Fuzzy Q-Learning Admission Control for WCDMA/WLAN Heterogeneous Networks with Multimedia Traffic Abstract-In this paper, admission control by a fuzzy Q-learning technique is proposed for WCDMA/WLAN heterogeneous networks with multimedia traffic. The fuzzy Q-learning admission control (FQAC) system is composed of a neural-fuzzy inference system (NFIS) admissibility estimator, an NFIS dwelling estimator, and a decision maker. The NFIS admissibility estimator takes essential system measures into account to judge how each reachable subnetwork can support the admission request's required QoS and then output admissibility costs. The NFIS dwelling estimator considers the Doppler shift and the power strength of the requested user to assess his/her dwell time duration in each reachable subnetwork and then output dwelling costs. Also, in order to minimize the expected maximal cost of the user's admission request, a minimax theorem is applied in the decision maker to determine the most suitable subnetwork for the user request or to reject. Simulation results show that FQAC can always maintain the system QoS requirement up to a traffic intensity of 1.1 because it can appropriately admit or reject the users' admission requests. Also, the FQAC can achieve lower blocking probabilities than conventional JSAC proposed in [20] and can significantly reduce the handoff rate by 15-20 percent. T HE interworking functionality in heterogeneous environments has become an essential part for the next generation wireless communication systems. Services in the coming wireless heterogeneous communication systems may have more than one suitable network and spectrum opportunity to select according to the user preference and the air link conditions. The advantage of heterogeneous networks is the complementary flexibility of designs including the coverage, system resource management, and services support. For example, the wireless local area network (WLAN) [1] is suitable for indoor, LAN-based applications because of its high throughput and small coverage. The cellular system, such as the third generation (3G) WCDMA system, provides better support in highmobility, low-latency services but it has lower data rate. The 3rd Generation Partnership Project (3GPP) has released a quite complete specification for WLAN interworking [2] . IEEE Task Group u is also standardizing the details of interworking with external networks [3] .. Admission control is the first step of the system resource management in WCDMA/WLAN heterogeneous networks because it directly determines whether the call with multimedia traffic is allowed to enter the system. A lot of researches and approaches of admission control for WCDMA only [4] or WLAN only [5] have been proposed to increase the system capacity while maintaining the quality of service (QoS) guarantee. However, these approaches for a single system may not be suitable for heterogeneous systems because they did not consider the other system's situations and vertical handoff problems. A call admission request may have more than one option to search for a better spectrum opportunity and utilize one or more links in the heterogeneous networks. Nevertheless, the original QoS has to be maintained when call requests have to change their access networks.. The vertical handoff with network selection is a good start point for the admission control in the heterogeneous networks. In this case, a new call can be regarded as a virtual handoff call from a "null" cell. Zhu and McNair [6] used a weighted cost function for each candidate network and formulated an optimization problem with criteria such as requirements of bandwidth, delay, and transmission power. Zhang [7] proposed a vertical handoff decision method by using multiple attributes, which was further solved by the fuzzy logic technique. Chen and Shu [8] proposed an active application oriented mechanism, which made the handoff decision according to a utility function. Song and Jamalipour [9] proposed a gray relational analysis scheme to rank the most suitable destination network in the vertical handoff process. These approaches considered advanced traffic type of multimedia services and formulated the vertical handoff problem as a best decision selection, which outperforms traditional signal strengthbased methods [10] . In addition, the factor of mobility, which combined the use of position-assisted and mobility prediction, was also considered for call admission control (CAC) in the work of Ye et al. [11] . However, these papers did not consider realistic dynamics of radio channels and network capabilities, which are essential to determine if the selected target cell is capable of providing service continuity for vertical handoff calls.. Some recent research also considered more comprehensive network selection problems in heterogeneous environments. Maldonado et al. [12] raised the issue about the cognitive radio, which has a higher flexibility of using the spectrum opportunity and provides a more balanced system loading. Chan et al. [13] proposed a utility-based economic model to solve the resource allocation and the network selection problems in heterogeneous networks. Song et al. [14] investigated the WLAN-first call admission scheme for the cellular/WLAN interworking environment, which can achieve the maximum overall resource utilization. Suri and Narahari [15] proposed a novel auction algorithm for procuring wireless channel in heterogeneous networks. All of these works in [13] , [14] , [15] also focused on only the decision of a homogeneous system for new connections in the heterogeneous networks.. On the other hand, in order to take good advantage of both systems in WCDMA/WLAN interworking, integrated and efficient mechanisms of admission control for new and handoff calls were proposed to consider overall conditions in such heterogeneous environments. Lai and Tsai [16] studied three measurement-based CAC algorithms in heterogeneous environments. Service requirements are compared with measured system resource constrains to decide if the service request is admissible. Later, Huang and Ho [17] proposed a straightforward CAC for heterogeneous personal communications service (PCS). Two traffic types, real-time (RT) and non-real-time (NRT), were considered. The amounts of RT traffic and NRT traffic are compared with a predetermined channel occupancy threshold and the NRT buffer threshold, respectively, to determine the admission results. Song and Zhuang [18] proposed an admission control method for voice and data traffic in cellular and WLAN networks with their derived admission regions to select possible service coverages. The policies of admission control considered the different support ability of QoS in cellular and WLAN systems, and some admission strategies are given to maximize the overall system utilization. Niyato and Hossain [19] proposed a cooperative method to consider three systems, IEEE 802.11 WLAN, CDMA cellular wireless access, and IEEE 802.16 WMAN, to provide a high bandwidth service to the new connection. The estimated result of bandwidth distribution in each network was also referred to the system admission control. But the mobility was not considered and the solution, the Shaply value in the core region, was not necessarily optimal under the criteria of system throughput, load balancing, or utilization. Yu and Krishnamurthy [20] proposed joint session admission control (JSAC) to optimize the utilization of radio resources in integrated WLAN and CDMA networks. The QoS of WLAN (throughput and packet delay) and the QoS of CDMA (outage probability of signal-tointerference ratio (SIR)) were used to formulate the admission control problem as a semi-Markov decision process (SMDP). With a linear programming method, the admission decision is made according to the QoS state. However, the JSAC does not consider the service types, the rapid state changes caused by channel variation, and the user mobility in such a stochastic-based method.. These mentioned works considered the essential QoS requirements such as data rate, delay bound, and packet loss rate, to establish the cost functions for decision making of vertical handoffs. Unfortunately, parameters for the decision were based on either the instant and single channel sounding or the long-term stochastic results. The former could have the risk to make an improper admission decision due to some occasional channel variations, and the later would have the drawback of slower reactions in environment changes. Although the methods with longterm stochastic results can avoid some unnecessary handoffs if the channel states change rapidly, the slower response of the handoff or admission decisions could cause higher forced termination rate and more serious problem with signal deterioration. Therefore, in heterogeneous networks, effective call admission control should periodically monitor system status such as the number of users, interference in WCDMA, network busy periods in WLAN, etc. Also, it has to be aware of QoS requirements of multimedia traffic and mobility of users to make the most proper decision for a user's admission request.. In this paper, we propose fuzzy Q-learning admission control (FQAC) for WCDMA/WLAN heterogeneous networks. The FQAC considers not only QoS requirements but also multiple system measures such as interferences from home cells and other adjacent cells, the numbers of realtime and non-real-time users in the system, and user's mobility. In order to put all the system measures and QoS requirements together for an admission request, the FQAC is designed to contain an admissibility estimator, a dwelling estimator, and a decision maker. The FQAC adopts a fuzzy Q-learning (FQL) method [21] , [22] for estimation and a minimax theorem [23] for decision making, where the FQL integrates the neural-fuzzy inference system (NFIS) [24] , [25] with the online Q-learning algorithm. The Q-learning has been proven to converge to a global optimum in the Markov decision process with simplicity of iteratively computational demands [21] . With the system measures and the user's profile, the NFIS will take an action, which is the estimated cost of the admission request, for each reachable network. Then, the decision maker collects the costs and makes a final admission/rejection decision by using the minimax theorem. The possible actions taken by the NFIS are tuned by FQL, which can simplify the reinforcement learning procedure and increase the feasibility and scalability in real implementation. Without the knowledge of system state-transition probability, the FQL can iteratively and adaptively adjust the relations between system states and actions by reinforcement learning signals feedbacked from the system. The complexity of FQAC is low, which is feasible and will be a great advantage in reducing the vertical handoff latency.. Simulation results show that FQAC performs more aggressive admission control than JSAC in [20] when the traffic intensity is low, and FQAC turns to be more prudential when the traffic intensity grows high. Hence, FQAC has lower new and handoff user blocking rates and higher system utilization while maintaining the QoS requirements. It is because FQAC has features to intelligently estimate user's admissibility and dwelling time. FQAC also achieves lower handoff rate than JSAC for the reason that the admitted (cell) subnetwork would be the one in which the user may dwell longer. Moreover, once the initial training is completed, the online convergence rate of FQAC is fast, which is about 11 episodes (about 850 milliseconds) in average and is feasible for real implementation and applications.. The organization of this paper is as follows: In Section 2, the system model for a WCDMA/WLAN heterogeneous network is described, and the essential system measures are selected. Section 3 presents the proposed approach of FQAC. Section 4 illustrates simulation results. Finally, Section 5 draws the conclusions. Fig. 1 shows the WCDMA/WLAN heterogeneous network with FQAC, which is installed in the radio network controller (RNC). Each WCDMA or WLAN system is a subnetwork of the heterogeneous network. Generally speaking, the subnetwork is deployed with its strategy of topology, and these subnetworks could be overlapped with each other. In this paper, we propose FQAC for multimedia traffic in WCDMA/WLAN heterogeneous networks. The FQAC system adopts a NFIS with Q-learning (FQL) method for admissibility estimator and dwelling estimator. Generally, the fuzzy logic technology can provide a robust mathematical method for admission control in realistic environments, especially when the mathematical model of the process is too complicated to find. The combination of fuzzy logic and neural network with Q-learning further provides the ability to adapt to system dynamics because it can automatically adjust the relations between system states and actions which exist in the fuzzy rules. Also, the FQAC system considers essential system measures as input linguistic variables, such as the number of mobile users, the interference in WCDMA systems, and the busy period in WLAN systems. Meanwhile, the QoS requirements and the mobility of the user's admission request are taken into account. According to these linguistic variables, FQAC would generate the admissibility cost and dwelling cost of each reachable subnetwork to reflect the impact of these subnetworks by the user's admission request. In order to minimize the expected maximal impact (cost) of the mobile user's admission request, the decision maker in the FQAC system adopts the minimax theorem to jointly estimate the mixed cost and decides the most suitable subnetwork or reject the mobile user request.. Simulation results show that FQAC performs more aggressive admission control than JSAC when the traffic intensity is low. However, whenever the traffic intensity grows high, FQAC turns to be more prudential to avoid QoS violations. Hence, FQAC has lower new and handoff user blocking rates, while maintaining the QoS. Also, the FQAC can significantly reduce the average number of handoff by 15-20 percent as compared to JSAC. The FQAC system would be capable to adapt to the fluctuation of traffic dynamics and should be one of the best choices for heterogeneous networks. His research areas include wireless medium access controls, radio resource management, scheduler control algorithms for wireless high-speed data systems, end-to-end performance, and provisioning strategies. He has published more than 50 technical memoranda, journal papers, and conference papers, and is the author of a chapter in Handbook of CDMA System Design, Engineering and Optimization. Currently, he also has 12 patents and 20 pending patents. He is a member of the IEEE.. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib.

