On The Optimal Amount of Training for Peak-Power-Limited Rayleigh Fading Channels
We consider the optimal amount of training for single-antenna Rayleigh flat fading channels with peak-power- limited input. The receiver uses known training symbols to perform minimum mean square error (MMSE) channel estimation. With a block-fading channel model and a perfect interleaving assumption, we first analyze the characteristics of the optimum input distribution, then numerically compute the training-based capacity, and then find the optimal number of training symbols that maximizes the capacity.

title: Relaying Schemes for MIMO Broadcast Channels: Coexistence of Direct-Link and Relay-Link Users
abstract: The combination of relay and Multiple-Input Multiple-Output (MIMO) techniques can significantly benefit the signal transmission in cellular networks. In this work, we propose a novel relaying scheme for the relay assisted MIMO broadcast channel including user classification and precoding. The users are firstly classified as Direct Link (DL) users and Relay Link (RL) users according to their channel conditions. After classification, both DL and RL users may coexist in the system, and Zero-Forcing (ZF) precoding is employed at both the BS and the relay for interference cancellation. Analysis and simulation results show that the proposed relaying scheme can effectively combat the large scale fading of the channels and provide significant performance gain compared with the conventional MIMO relaying schemes without user classification.

title: The Impacts of Radio Channels and Node Mobility on Link Statistics in Mobile Ad Hoc Networks
abstract: Understanding link statistics in mobile ad hoc networks (MANETs) is essential to design adaptive routing protocols and achieve desired network performance. While much attention has been given to the node mobility impacts, little has been done to investigate the influence of dynamic channel fadings and the joint effects of the interactions among radio channels, transmission range, node mobility and node-pair distance on link statistics. In this paper, we investigate the link stability and availability by using a distance transition probability matrix of a relative distance between two nodes. Our analysis takes the node effective transmission range Re into account. The relative node movement is based on the Semi-Markov Smooth (SMS) mobility model  which captures the smooth node speed (V) transition and the radio channel variations in a small time- scale. We show that the PDF of link lifetime in MANETs can be effectively approximated by the exponential distribution characterized by the parameter V/Re. Moreover, we And that the impacting factors on residual link lifetime are in the decreasing order of node speed, transmission range, node-pair distance. The analytical results are validated by extensive simulations.

title: Linear Precoding for V-BLAST Systems in the Presence of Fading Correlations
abstract: This letter proposed a linear precoding scheme for the V-BLAST system that requires only knowledge of the statistical CSI; the transmitter does not need the instantaneous CSI. Power allocation on the eigenmodes of the transmit correlation matrix is one way to minimize bit error rate (BER). Simulation results show that the proposed precoding V-BLAST system provides a significant reduction in the BER compared with the conventional V-BLAST systems.

title: A Novel Evaluation Method for the Downlink Capacity of Distributed Antenna Systems
abstract: This letter focuses on the simplified capacity evaluation for the downlink of a distributed antenna system (DAS) with random antenna layout. Based on system scale-up, we derive a good approximation of the downlink capacity by developing the results from random matrix theory. We also propose an iterative method to calculate the unknown parameters in the approximated expression of the downlink capacity. The approximation is illustrated to be quite accurate and the iterative method is shown to be quite efficient by Monte Carlo simulations.

title: Iterative Turbo Channel Estimation for OFDM System over Rapid Dispersive Fading Channel
abstract: Coherent OFDM detection requires accurate channel state information (CSI). Mobile radio channels are both time and frequency dispersive, especially at high vehicular speeds, which makes channel estimation a challenging problem in system design. Conventional preamble-based and pilot-aided channel estimation require numerous reference signals, which significantly compromises the system throughput. This paper proposes a novel low complexity iterative turbo channel estimation technique which makes use of preamble, pilots and soft decoded data information in an iterative fashion to improve the system performance over the time and frequency selective fading channel while maintaining the system throughput. The numerical and analytical results show that the proposed technique can approach the performance of systems with perfect CSI with much fewer preamble and pilots symbols compared to existing channel estimation methods.

title: Subspace analysis and optimization for AAM based face alignment
abstract: Active appearance models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to find the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can find the optimal AAM subspace model rapidly and improve the performance of AAM significantly.

title: Application-Tailored Cache Consistency for Wide-Area File Systems
abstract: The inability to perform optimizations based on application-specific information presents a hurdle to the deployment of pervasive LAN file systems across WAN environments. This paper proposes a novel approach addressing this problem through application-tailored caching and consistency in widearea file systems. It leverages widely available Network File System (NFS) deployments without any modifications to kernels nor applications, and employs middleware to dynamically establish Grid-wide Virtual File System (GVFS) sessions with application-tailored cache consistency. Two consistency models are discussed in this paper: a relaxed model based on invalidation polling, and a stronger model based on delegation and callback. Experimental evaluation based on microbenchmarks and scientific applications show that with application-tailored cache consistency, GVFS is able to both improve application runtimes and reduce server load significantly, compared to kernel-level NFS in WAN.

title: A Novel Fast Recursive MMSE-SIC Detection Algorithm for V-BLAST Systems
abstract: A novel fast recursive minimum mean square error successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed. In this algorithm, the MMSE Alter matrices and the optimal detection order are successively computed from the previously obtained filter matrices according to simple recursive pseudoinverse formulas, so that the algorithmic complexity is reduced significantly, especially for the practical number of transmit/receive antennas.

title: Generalized Parallel Interference Cancellation With Near-Optimal Detection Performance
abstract: The maximum-likelihood detection (MLD) algorithm is the optimal detection scheme for vertical Bell Laboratories layered space-time (V-BLAST) systems. However, it is generally infeasible to implement the MLD algorithm in practical systems because of its high computational complexity. In this paper, we used two parallel interference cancellation techniques, including precancellation and postcancellation, to simplify the MLD algorithm into a class of computationally efficient suboptimal detection algorithms, herein called the generalized parallel interference cancellation (GPIC) algorithm. Numerical analysis shows that the GPIC algorithm can approximately achieve the MLD performance with considerably low and stable computational complexity. Furthermore, it can be significantly accelerated via parallel processing methods. With near-optimal performance, low and stable complexity, and high running speed, the GPIC algorithm is very suitable for real-time communication systems.

title: Automatic Person Annotation of Family Photo Album
abstract: Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of “who” in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective.