple antennas, and each of the two source nodes in one group exchanges independent messages with the two source nodes in the other group via the relay node. It is assumed that every source node is equipped with M antennas while the relay is equipped with N antennas. We first show that the upper bound on the total DOF for this network is 2min{2M,N} and then focus on the case of N \leq 2M so that the DOF is upper bounded by the number of antennas at the relay. By applying signal alignment for network coding and joint transceiver design for interference cancellation, we show that this upper bound can be achieved when N \leq8M/5. We also show that with signal alignment only but no joint transceiver design, the upper bound is achievable when N\leq4M/3. Simulation results are provided to corroborate the theoretical results and to demonstrate the performance of the proposed scheme in the finite signal-to-noise ratio regime.
We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.
We consider decentralized detection through distributed sensors that perform level-triggered sampling and communicate with a fusion center via noisy channels. Each sensor computes its local log-likelihood ratio (LLR), samples it using the level-triggered sampling, and upon sampling transmits a single bit to the FC. Upon receiving a bit from a sensor, the FC updates the global LLR and performs a sequential probability ratio test (SPRT) step. We derive the fusion rules under various types of channels. We further provide an asymptotic analysis on the average detection delay for the proposed channel-aware scheme, and show that the asymptotic detection delay is characterized by a KL information number. The delay analysis facilitates the choice of appropriate signaling schemes under different channel types for sending the 1-bit information from sensors to the FC.
We address the problem of downlink beamforming for mitigating the co-channel interference in multi-cell OFDMA networks. Based on the network utility maximization framework, we formulate the problem as a non-convex optimization problem subject to the per-cell power constraints, in which a general utility function of SINR is used to characterize the network performance. Some classical utility functions, such as the proportional fairness utility, the weighted sum-rate utility and the {$α$}-fairness utility, are subsumed as special cases of our formulation. To solve the problem in a distributed fashion, we devise an algorithm based on the non-cooperative game with pricing mechanism. We give a sufficient condition for the convergence of the algorithm to the Nash equilibrium (NE), and analyze the information exchange overhead among the base stations. Moreover, to speed up the optimization of the beam-vectors at each cell, we derive an efficient algorithm to solve for the KKT conditions at each cell. We provide extensive simulation results to demonstrate that the proposed distributed multi-cell beamforming algorithm converges to an NE point in just a few iterations with low information exchange overhead. Moreover, it provides significant performance gains, especially under the strong interference scenario, in comparison with several existing multi-cell interference mitigation schemes, such as the distributed interference alignment method.
Cognitive radios process their sensed information collectively in order to opportunistically identify and access under-utilized spectrum segments (spectrum holes). Due to the transient and rapidly-varying nature of the spectrum occupancy, the cognitive radios (secondary users) must be agile in identifying the spectrum holes in order to enhance their spectral efficiency. We propose a novel {\em adaptive} procedure to reinforce the agility of the secondary users for identifying {\em multiple} spectrum holes simultaneously over a wide spectrum band. This is accomplished by successively {\em exploring} the set of potential spectrum holes and {\em progressively} allocating the sensing resources to the most promising areas of the spectrum. Such exploration and resource allocation results in conservative spending of the sensing resources and translates into very agile spectrum monitoring. The proposed successive and adaptive sensing procedure is in contrast to the more conventional approaches that distribute the sampling resources equally over the entire spectrum. Besides improved agility, the adaptive procedure requires less-stringent constraints on the power of the primary users to guarantee that they remain distinguishable from the environment noise and renders more reliable spectrum hole detection.
We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers, called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user will send one (or several) bit of information to the fusion center. The fusion center asynchronously receives the bits from different secondary users and updates the global sensing decision statistic to perform a sequential probability ratio test (SPRT), to reach a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Simulation results show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits under changing target error probabilities, SNR values, and number of SUs.
Concurrent presence of inter-cell and intra-cell interferences constitutes a major impediment to reliable downlink transmission in multi-cell multiuser networks. Harnessing such interferences largely hinges on two levels of information exchange in the network: one from the users to the base-stations (feedback) and the other one among the base-stations (cooperation). We demonstrate that exchanging a finite number of bits across the network, in the form of feedback and cooperation, is adequate for achieving the optimal capacity scaling. We also show that the average level of information exchange is independent of the number of users in the network. This level of information exchange is considerably less than that required by the existing coordination strategies which necessitate exchanging infinite bits across the network for achieving the optimal sum-rate capacity scaling. The results provided rely on a constructive proof.
We consider application of the discrete Fourier transform-spread orthogonal frequency-division multiplexing (DFT-spread OFDM) technique to high-speed fiber optic communications. The DFT-spread OFDM is a form of single-carrier technique that possesses almost all advantages of the multicarrier OFDM technique (such as high spectral efficiency, flexible bandwidth allocation, low sampling rate and low-complexity equalization). In particular, we consider the optical DFT-spread OFDM system with polarization division multiplexing (PDM) that employs a tone-by-tone linear minimum mean square error (MMSE) equalizer. We show that such a system offers a much lower peak-to-average power ratio (PAPR) performance as well as better bit error rate (BER) performance compared with the optical OFDM system that employs amplitude clipping.
We consider a well defined joint detection and parameter estimation problem. By combining the Baysian formulation of the estimation subproblem with suitable constraints on the detection subproblem we develop optimum one- and two-step test for the joint detection/estimation case. The proposed combined strategies have the very desirable characteristic to allow for the trade-off between detection power and estimation efficiency. Our theoretical developments are then applied to the problems of retrospective changepoint detection and MIMO radar. In the former case we are interested in detecting a change in the statistics of a set of available data and provide an estimate for the time of change, while in the latter in detecting a target and estimating its location. Intense simulations demonstrate that by using the jointly optimum schemes, we can experience significant improvement in estimation quality with small sacrifice in detection power.
In this note, we prove the following generalization of a theorem of Shi and Tam \cite{ShiTam02}: Let $(Ω, g)$ be an $n$-dimensional ($n \geq 3$) compact Riemannian manifold, spin when $n>7$, with non-negative scalar curvature and mean convex boundary. If every boundary component $Σ_i$ has positive scalar curvature and embeds isometrically as a mean convex star-shaped hypersurface ${\hat Σ}_i \subset \R^n$, then \int_{Σ_i} H d σ\le \int_{{\hat Σ}_i} \hat{H} d {\hat σ} where $H$ is the mean curvature of $Σ_i$ in $(Ω, g)$, $\hat{H}$ is the Euclidean mean curvature of ${\hat Σ}_i$ in $\R^n$, and where $d σ$ and $d {\hat σ}$ denote the respective volume forms. Moreover, equality in (\ref{eqn: main theorem}) holds for some boundary component $Σ_i$ if, and only if, $(Ω, g)$ is isometric to a domain in $\R^n$. In the proof, we make use of a foliation of the exterior of the $\hat Σ_i$'s in $\R^n$ by the $\frac{H}{R}$-flow studied by Gerhardt \cite{Gerhardt90} and Urbas \cite{Urbas90}. We also carefully establish the rigidity statement in low dimensions without the spin assumption that was used in \cite{ShiTam02}