B I B I O O O O O O O O O O B I O O B I O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Robust Recovery of Subspace Structures by Low-Rank Representation Abstract-In this work we address the subspace clustering problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into their respective subspaces and remove possible outliers as well. To this end, we propose a novel objective function named Low-Rank Representation (LRR), which seeks the lowestrank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: when the data is clean, we prove that LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for data corrupted by arbitrary sparse errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace clustering and error correction, in an efficient and effective way. In pattern analysis and signal processing, an underlying tenet is that the data often contains some type of structure that enables intelligent representation and processing. So one usually needs a parametric model to characterize a given set of data. To this end, the well-known (linear) subspaces are possibly the most common choice, mainly because they are easy to compute and often effective in real applications. Several types of visual data, such as motion [1] , [2] , [3] , face [4] and texture [5] , have been known to be well characterized by subspaces. Moreover, by applying the concept of reproducing kernel Hilbert space, one can easily extend the linear models to handle nonlinear data. So the subspace methods have been gaining much attention in recent years. For example, the widely used Principal Component Analysis (PCA) method and the recently established matrix completion [6] and recovery [7] methods are essentially based on the hypothesis that the data is approximately drawn from a low-rank subspace. However, a given data set can seldom be well described by a single subspace. A more reasonable model is to consider data as lying near several subspaces, namely the data is considered as samples approximately drawn from a mixture of several low-rank subspaces, as shown in Fig.1 . The generality and importance of subspaces naturally lead to a challenging problem of subspace segmentation (or clustering), whose goal is to segment (cluster or group) data into clusters with each cluster corresponding to a subspace. Subspace segmentation is an important data clustering problem and arises in numerous research areas, including computer vision [3] , [8] , [9] , image processing [5] , [10] and system identification [11] . When the data is clean, i.e., the samples are strictly drawn from the subspaces, several existing methods (e.g., [12] , [13] , [14] ) are able to exactly solve the subspace segmentation problem. So, as pointed out by [3] , [14] , the main challenge of subspace segmentation is to handle the errors (e.g., noise and corruptions) that possibly exist in data, i.e., to handle the data that may not strictly follow subspace structures. With this viewpoint, in this paper we therefore study the following subspace clustering [15] problem. Problem 1.1 (Subspace Clustering): Given a set of data samples approximately (i.e., the data may contain errors) drawn from a union of linear subspaces, correct the possible errors and segment all samples into their respective subspaces simultaneously. In this section, we discuss some existing subspace segmentation methods. In general, existing works can be roughly divided into four main categories: mixture of Gaussian, factorization, algebraic and spectral-type methods.. In statistical learning, mixed data is typically modeled as a set of independent samples drawn from a mixture of probabilistic distributions. As a single subspace can be well modeled by a (degenerate) Gaussian distribution, it is straightforward to assume that each probabilistic distribution is Gaussian, i.e., adopting a mixture of Gaussian models. Then the problem of segmenting the data is converted to a model estimation problem. The estimation can be performed either by using the Expectation Maximization (EM) algorithm to find a maximum likelihood estimate, as done in [21] , or by iteratively finding a min-max estimate, as adopted by K-subspaces [8] and Random Sample Consensus (RANSAC) [10] . These methods are sensitive to errors. So several efforts have been made for improving their robustness, e.g., the Median K-flats [22] for K-subspaces, the work [23] for RANSAC, and [5] use a coding length to characterize a mixture of Gaussian. These refinements may introduce some robustness. Nevertheless, the problem is still not well solved due to the optimization difficulty, which is a bottleneck for these methods.. Factorization based methods [12] seek to approximate the given data matrix as a product of two matrices, such that the support pattern for one of the factors reveals the segmentation of the samples. In order to achieve robustness to noise, these methods modify the formulations by adding extra regularization terms. Nevertheless, such modifications usually lead to non-convex optimization problems, which need heuristic algorithms (often based on alternating minimization or EMstyle algorithms) to solve. Getting stuck at local minima may undermine their performances, especially when the data is grossly corrupted. It will be shown that LRR can be regarded as a robust generalization of the method in [12] (which is referred to as PCA in this paper). The formulation of LRR is convex and can be solved in polynomial time.. Generalized Principal Component Analysis (GPCA) [24] presents an algebraic way to model the data drawn from a union of multiple subspaces. This method describes a subspace containing a data point by using the gradient of a polynomial at that point. Then subspace segmentation is made equivalent to fitting the data with polynomials. GPCA can guarantee the success of the segmentation under certain conditions, and it does not impose any restriction on the subspaces. However, this method is sensitive to noise due to the difficulty of estimating the polynomials from real data, which also causes the high computation cost of GPCA. Recently, Robust Algebraic Segmentation (RAS) [25] has been proposed to resolve the robustness issue of GPCA. However, the computation difficulty for fitting polynomials is unfathomably large. So RAS can make sense only when the data dimension is low and the number of subspaces is small.. As a data clustering problem, subspace segmentation can be done by firstly learning an affinity matrix from the given data, and then obtaining the final segmentation results by spectral clustering algorithms such as Normalized Cuts (NCut) [26] . Many existing methods such as Sparse Subspace Clustering (SSC) [13] , Spectral Curvature Clustering (SCC) [27] , [28] , Spectral Local Best-fit Flats (SLBF) [29] , [30] , the proposed LRR method and [2] , [31] , possess such spectral nature, so called as spectral-type methods. The main difference among various spectral-type methods is the approach for learning the affinity matrix. Under the assumption that the data is clean and the subspaces are independent, [13] shows that solution produced by sparse representation (SR) [32] could achieve the so-called ℓ 1 Subspace Detection Property (ℓ 1 -SDP): the within-class affinities are sparse and the betweenclass affinities are all zeros. In the presence of outliers, it is shown in [15] that the SR method can still obey ℓ 1 -SDP. However, ℓ 1 -SDP may not be sufficient to ensure the success of subspace segmentation [33] . Recently, Lerman and Zhang [34] prove that under certain conditions the multiple subspace structures can be exactly recovered via ℓ p (p ≤ 1) minimization. Unfortunately, since the formulation is not convex, it is still unknown how to efficiently obtain the globally optimal solution. In contrast, the formulation of LRR is convex and the corresponding optimization problem can be solved in polynomial time. What is more, even if the data is contaminated by outliers, the proposed LRR method is proven to exactly recover the right row space, which provably determines the subspace segmentation results (we shall discuss this in Section III-B). In the presence of arbitrary errors (e.g., corruptions, outliers and noise), LRR is also guaranteed to produce near recovery.

O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O


Decoding

techniques

for

alternate-relaying

BICM

cooperative

systems

In

this

paper,

we

propose

the

use

of

bit-interleaved

coded

modulation

in

alternate-relaying

decode-and-forward

cooperative

communication

systems.

At

the

destination,

we

exploit

the

interference

signal,

which

results

from

the

simultaneous

transmission

of

data

streams

through

both

direct

and

one

of

the

relay

channels

to

develop

an

optimal

detector.

It

is

shown

that

the

proposed

detector

can

be

implemented

by

parallel

concatenating

maximum

a

posteriori

(MAP)

algorithms

and

demappers

to

the

decoders.

The

detector

exchanges

soft

information

between

the

decoders

and

the

MAP

algorithms

in

an

iterative

way

for

performance

improvement.

The

proposed

optimal

detector

requires

a

long

delay

as

the

destination

has

to

receive

and

store

the

entire

frame

before

performing

data

detection.

To

avoid

this,

a

sub-optimal

detector

is

also

proposed.

Unlike

the

optimal

detector,

the

sub-optimal

one

exploits

two

consecutive

received

packets

to

decode

one

packet.

It

turns

out

that

the

sub-optimal

detector

has

less

reduced

delay,

complexity,

memory

size,

and

bandwidth

loss

with

a

slight

increase

of

the

bit-error-rate.

Extensive

simulation

results

are

presented

to

demonstrate

the

effectiveness

of

the

proposed

detectors.

Cooperative

technology

constitutes

a

breakthrough

in

the

design

of

wireless

communication

systems.

This

is

due

to

its

relatively

simple

implementation

and

its

significant

performance

gains

in

terms

of

link

reliability,

system

capacity,

and

transmission

range

In

cooperative

communications,

multiple

terminals

in

a

wireless

network

cooperate

to

form

a

virtual

antenna

array

in

a

distributed

fashion.

In

this

manner,

spatial

diversity

gain

can

be

achieved

even

when

a

local

antenna

array

is

not

available.

It

is

not

surprising

that

cooperative

communications

have

become

a

strong

candidate

for

many

wireless

applications,

such

as

cellular

networks,

wireless

local

area

network,

mobile

ad

hoc

networks,

and

wireless

sensor

networks

Generally,

there

are

two

kinds

of

relaying

modes:

full

duplex

and

half

duplex.

In

a

full-duplex

mode,

a

relay

transmits

and

receives

simultaneously

in

the

same

band;

however,

the

transmitted

signal

interferes

at

the

relay

with

the

received

signal.

In

theory,

it

is

possible

for

the

relay

*Correspondence:

h.mostafa@mun.ca

Faculty

of

Engineering

and

Applied

Science,

Memorial

University,

St.

John's,

NL,

Canada

Full

list

of

author

information

is

available

at

the

end

of

the

article

to

cancel

out

the

interference

because

it

knows

the

transmitted

signal.

However,

in

practice,

a

small

error

in

the

interference

cancellation

can

be

fatal

because

the

transmitted

signal

is

typically

to

dB

stronger

than

the

received

signal,

as

indicated

in

This

error

results

from

inaccurate

knowledge

of

the

device

characteristics

or

from

the

effects

of

quantization

and

finite

precision

processing.

Therefore,

the

full-duplex

mode

is

not

commonly

used.

In

a

half-duplex

mode,

a

relay

cannot

simultaneously

transmit

and

receive.

In

other

words,

the

source

and

relay

transmissions

must

be

orthogonal

in

order

to

eliminate

any

potential

interference.

Orthogonality

can

be

in

time

domain,

in

frequency

domain,

or

using

any

set

of

signals

that

are

orthogonal

over

the

time-frequency

plane.

A

major

problem

of

the

half-duplex

relaying

mode

is

the

reduction

in

the

spectral

efficiency

To

combat

this

problem,

different

cooperative

techniques

are

introduced,

such

as

non-orthogonal,

two

way,

and

alternate

relaying.

In

the

non-orthogonal

cooperative

systems

(e.g.,

the

source

is

active

all

the

time.

In

the

first

half

of

the

transmission

interval,

the

source

sends

data

to

a

relay

and

destination.

However,

since

the

relay

is

assumed

to

be

half

duplex,

the

relay

does

not

receive

what

the

source

transmits

in

the

second

half

of

the

transmission

interval.

This

results

in

a

reduction

in

the

diversity

order

of

the

system.

Furthermore,

an

additional

processing

is

required

at

the

destination

in

order

to

separate

the

signals

received

simultaneously

in

the

second

half

of

the

transmission

interval.

In

the

two-way

cooperative

systems,

two

sources

exchange

data

via

the

aid

of

a

shared

relay

(e.g.,

The

two

sources

send

simultaneously

during

the

first

time

slot,

while

in

the

second

time

slot,

the

rely

broadcasts

the

mixture

of

these

two

signals.

Since

parts

of

the

transmit

signal

sent

by

the

relay

are

known

at

the

destinations,

each

receiver

can

extract

the

data

of

the

other

partner.

It

is

obvious

that

the

full-rate

transmission

can

be

attained

since

two

time

slots

are

required

to

transmit

the

data

of

the

two

partners;

however,

this

cooperative

transmission

requires

two

partners

sending

messages

to

each

other

simultaneously..

In

alternate-relaying

transmission

protocols

(e.g.,

the

source

communicates

with

the

destination

via

two

relays.

The

basic

idea

behind

these

protocols

is

to

use

two

successively

forwarding

relays

to

mimic

a

full-duplex

relay.

More

specifically,

at

any

time

slot,

the

source

sends

its

information

to

the

destination

and

one

of

the

relays,

while

the

other

relay

forwards

the

information

received

from

the

source

in

the

previous

time

slot

to

the

destination.

In

this

way,

the

source

can

continuously

transmit

data

without

being

halted,

and

hence,

the

spectral

efficiency

loss

is

recovered..

The

vast

majority

of

research

in

alternate-relaying

transmission

protocols

focuses

on

information-theoretic

analysis

to

assess

achievable

rates,

capacity

bounds,

and

diversity-multiplexing

tradeoff

(e.g.,

However,

the

major

issue

associated

with

these

protocols

is

how

to

handle

the

interference,

which

is

caused

by

the

simultaneous

transmission

of

the

source

and

one

of

the

relays,

in

a

simple

way.

Basically,

the

way

used

to

treat

the

interference

at

the

relays

and

destination

depends

on

whether

the

relays

employ

amplify-and-forward

(AF)

or

decode-andforward

(DF)

relaying

strategies..

For

the

AF

alternate-relaying

protocols,

the

authors

in

propose

successive

decoding

at

the

destination

with

partial

cancellation

of

inter-relay

interference.

The

authors

in

introduce

inter-relay

self-interference

cancellation,

where

the

cancellation

is

performed

at

one

of

the

relays;

however,

the

detection

process

at

the

destination

requires

high

computational

complexity.

The

authors

in

propose

a

full

inter-relay

interference

algorithm,

where

the

cancellation

is

performed

at

the

destination

in

a

simple

manner;

however,

the

noise

accumulation

associated

with

the

algorithm

limits

the

overall

system

performance..

Generally,

the

deployment

of

AF

relaying

strategy

in

alternate-relaying

cooperative

systems

is

challenging,

as

the

interference

and

noise

accumulation

which

results

from

the

inter-relay

link

degrade

the

overall

performance.

Accordingly,

the

detection

process

at

the

destination

has

to

be

associated

with

sophisticated

interference

cancellation

detectors.

This

increases

the

computational

complexity

significantly,

as

shown

in

Due

to

its

symbol-by-symbol

decision

base,

DF

is

an

attractive

relaying

strategy

to

avoid

interference

accumulation

at

the

destination

for

alternate-relaying

cooperative

systems..

For

DF

alternate-relaying

protocols,

it

is

usually

assumed

that

the

inter-relay

link

is

either

sufficiently

weak

and

then

its

contribution

can

be

treated

as

extra

noise,

or

sufficiently

strong

that

it

can

be

canceled

through

successive

interference

cancellation

at

the

relay

However,

these

two

extreme

scenarios

may

not

always

occur

in

practical

systems.

In

dirty

paper

coding

based

on

interference

pre-subtraction

at

the

source

is

proposed

to

cancel

the

inter-relay

interference.

However,

this

requires

high

computational

complexity

and

full

knowledge

of

the

channel

state

information

of

all

the

links

at

the

source,

which

is

not

easy

to

achieve

in

practice.

Beam-forming/smart

antennas

and

code

division

multiple-access

techniques

are

also

proposed

to

eliminate

the

interference

at

the

relays

and

destination.

The

technique

comes

at

the

cost

of

complexity,

where

the

latter

comes

at

the

cost

of

wasting

resources.

In

the

authors

propose

employing

multiple

antenna

at

the

relays

to

cancel

out

the

inter-relay

interference.

However,

implementing

multiple

antennas

at

the

relays

is

not

applicable

for

some

wireless

applications

due

to

size,

power,

and

cost

constraints.

A

rotated

signal

constellation

has

been

proposed

to

achieve

full

interference

cancellation

The

idea

is

that

there

are

no

two

symbols

in

the

rotated

constellation

having

the

same

real

or

imaginary

value.

Accordingly,

an

orthogonal

transmission

can

be

achieved

between

source

relays

and

inter-relay

links

by

assigning

real

parts

of

the

rotated

symbols

to

one

link

and

imaginary

parts

to

the

other

link.

However,

the

direct

link

between

the

source

and

destination

is

not

available,

and

there

is

significant

degradation

in

the

BER

performance

when

compared

with

that

of

the

original

constellation..

All

the

previous

works

deal

with

uncoded

transmission,

which

is

not

usually

used

in

practice.

To

the

best

of

our

knowledge,

this

is

the

first

work

in

the

literature

that

applies

error-correcting

coding

for

alternate-relaying

cooperative

systems.

In

this

paper,

we

exploit

the

interference

signal

at

the

destination

to

develop

an

optimal

detector

for

alternate-relaying

DF

cooperative

systems

in

conjunction

with

a

bit-interleaved

coded

modulation

(BICM)

signal.

Starting

from

the

maximum

a

posteriori

(MAP)

principle,

it

is

shown

that

the

proposed

detector

can

be

implemented

by

combining

the

MAP

algorithm

with

the

decoder

of

BICM.

Furthermore,

to

reduce

the

delay

associated

with

the

optimal

detector,

a

sub-optimal

one

is

also

introduced.

The

proposed

detectors

can

work

with

any

receiver

as

long

as

this

can

compute

the

a

posteriori

probabilities

of

the

data

symbols..

The

remainder

of

the

paper

is

organized

as

follows.

In

Section

the

system

model

and

problem

formulation

are

presented.

In

Sections

and

the

optimal

and

sub-optimal

detectors

are

proposed

at

the

destination,

respectively.

Section

explains

the

used

detector

at

the

relays.

The

performance

of

the

proposed

detectors

is

evaluated

through

computer

simulations

in

Section

Finally,

the

paper

is

concluded

in

Section

System

model

and

problem

formulation

The

receiver

design

was

studied

at

destination

for

alternate-relaying

decode-and-forward

cooperative

communication

systems

with

BICM

signals.

The

interference

signal,

which

results

from

the

simultaneous

transmission

of

data

streams

through

both

direct

and

one

of

the

relay

channels,

was

exploited

as

a

beneficial

resource

to

develop

an

optimal

detector.

We

showed

that

the

optimal

detector

was

implemented

by

parallel

concatenating

MAP

algorithms

and

demappers

to

the

decoders.

In

order

to

avoid

the

delay

problem

associated

with

the

optimal

detector,

a

sub-optimal

detector

was

also

developed.

The

sub-optimal

detector

achieved

a

close

performance

when

compared

with

the

optimal

one.

Both

of

the

optimal

and

sub-optimal

detectors

exchanged

soft

information

between

decoders

and

MAP

algorithms

in

an

iterative

fashion

for

performance

improvement.

In

this

section,

we

validate

the

proposed

detectors

through

Monte

Carlo

computer

simulations.

We

consider

an

alternate-relaying

DF

cooperative

communication

system,

using

a

convolutional

code

with

constraint

length

rate

and

polynomial

generators

and

The

BCJR

algorithm

is

used

for

decoding.

A

framebased

transmission

is

assumed;

each

has

packets.

A

packet

length

of

N

b

information

bits

is

chosen,

leading

to

N

c

coded

bits.

The

coded

bits

are

set,

partition

mapped

on

an

constellation,

resulting

in

N

d

symbols.

For

each

link,

the

channel

is

assumed

to

be

frequency

non-selective

and

modeled

as

a

zero-mean

independent

complex

Gaussian

random

variable.

To

capture

the

effect

of

the

path

loss

on

the

performance,

we

consider

E

|h

AB.

where

h

AB

d

SD

and

d

AB

are

the

channel

coefficient,

the

distance

between

source

and

destination,

and

the

distance

between

the

nodes

A

and

B,

respectively;

is

the

path

loss

exponent,

and

E[.]

is

the

statistical

average

operator.

Unless

mentioned

otherwise,

the

distance

between

the

source

and

the

two

relays

equals

while

the

distance

between

the

two

relays

is

All

these

distances

are

normalized

to

the

source-to-destination

distance,

and

is

set

to

be

Figures

and

depict,

respectively,

the

bit

error

rate

(BER)

performances

of

the

proposed

optimal

and

suboptimal

detectors

at

the

destination

as

a

function

of

E

b

/N

where

E

b

and

N

are

the

energy

per

bit

contributed

by

the

source

and

noise

power

spectral

density,

respectively.

One

can

express

E

b

at

the

destination

as

Here

we

assume,

without

loss

of

generality,

that.

σ

SD

and

the

transmit

pulse

shaping

satisfies

the

Nyquist

criterion.

Since

two

relays

are

used

in

the

alternate-relaying

cooperative

systems,

the

BER

performance

of

the

half-rate

one

relay

and

best

relay

from

a

set

of

two

relays

is

also

shown.

For

a

fair

comparison

between

the

half-rate

and

full-rate

(alternate-relaying)

systems,

we

keep

the

same

data

rate

and

transmitted

power

for

both

systems.

Hence,

we

use

modulation

for

the

half-rate

systems

and

for

the

full-rate

systems.

As

one

can

observe,

for

the

fullrate

cooperative

systems,

iterative

processing

achieves

a

significant

performance

improvement

for

the

proposed

optimal

and

sub-optimal

detectors.

Furthermore,

it

is

seen

that

the

performance

of

the

proposed

full-rate

cooperative

systems

outperforms

that

of

the

half-rate

cooperative

systems.

Figure

compares

the

BER

performance

of

the

proposed

optimal

detector

with

that

of

the

proposed

sub-optimal

detector.

As

one

can

observe,

at

the

first

iteration,

the

performance

of

the

sub-optimal

detector

is

much

worse

than

that

of

the

optimal

one.

However,

by

increasing

the

number

of

iterations,

the

difference

between

the

performance

of

the

sub-optimal

and

optimal

detectors

reduces.

This

occurs

because

at

the

first

iteration,

the

a

posteriori

probabilities

provided

by

the

sub-optimal

detector

are

not

reliable

to

initialize

the

SISO

decoder.

With

the

aid

of

iterative

processing,

the

reliability

of

the

sub-optimal

detector

increases,

and

its

performance

approaches

the

performance

of

the

optimal

detector.

Figure

shows

the

BER

performance

of

the

proposed

optimal

and

sub-optimal

detectors

with

different

relay

positions,

while

the

distance

between

the

two

relays

is

kept

constant.

As

one

can

observe,

the

BER

at

the

destination

degrades

with

d

SR

and

d

SR

It

is

because

the

BER

at

the

relays

increases

with

d

SR

and

d

SR

this,

in

turn,

increases

the

BER

at

the

destination.

Figure

shows

the

BER

performance

of

the

used

detector

at

the

relays,

as

shown

in

Section

as

a

function

of

E

b

/N

Here,

E

b

is

the

energy

per

bit

contributed

by

the

source

at

the

listening

relay

R

x

x

given

as.

For

the

sake

of

comparison,

we

show

the

performance

when

the

interference

signal

can

be

treated

as

additional

noise,

and

this

is

referred

to

as

detector

In

addition,

we

show

the

BER

performance

of

the

successive

interference

cancellation

detector

proposed

in

which

is

based

on

detecting

the

strongest

signal

first,

then

subtracting

its

contributions

from

the

received

interfered

signal

before

detecting

the

other

signal.

In

the

sequel,

this

is

referred

to

as

detector

As

one

can

observe,

both

detector

and

detector

lead

to

an

unacceptable

performance.

We

also

notice

that

the

used

detector

achieves

a

strong

improvement

of

the

performance

after

only

two

iterations.

In

addition,

there

is

no

significant

improvement

in

the

performance

after

three

iterations

for

any

E

b

/N

values.

O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hybrid video quality prediction: reviewing video quality measurement for widening application scope Abstract A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration. Video quality assessment has been an important topic during the last decades, notably in the television broadcasting domain. The continuous spread of coverage and the transmission bitrate increase in Internet connectivity at home led not only to the expected increase of IPTV services and consumption but also to an unexpected number of added-value services, both from commercial providers, such as downloadable video content and video-on-demand offers as well as customer-to-customer offers such as video sharing platforms.. Customer satisfaction, notably in terms of transmitted video quality, is the key to the success of these services. Topics which had limited application previously are gaining more interest now. One example in which automated measurement of perceived video quality plays an important role is the inter-channel bitrate allocation. It is well known that fast moving content such as sports requires higher bitrates than slow content, such as a single person presentation of the news in order to provide a comparable perceived quality. On satellite channels and in Digital Video Broadcasting (DVB), often only a limited number of content channels could be bundled, two to eight being a typical limitation. The combined bit-rate was fixed. Splitting the bit-rate unequally between the various programs was therefore limited to quantized values that did not vary significantly over time. This is different when the Internet is used as transmission channel. Each household with its DSL subscriber line may demand several video streams from different server locations. Head-end stations accumulate several subscriber lines and the combined traffic volume may be limited. In a spatially local area, several hundred video bit-streams may be streamed at the same time, accounting to and benefitting from a high temporal flexibility in individual bitrate allocation. In the ideal case, all subscribers will receive the desired Quality of Experience (QoE) for their service which is continuously monitored by automated measurements taking into consideration the transmitted bitstream features and the decoded video's quality instead of randomly dropping packets.. The objective measurement of QoE in the network or at the receiving side is the key aspect of this scenario. QoE is often used with ambiguous meaning. In this paper, we understand QoE in the way that has recently been defined as "the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user's personality and current state" [35] . This paper focuses on the prediction of one aspect of QoE: perceived video quality. It has important interactions with other aspects of QoE such as audio, environmental setup, or interactivity which are outside of the scope of this paper.. Objective video quality models can be divided into different categories depending on whether they use reference information or not, as well as if they are using bit-stream and/or video data. Historically, as most video quality models worked only on the video data, the terms Full-Reference (FR), Reduced-Reference (RR) and No-Reference (NR) were used (and are still used) for describing the amount of information from the reference video. In this case, the reference video corresponds to a high quality uncompressed version of the video. FR models will then require access to the complete reference or original video. An RR model, on the other hand, will extract key information from both the reference and the degraded video, and compare these. As such, only a reduced amount of data needs to be transferred either to the sending point or the receiving point in order to compare the videos and predict perceived quality. An NR model is working only on the degraded video and does not require a reference. They are also sometimes called zero reference.. As per the ITU standardization activities, the objective quality measurement methods have been classified into the following five main categories depending on the type of input data that is being used for quality assessment: media-layer models, parametric packet-layer models, parametric planning models, bitstream-layer models and hybrid models [13, 55] . Parametric packet-layer models inspect only the packet header such as IP-packets e.g. Real-time Transmission Protocol (RTP) or User Datagram Protocol (UDP), and from this information make predictions of the quality [25] . Such models are particularly interesting when video traffic is encrypted, or in the case of Digital Rights Management (DRM). Bit-stream models go further and also analyze the encoded (impaired) bit-stream itself and extract the information needed to make a quality prediction without fully decoding and processing the actual video [26] . Hybrid models use both video pixel information in combination with the bit-stream information eventually also doing a full decoding of the video payload.. Media-layer models use the speech or video signal to compute the Quality of Experience (QoE). These models do not require any information about the system under testing. Parametric planning models make use of quality planning parameters for networks and terminals to predict the QoE. As a result they require a priori knowledge about the system that is being tested. In [13] the media-layer models are classified into traditional point-based metrics (e.g., MSE and PSNR), Natural Visual Characteristics oriented metrics, and Perceptual (HVS) oriented metrics. The Natural Visual Characteristics metrics are further classified into Natural Visual Statistics and Natural Visual Features based methods. Similarly, the HVS methods are further classified into DCT domain, DWT domain and pixel domain models. The objective methods can also be classified in terms of their usability in the context of adaptive streaming solutions as out-of-service methods and in-service methods. In the out-of-service methods, no time constraints are imposed and the original sequence can be available.. We can generalize the concept of FR, RR and NR by also including packet header models, bit-stream models and hybrid models together with the pure video based models, providing a classification based on the amount of reference information used by the models, such as identifying a hybrid Bitstream-NR model.. The usefulness and applicability of these models are quite different. It has been considered that the most accurate models are the FR models; the VQEG phase II test did show that RR models can be at least as accurate [61] . The FR models and RR models can therefore be used for offline tuning of encoders and comparison between them. They can also be useful for generating training data for the development of NR models [11] . RR models are often argued to be an alternative for quality monitoring purposes in the network, but practically they are not. The reason is that the reference information that is processed at the sender side to generate the auxiliary RR information requires in most cases to be based on high quality, preferably uncompressed, versions of the videos. In practice, the network provider is not in possession of the same video as the content provider sends a coded video signal for distribution. This version of the video is often of quite low quality in itself and most RR models are not designed to compare towards a low quality reference.. Research on Hybrid-NR perceptual measurements faces two main categories of problems. The first category relates to technical details, such as capturing a transmitted video bit-stream, parsing the information from the current complex video compression standards, accessing the decoded video's pixel data, etc. The second category relates to the complexity of algorithmically modeling the Human Visual System (HVS). This comprises some prominent questions and fundamental research such as contrast sensitivity, spatial and motion masking, visual saliency, etc.. The first category can be tackled by centralizing the effort and managing technical tools for the research community in a collaborative effort. This has been recently established by the Joint Effort Group (JEG) of the Video Quality Experts Group, notably in their Hybrid project [52, 60] . Within JEG-Hybrid, a number of tools are made freely available for, amongst other, automatic monitoring and gathering of information at the video and network level. All information is gathered in structured XML files, which enable easy processing of the captured data. Furthermore, instead of having to parse the received encoded video bit-streams, JEGHybrid proposed the use of an XML-based file structure for representing the content of the (impaired) video bit-stream. These Hybrid Input XML files (HMIX files) contain information in a human readable format and enable quick and efficient processing of the video instead of having to write a complete parser. All software tools can be downloaded from VQEG's Tools and Subjective Labs Setup website [54] .. The second category, the development of algorithmic prediction of the behavior of the HVS, is subject of this paper. In most publications, either an isolated aspect of the HVS is analyzed in detail, or a (complex) measurement algorithm is presented and verified on a video database annotated with subjective voting. The first approach lacks the verification on general content; the second approach misses to prove that the HVS has been modeled successfully.. A recent review of objective video quality measurement algorithms has been published in [13] . The authors investigated into a classification of the general methodology for predicting video quality in categories such as natural visual statistics or frequency domain HVS based. They also compared the performance of several models on a subjective database.. The approach of this paper is to first identify typical perceptual degradations that may be annotated by naive human observers, for example in Open Profiling approaches [53] . Publications tackling these isolated perceptual degradations will be referenced. In a second step, existing published (complex) models are analyzed with respect to their capability of predicting these perceptual artifacts.. The paper is limited in the sense that it cannot and will not exhaustively cover one or the other. It shall provide an incomplete list of algorithms to measure isolated degradations that were previously used in order to stimulate comparison. It shall further show which types of degradations measurement have previously been used in combined measurement algorithms. A continuous update of this list is expected to take place within the VQEG JEG group. The goal of this paper is to help in the identification of the artifacts and their measurement algorithms that may form the advantage of a Hybrid-NR measurement, providing access to both bit-stream measurement indicators and pixel based indicators.. The paper is organized as follows. In Section 2, the development of objective video quality prediction algorithms is introduced. For one of the identified components, the perceptual features and their algorithmic indicators existing methods are reviewed in Section 3. Complex algorithms for video quality measurement are reviewed in Section 4 with particular emphasis on their indicators. Section 5 proposes methods to identify the scope of the indicators and suggests optimization criteria. Currently ongoing collaborative efforts are documented in Section 6 before the paper is concluded in Section 7. The research on subjective and objective video quality assessment is often considered an interdisciplinary task. A lot of effort has been spent by different research communities to identify and measure perceptual attributes of the HVS, to develop, train, verify, and validate individual indicators, or to develop perceptually meaningful spatial and temporal pooling algorithms. Data mining and machine learning has been applied to combine the different predictions into a global quality score. It has however been shown that some of the isolated indicators may not be suitable for the complete application range or they may behave erroneously when confronted with degradations outside of their scope. In this context, the importance of creating various subjectively evaluated datasets has been emphasized. Each such databases may contain either psychophysical stimuli or an isolated degradation type or an overall scope evaluation. The combination of the subjective voting given on each individual database needs to be tackled leading to the notion of an objective video quality scale. The individual indicators may then be analyzed for the degradations which they are supposed to measure, i.e. "in scope" degradations, which they may measure with less prediction performance "extended scope", and their neutral behavior in all other conditions "out of scope". VQEG JEG's Hybrid group is working towards organizing collaboration towards these goals. Kjell Brunnström, Ph.D. is a Senior Scientist at Acreo Swedish ICT AB and Adjunct Professor at Mid Sweden University. He is an expert in image processing, computer vision, image and video quality assessment having worked in the area for more than 25 years, including work in Sweden, Japan and UK. He has written a number of articles in international peer-reviewed scientific journals and conference papers, as well as having reviewed a number of scientific articles for international peer-reviewed journals. He has been awarded fellowships by the Royal Swedish Academy of Engineering Sciences as well as the Royal Swedish Academy of Sciences. He has supervised Ph.D. and M.Sc students. Currently, he is leading standardisation activities for video quality measurements as Co-chair of the Video Quality Experts Group (VQEG). He is one of two Swedish MC representative in EU-Cost action QUALINET. His current research interests are in Quality of Experience for visual media in particular video quality assessment both for 2D and 3D, as well as display quality related to the TCO requirements.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B I I O O O O O O O O O O O O O O O


