Sheng Chen

University of Southampton, Southampton, England, United Kingdom

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Publications (229)261.09 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: In the past few decades, the world has witnessed a rapid growth in mobile communication and reaped great benefits from it. Even though the fourth generation (4G) mobile communication system is just being deployed worldwide, proliferating mobile demands call for newer wireless communication technologies with even better performance. Consequently, the fifth generation (5G) system is already emerging in the research field. However, simply evolving the current mobile networks can hardly meet such great expectations, because over the years the infrastructures have generally become ossified, closed, and vertically constructed. Aiming to establish a new paradigm for 5G mobile networks, in this article, we propose a cross-layer software-defined 5G network architecture. By jointly considering both the network layer and the physical layer together, we establish the two software-defined programmable components, the control plane and the cloud computing pool, which enable an effective control of the mobile network from the global perspective and benefit technological innovations. Specifically, by the cross-layer design for software-defining, the logically centralized and programmable control plane abstracts the control functions from the network layer down to the physical layer, through which we achieve the fine-grained controlling of mobile network, while the cloud computing pool provides powerful computing capability to implement the baseband data processing of multiple heterogeneous networks. We discuss the main challenges of our architecture, including the fine-grained control strategies, network virtualization, and programmability. The architecture significantly benefits the convergence towards heterogeneous networks and it enables much more controllable, programmable and evolvable mobile networks.
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    ABSTRACT: publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10. Abstract—As a highly efficient decoding and demodulation scheme, bit-interleaved coded modulation (BICM) is widely adopted in modern communication systems. In order to enhance the attainable spectral efficiency, usually high-order modulation schemes are used for BICM systems. When combined with iterative demapping and decoding, BICM-ID is capable of further improving the achievable receiver performance. However, the complexity of the standard max-sum approximation of the maximum a posteriori probability in log-domain (Max-Log-MAP) invoked by the iterative demapper is on the order of 2 m or O (2 m) for a 2 m -ary modulation constellation having m bits per symbol, which may become excessive for high-order BICM-ID systems. The existing simplified algorithms employed for non-iterative demappers are based on exploiting the constellation's symmetry, which is no longer retained upon the introduction of the a priori information in BICM-ID systems. Hence in this contribution, a simplified iterative demapping algorithm is proposed for substantially reducing the demapping complexity for binary-reflected Gray-labeled constellation. Our detailed analysis shows that the simplified demapping scheme proposed for BICM-ID reduces the computational complexity to O(m). We demonstrate that this dramatic computational complexity only imposes a modest performance degradation with respect to that of the optimal high-complexity Max-Log-MAP scheme. Index Terms—Bit-interleaved coded modulation with iterative decoding, iterative demapper, maximum a posteriori probability in log-domain demapping, pulse amplitude modulation, quadra-ture amplitude modulation
    IEEE Transactions on Vehicular Technology 10/2014; PP(99):1. · 2.06 Impact Factor
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    ABSTRACT: Graphical abstract Decision boundary produced by the proposed algorithm for synthetic data set, where stars and dots represent two-class data points, respectively, while circles are the selected centres.
    Applied Soft Computing 10/2014; 23:9–18. · 2.68 Impact Factor
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    ABSTRACT: Pilot contamination constitutes a particularly significant impairment in large-scale multi-cell systems. We propose an effective pilot contamination elimination scheme for multi-cell time division duplexing based orthogonal frequency division multiplexing systems, by carefully designing a sophisticated amalgam of downlink (DL) training and ‘scheduled’ uplink (UL) training. During the DL training stage, each base station (BS) transmits the DL pilot symbols (PSs) to its mobile stations (MSs) for them to estimate their frequency-domain channel transfer functions (FDCHTFs), which are then embedded in the UL PSs by ‘predistorting’ the PSs with the estimated FDCHTFs. During the scheduled UL training, each BS's UL receiver in turn extracts the FDCHTFs of its MSs from their received PSs by eliminating the pilot contamination imposed by the simultaneously transmitted UL PSs of all other cells. Our simulation results demonstrate that the pilot contamination is completely eliminated by the proposed scheme, even for the network consisting of a large number of unity frequency reuse cells. Most significantly, unlike many existing pilot contamination reduction schemes, our scheme does not rely on the assumption that each BS knows the second-order statistics of all the interfering UL channels.
    IEEE Journal of Selected Topics in Signal Processing 10/2014; · 3.30 Impact Factor
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    ABSTRACT: Vehicular networks have been attracting increasing attention recently from both the industry and research communities. One of the challenges in this area is understanding vehicular mobility, which is vital for developing accurate and realistic mobility models to aid the vehicular communication and network design and evaluation. Most of the existing works mainly focus on designing microscopic level models that describe the individual mobility behaviors. In this paper, we explore the use of Markov jump process to model the macroscopic level vehicular mobility. Our proposed simple model can accurately describe the vehicular mobility and, moreover, it can predict various measures of network-level performance, such as the vehicular distribution, and vehicular-level performance, such as average sojourn time in each area and the number of sojourned areas in the networks. Model validation based on two large scale urban city vehicular motion traces confirms that this simple model can accurately predict a number of system metrics crucial for vehicular network performance evaluation. Furthermore, we propose two applications to illustrate that the proposed model is effective in analysis of system-level performance and dimensioning for vehicular networks.
    IEEE Transactions on Mobile Computing 09/2014; 13(9):1911-1926. · 2.40 Impact Factor
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    ABSTRACT: This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier׳s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.
    Neurocomputing 08/2014; 138:248–259. · 2.01 Impact Factor
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    ABSTRACT: Orthogonal frequency-division multiplexing (OFDM) has been widely used in visible light communication systems to achieve high-rate data transmission. Due to the nonlinear transfer characteristics of light emitting diodes (LEDs) and owing the high peak-to-average-power ratio of OFDM signals, the transmitted signal has to be scaled and biased before modulating the LEDs. In this contribution, an adaptive scaling and biasing scheme is proposed for OFDM-based visible light communication systems, which fully exploits the dynamic range of the LEDs and improves the achievable system performance. Specifically, the proposed scheme calculates near-optimal scaling and biasing factors for each specific OFDM symbol according to the distribution of the signals, which strikes an attractive trade-off between the effective signal power and the clipping-distortion power. Our simulation results demonstrate that the proposed scheme significantly improves the performance without changing the LED's emitted power, while maintaining the same receiver structure.
    Optics Express 05/2014; 22(10):12707-12715. · 3.55 Impact Factor
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    ABSTRACT: SUMMARYA multistep nonlinear model predictive control (MPC) framework is developed to achieve steady-state offset-free control in the presence of plant–model mismatch. Our formulation explicitly accounts for the effect of plant–model mismatch by involving the output feedback error, which is expressed as the difference between the measured process output and the predicted model output at the previous sampling instance, in the multistep model recursive prediction. The proposed scheme is capable of improving the performance of nonlinear MPC, because the plant–model mismatch is effectively compensated through the recursive prediction propagation. We prove that this formulation is able to remove the steady-state error to achieve offset-free control. The proposed nonlinear MPC framework is applied to a highly nonlinear two-input two-output continuous stirred tank reactor, in comparison with other MPC implementations. The results obtained demonstrate that the proposed technique outperforms some existing popular MPC schemes and can realise offset-free control even under significant plant–model mismatch and unmeasured disturbances. Copyright © 2012 John Wiley & Sons, Ltd.
    International Journal of Adaptive Control and Signal Processing 03/2014; 28(3-5). · 1.22 Impact Factor
  • Lianfang Cai, Xuemin Tian, Sheng Chen
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    ABSTRACT: Independent component analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy independent component analysis, referred to as NoisyICAn, is proposed. Using the noisy ICA model which considers the measurement noise explicitly, a NoisyICAn algorithm is developed to estimate the mixing matrix by setting up a series of the fourth-order cumulant matrices of the measured data and performing the joint diagonalization of these matrices. The kurtosis relationships of the independent components and measured variables are subsequently obtained based on the estimated mixing matrix, for recursively estimating the kurtosis of independent components. Two monitoring statistics are then built to detect process faults using the obtained recursive estimate of the independent components' kurtosis. The simulation studies are carried out on a simple three-variable system and a continuous stirred tank reactor system, and the results obtained demonstrate that the proposed NoisyICAn-based monitoring method outperforms the conventional noise-free ICA-based monitoring methods as well as the benchmark monitoring methods based on the existing noisy ICA schemes adopted from blind source separation, in terms of the fault detection time and local fault detection rate.