Spurious responses suppression of parallel coupled-lines microstrip bandpass filters: Comparison and improvements of planar approaches In this paper, three methods to reject the first spurious harmonic in parallel-coupled microstrip filters are investigated. The authors proposed an extension of these approaches to also suppress the second unwanted passbands while keeping the initial passband practically unchanged. Then, two of these techniques are combined to achieve a wider upper stopband. Indeed, the sinusoidal perturbation technique is well combined with overcoupled end sections to reject the first three spurious passbands of an order three filter. A comparison of simulations and experimental results is made, with a discussion on each method efficiency and possible applications to higher order filters. Microstrip coupled-lines bandpass filters are widely used due to simple synthesis procedure and easy and low cost implementation. Designers can found synthesis equation in many microwave books or used built-in procedure in CAD microwave circuit software. But, this kind of filter suffers from spurious responses that limit their integration in RF frond end of microwave wireless systems. Indeed, these systems require filters with high selectivity and wide stopband while microstrip coupled-line filters exhibits their first spurious passband at twice the passband frequency f c . Theses harmonics of the operating frequency and the asymmetry of the passband are due to a difference between the phase velocities of even and odd-modes in each microstrip coupled-line section. This phase velocity difference comes from different field distributions for even and odd-modes in the inhomogeneous microstrip media. Designers sometimes react by using a low-pass filter behind a bandpass filter which, of course, increases the circuit size drastically. In general, three ways exists to reduce the spurious harmonics: equalizing the length view for each mode, decreasing the inhomogeneity of the media, and creating stopband or transmission zeros with additional elements/parameters at spurious passbands. To equalize phase velocities and symmetrize the passband, overcoupling [1] [2] is introduced at the end of the resonators to extend the odd-mode traveling path at the first spurious. Wiggly [3] or corrugated lines [4] are also used to equalize the traveling routes of the modes. But following that technique a perfect 2 nd harmonic cancellation is quite difficult to obtain because of empirical synthesis and a lack of layout generation facilities. Capacitors may also be used to extend the odd-mode traveling path. The second approach consists in a modification of the propagation media, for example with suspended substrate [5] , or multilayer technologies [6] and also with anisotropic media. These technological proposals modify the microstrip topology with certainly higher manufacturing costs or even with integration compatibility problems. In the third category, sinusoïdal perturbations [7] which periods are calculated following Bragg's law are added on coupled lines to create a stopband at spurious frequencies. Sometimes also called wiggly lines, different periods may be applied to achieve multispurious suppression. Stepped impedance resonator (SIR) filters [8] may also be merged into this kind of approach with broad stopband but with not also widespread synthesis method. Periodic patterns etched in the ground plane [9] , for example uniplanar compact photonic bandgap (UC-PBG) structures, is another way to improve the stopband responses of microstrip coupled-lines filter responses. Defect ground structures following several possibilities bring solutions to attenuate harmonics. Capacitive termination of parallel coupled lines with folded open stub has also been investigated recently [10] . This non-exhaustive list identifies the major and most efficient possibilities. From three of the most efficient ones that doesn't need to modify the propagation media nor the ground plane, our purpose is here to improve their efficiency and to bring new design approaches and advices to filter designers. The first investigated technique is the overcoupled end resonator one which will be extended to reject the first two harmonics for an order three filter. Then, a new simple concept introducing open stub at the end of the first coupled sections for the same filter will be compared to the first proposal. The third technique applied to the order three starting filter will associate overcoupled end resonator and sinusoidal perturbation to achieve three spurious harmonics suppression. Moreover, for this latter, we propose to use new hierarchical model of non-uniform symmetrical coupled-lines to make easier their simulation and layout generation. Circuit and electromagnetic (EM) simulations will be compared to experimental results. A final comparison on filter results, available number of spurious suppression for a given order, easy design and technological sensibility will be carried out. We proposed a new method with open stubs and improvements in the two other techniques (overcoupled end sections and period perturbation) to get multispurious rejection in microstrip coupled-line bandpass filter. The different methods performances were investigated and compared on the basis of the same initial filter for WiFi application. First, two harmonics were rejected and then the third one with a dual approach. The simplest and efficient method consists in open-stub placed symmetrically at the end of the first coupled-line sections. Three microstrip prototypes were designed and experimentally characterized to validate the suggested concepts. This techniques panel brings designers some advices and flexibilities for a wide range of applications. Moreover, symmetrical perturbation may be applied to hairpin filter and the non-uniform hierarchical models would facilitate the design of microwave systems requiring non-uniform lines such as wideband couplers, nonuniform match tapers in microstrip and coplanar waveguide or impedance steps with reduced radiation at millimeter frequencies.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An Explicit Nonlinear Mapping for Manifold Learning Abstract-Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, that there are no explicit mappings from the input data manifold to the output embedding. This prohibits the application of manifold learning methods in many practical problems such as classification and target detection. Previously, in order to provide explicit mappings for manifold learning methods, many methods have been proposed to get an approximate explicit representation mapping with the assumption that there exists a linear projection between the high-dimensional data samples and their low-dimensional embedding. However, this linearity assumption may be too restrictive. In this paper, an explicit nonlinear mapping is proposed for manifold learning, based on the assumption that there exists a polynomial mapping between the high-dimensional data samples and their low-dimensional representations. As far as we know, this is the first time that an explicit nonlinear mapping for manifold learning is given. In particular, we apply this to the method of locally linear embedding and derive an explicit nonlinear manifold learning algorithm, which is named neighborhood preserving polynomial embedding. Experimental results on both synthetic and real-world data show that the proposed mapping is much more effective in preserving the local neighborhood information and the nonlinear geometry of the high-dimensional data samples than previous work.Index Terms-Data mining, machine learning, manifold learning, nonlinear dimensionality reduction (NDR). In this paper, an explicit nonlinear mapping for manifold learning is proposed for the first time. Based on the assumption that there exists a polynomial mapping between high-and lowdimensional representations, an explicit polynomial mapping is obtained by applying this assumption to a generic model of manifold learning. Furthermore, the SNPPE algorithm is an NDR technique with an explicit nonlinear mapping, which tends to preserve not only the locality but also the nonlinear geometry of the high-dimensional data samples. SNPPE can provide convincing embedding results and locate new coming data samples in the reduced low-dimensional space simply and quickly at the same time. Experimental tests on both synthetic and real-world data have validated the effectiveness of the proposed SNPPE algorithm.. Compared with linear projection methods (such as LPP and ONPP), kernel-based nonlinear mapping methods (such as KLPP and KONPP) and incremental methods (such as RML and IAML), SNPPE gives a much better performance with linear running time complexity in locating new coming samples. However, kernel-based and incremental methods are time consuming for very large data sets. Since SNPPE is very fast and more accurate in embedding new data samples on the learned low-dimensional manifold, it may be expected to be used for practical online learning problems such as online classification, detection, and visual tracking. From the above two subsections, the difference between the SNPPE algorithm and the kernel embedding methods can be summarized as follows.. • In the SNPPE algorithm, the feature mapping φ is an explicitly defined polynomial of degree p, whereas in the kernel embedding methods, the feature mapping ψ is unknown. • In the SNPPE algorithm, the feature space F is a finitedimensional subspace of R pn , whereas in the kernel embedding methods, the feature space H is an unknown Hilbert space induced by the kernel κ and may be infinitedimensional.. • In the SNPPE algorithm, the optimization problem (27) is directly solved in the feature space; hence, the local geometric property of the data manifold can be optimally preserved in the feature space F. However, in the kernel embedding methods, the optimization problem (25) is solved within the range of ψ(X), which is only a subset of the feature space. Therefore, the geometric characteristics of the data manifold may not be optimally preserved in the feature space H. Table IV.) • The SNPPE algorithm provides a closed-form explicit mapping from X to Y, which does not depend on the training data and is very fast to embed new coming data. To the contrary, the mapping obtained by the kernel embedding methods is expressed in terms of the kernel and the training data; hence, it may be inefficient in dealing with very large data sets. In this section, we briefly review existing mapping methods for manifold learning, including those based on linear projections and out-of-sample nonlinear extensions for learned manifolds.. For convenience of presentation, the main notations used in this paper are summarized in Table I . Throughout this paper, all data samples are in the form of column vectors. The superscript of a data vector is the index of its component.