    Neurocomputing 01/2014; 127:231–246. · 2.01 Impact Factor
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    ABSTRACT: White light emitting diodes (LEDs) have been widely utilized for illumination owing to their desired properties of inherent bright output, high efficiency, low power consumption and long life-time. They are also increasingly applied in optical wireless communications for realizing high data rate transmission. This paper presents an improved scheme relying on the insertion of a simple predistortion module before the decoder at the receiver of optical wireless communication systems that use white LEDs. The proposed predistortion scheme exploits the inherent nature of mixing the three unequal optical-power primary colours in generating white light to enhance the system's performance. Specifically, we design this predistortion module by minimizing the upper bound of the error probability in conjunction with a soft-decision decoder. Our simulation results demonstrate that the detection performance is considerably improved with the aid of the proposed predistortion module.
    Optics Express 12/2013; 21(25):30295-30305. · 3.55 Impact Factor
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    ABSTRACT: A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how ''Occam's razor'' is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.
    Neurocomputing 12/2013; 122:210-220. · 2.01 Impact Factor
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    ABSTRACT: An efficient and high-performance semi-blind scheme is proposed for Multiple-Input Multiple-Output (MIMO) systems by iteratively combining channel estimation with K-Best Sphere Decoding (SD). To avoid the exponentially increasing com-plexity of Maximum Likelihood Detection (MLD) while achieving a near optimal MLD performance, K-best SD is considered to accomplish data detection. Semi-blind iterative estimation is adopted for identifying the MIMO channel matrix. Specifically, a training-based least squares channel estimate is initially provided to the K-best SD data detector, and the channel estimator and the data detector then iteratively exchange information to perform the decision-directed channel update and consequently to enhance the detection performance. The proposed scheme is capable of approaching the ideal detection performance obtained with the perfect MIMO channel state information.
    IEEE PACRIM 2013; 08/2013
  • M.I. Kadir, Li Li, Sheng Chen, Lajos Hanzo
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    ABSTRACT: Successive-relaying-aided (SR) cooperative multicarrier (MC) space-time shift keying (STSK) is proposed for frequency-selective channels. We invoke SR to mitigate the typical 50% throughput loss of conventional half-duplex relaying schemes and MC code-division multiple access (MC-CDMA) to circumvent the dispersive effects of wireless channels and to reduce the SR-induced interference. The distributed relay terminals form two virtual antenna arrays (VAAs), and the source node (SN) successively transmits frequency-domain (FD) spread signals to one of the VAAs, in addition to directly transmitting to the destination node (DN). The constituent relay nodes (RNs) of each VAA activate cyclic-redundancy-checking-based (CRC) selective decode-and-forward (DF) relaying. The DN can jointly detect the signals received via the SN-to-DN and VAA-to-DN links using a low-complexity single-stream-based joint maximum-likelihood (ML) detector. We also propose a differentially encoded cooperative MC-CDMA STSK scheme to facilitate communications over hostile dispersive channels without requiring channel estimation (CE). Dispensing with CE is important since the relays cannot be expected to altruistically estimate the SN-to-RN links for simply supporting the source. Furthermore, we propose soft-decision-aided serially concatenated recursive systematic convolutional (RSC) and unity-rate-coded (URC) cooperative MC STSK and investigate its performance in both coherent and noncoherent scenarios.
    IEEE Transactions on Vehicular Technology 07/2013; 62(6):2544-2557. · 2.06 Impact Factor
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    ABSTRACT: To provide communication services in delay-tolerant networks (DTNs) where there may exist no end-to-end paths between mobile node pairs, a variety of relaying and routing algorithms have been proposed under the assumption that the mobile nodes are homogeneously distributed in the network with the same contact rate and delivery cost. However, experimental data have revealed the heterogeneous contact rates between node pairs, and various applications of DTNs have shown that the mobile nodes often belong to different types in terms of energy consumption, communication ability, and other properties. Following the philosophy of exploiting the heterogeneous features of nodes to enhance the routing performance, we design an optimal relaying scheme for DTNs, which takes into account the nodes' heterogeneous contact rates and delivery costs when selecting relays to minimize the delivery cost while satisfying the required message delivery probability. We use the trace-driven simulations to demonstrate the effectiveness of our optimal relaying scheme in various distributions of nodes' delivery costs and mobility environments. Simulation results show that our proposed optimal relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect or do not fully take into account nodes' heterogeneity.
    IEEE Transactions on Vehicular Technology 06/2013; 62(5):2239-2252. · 2.06 Impact Factor
  • Sheng Chen, Xia Hong, Yu Gong, C.J. Harris
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    ABSTRACT: This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.
    Circuits and Systems I: Regular Papers, IEEE Transactions on 06/2013; 60(6):1584-1594. · 2.24 Impact Factor
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    ABSTRACT: Joint Timing and Channel Estimation (JTCE) for bandlimited long-code-aided Multi-Carrier Direct-Sequence Code Division Multiple Access (MC-DS-CDMA) systems is investigated. We establish the optimal multiuser timing and channel estimates for the uplink MC-DS-CDMA receiver by minimising a weighted least squares cost function with respect to K independent parameters, where K is the number of active users. A guided random search procedure known as Repeated Weighted Boosting Search (RWBS) is invoked for numerically solving this challenging multivariate optimisation problem, and thereby for producing near-optimal timing and channel estimates. The Cramer-Rao Lower Bound (CRLB) for the JTCE problem of interest is derived to benchmark the performance of the proposed RWBS based estimator. Quantitatively, for the scenario of K=10 users, E_b/N_0≥3 dB where E_b is the energy per bit and N_0 the single-sided noise power spectral density, and for a near-far ratio of 10 dB, the RWBS based estimator using an observation window of 20 symbols is shown to approach the CRLB at a complexity 10 orders of magnitude lower in comparison to its full maximum likelihood search based counterpart. The proposed algorithm does not require the transmission of known pilots, yet it is capable of handling time-variant channel states.
    IEEE Transactions on Communications 05/2013; 61(5):1998-2011. · 1.75 Impact Factor
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    ABSTRACT: We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators.
    Neurocomputing 02/2013; 115:122–129. · 2.01 Impact Factor
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    ABSTRACT: Contact duration between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs), that critically influences the design of routing schemes and network throughput. Due to prohibitive costs to collect enough realistic contact records, little experimental work has been conducted to study the contact duration in urban VANETs. In this work, we carry out an extensive experiment involving tens of thousands of operational taxis in Beijing city. Based on studying this newly collected Beijing trace and the existing Shanghai trace, we find an invariant characteristic that there exists a characteristic time point, up to which the contact duration obeys an exponential distribution that includes at least 80% of the whole distribution, while beyond which it decays as a power law one. This property is in sharp contrast to the recent empirical data studies based on human mobility, where the contact duration exhibits a power law distribution. Our observations thus provide fundamental guidelines for the design of new urban VANETs' routing protocols and their performance evaluation.
    IEEE Signal Processing Letters 01/2013; 20(1):110-113. · 1.67 Impact Factor
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    ABSTRACT: Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.
    Evolutionary Computation (CEC), 2013 IEEE Congress on; 01/2013
  • Xia Hong, Yi Guo, Sheng Chen, Junbin Gao
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    ABSTRACT: We propose a new sparse model construction method aimed at maximizing a model's generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.
    Digital Signal Processing (DSP), 2013 18th International Conference on; 01/2013

Publication Stats

2k Citations
261.09 Total Impact Points


  • 2001–2014
    • University of Southampton
      • • Department of Electronics and Computer Science (ECS)
      • • Faculty of Physical and Applied Sciences
      Southampton, England, United Kingdom
  • 2012
    • King Abdulaziz University
      Djidda, Makkah, Saudi Arabia
  • 2004–2012
    • University of Reading
      • School of Systems Engineering
      Reading, ENG, United Kingdom
    • China Agricultural University
      Peping, Beijing, China
  • 2011
    • Toyota Central R & D Labs., Inc.
      Nagoya, Aichi, Japan
    • Tsinghua University
      • Department of Electronic Engineering
      Beijing, Beijing Shi, China
  • 2009–2011
    • National University of Computer and Emerging Sciences
      • Department of Electrical engineering
      Lahore, Punjab, Pakistan
  • 2008–2011
    • Stevens Institute of Technology
      • Department of Electrical & Computer Engineering
      Hoboken, New Jersey, United States
  • 2000–2010
    • Zhejiang University
      • Institute of Cyber-Systems and Control
      Hang-hsien, Zhejiang Sheng, China
  • 2004–2007
    • University of Maryland, College Park
      • Department of Nutrition and Food Science
      College Park, MD, United States
    • U.S. Food and Drug Administration
      • Office of Research
      Washington, Washington, D.C., United States