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B I I I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions Abstract-Facial expression recognition has numerous applications, including psychological research, improved human computer interaction, and sign language translation. A novel facial expression recognition system based on hybrid face regions (HFR) is investigated. The expression recognition system is fully automatic, and consists of the following modules: face detection, facial detection, feature extraction, optimal features selection, and classification. The features are extracted from both whole face image and face regions (eyes and mouth) using log Gabor filters. Then, the most discriminate features are selected based on mutual information criteria. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features are classified using the Naive Bayesian (NB) classifier. The proposed method has been extensively assessed using Cohn-Kanade database and JAFFE database. The experiments have highlighted the efficiency of the proposed HFR method in enhancing the classification rate. Since last decade, a growing interest in human computer interaction (HCI) systems has been developed. Automated Facial expression recognition is an important task in human computer interaction systems that include emotion processing. Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. Emotion or intention is often communicated by subtle changes in one or several discrete features. The addition or absence of one or more facial actions may alter its interpretation. In addition, some facial expressions may have a similar gross morphology but indicate varied meaning for different expression intensities.. Automatic facial expression analysis is a flourishing area of research in computer science. Problems that have been tackled with previously are the tracking of facial expression in static images and video sequences, the transfer of expressions to novel faces, the repurposing of a person's expression to a virtual model and recognition of facial expression. These recognition tasks have focused on the classification of emotional expressions [1] , classification of complex mental states [2] or the automatic recognition of FACS action units [3] .. A problem that is frequently encountered in each of these tasks is that of partial occlusions. Occlusions can introduce errors into the predicted expression or result in an incorrect expression being transferred to a virtual head. One type of partial occlusion is a temporary occlusion caused by a part of the face being obscured momentarily by an object or as a result of a person moving their head so that not all features of the face can be seen by a camera. Another type of occlusion is a systematic occlusion, which can be caused by a person wearing something such as a head-mounted display, which causes the features of the upper half of the face to be invisible. These types of occlusions are potentially more damaging since they result in whole features of relevance to judging facial expression being obscured.. Plenty of work has been done on facial expression recognition [15] , [17] , [19] , [21] . In this study, we investigate the part of the face that contains the most discriminative information for facial expression recognition system and propose hybrid face region method for feature extraction. An automatic classification of facial expressions consists of two stages: feature extraction and feature classification. The feature extraction is extremely important to the whole classification process. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In most cases of facial expression classification, the process of feature extraction yields a prohibitively large number of features and subsequently a smaller sub-set of features needs to be selected according to some optimality criteria.. The Gabor wavelet feature representation showed high performance in the recognition of facial actions from image sequences. Although the Gabor wavelet facial feature representations have been widely adopted [6] , [7] , [20] , it is computationally expensive to convolve the face images with the multi-level banks of the Gabor filters in order to extract the scale and the orientation coefficients. Furthermore, the Gabor wavelet analysis suffers from two major limitations: the maximum bandwidth of a Gabor filter is limited to approximately one octave and the Gabor filters are not optimal when the objective is to achieve broad spectral information with the maximum spatial localization. These drawbacks can be overcome when using the logarithmic form of the Gabor filters in the process of feature extraction.. The log Gabor filters are known to provide excellent simultaneous localization of spatial and frequency information, however the dimensionality of the resulting data is high. The dimensionality reduction can be achieved by selection of a small sub-set of the log Gabor features based on specified optimality criteria.. The dimensionality reduction can be achieved by selection of the more informative features based on feature selection and data reduction methods such as: principle component analysis (PCA), independent component analysis (ICA), mutual information (MI), etc. [10] , [11] , [12] . In this paper, the mutual information [25] is investigated to select the optimum features for classification. In contrast to the classical correlation-based feature selection methods, the mutual information can measure arbitrary relations between variables and it does not depend on transformations applied to different variables. It can be potentially useful in problems where methods based on linear relations between data are not performing well. Figure 1 illustrates a block diagram of the proposed facial expression recognition system.

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