The Benders decomposition algorithm often shows poor convergence. To improve the convergence of the Benders decomposition algorithm. Recently, it was proposed the use of feasibility cuts closest to a solution in the set defined by all feasibility cuts. We extend this feasibility cut selection scheme to a new cut selection scheme for optimality cuts and propose a new Benders separation framework that a single linear programming problem can solve. We show that optimality cuts generated by this scheme are Pareto optimal when some conditions are satisfied. Theoretical connections to the existing Benders cut generation methods are also identified. Extensive computational experiments on the multiple classes of benchmark problems demonstrate that the proposed algorithm improves the convergence speed and computational time. Summary of Contribution: The Benders decomposition algorithm is one of the most widely used algorithms in operations research. However, the Benders decomposition algorithm often shows poor convergence for some optimization problems. In this paper, to improve the convergence of the Benders decomposition algorithm, we propose a unified closest Benders cut generation scheme. We give theoretical properties of the proposed Benders cuts, including Pareto optimality and facet-defining conditions. Also, we conducted extensive computational tests on various instances, such as network design and expansion problems. The results show the effectiveness of the closest Benders cut compared with existing algorithms and Cplex.

In this article we present a lifting algorithm and separation algorithms for robust cover inequalities of the binary robust knapsack problem using the Bertsimas and Sim model. First, we propose a polynomial time lifting algorithm for robust cover inequalities. Then the bounds on lifted coefficients are examined. We also propose three separation algorithms for robust cover inequalities and an exact separation algorithm for extended robust cover inequalities. Finally, the computational experiments exhibit the effect of proposed algorithms. The branch‐and‐cut algorithms with proposed lifting and separation algorithms are tested on the robust bandwidth packing problem and the robust knapsack problem.

We consider an energy-harvesting cognitive radio system where the secondary transmitter harvests energy. This system operates under an energy causality constraint mandating that the average energy consumption must not exceed the average harvested energy, and a collision constraint mandating the protection of the primary system. The purpose of this paper is to identify the optimal pairing of the sensing duration and the energy detector's sensing threshold in order to maximize the average throughput of the secondary network. Since the sensing duration and sensing threshold are intertwined with the energy causality constraint, they need to be redesigned with the purpose of conserving energy in mind. Hence, the sensing duration must be shorter while still satisfying the collision constraint. The numerical results show that the optimal sensing duration is determined based on which constraint, collision or energy causality, needs to have priority. In addition, the simulation results show a pairing of the optimal sensing duration and sensing threshold provided by the coordination between the two constraints, which gives insight into how to design them.

In cognitive radio, spectrum sensing is used to find the white spectrum or protect the primary user from interference caused by the secondary user (SU). There are two conventional spectrum sensing approaches: quiet and active. However, these conventional approaches have several problems. In quiet sensing, the quiet period degrades the SU capacity. With active sensing, the SU capacity is also degraded by the need for additional resource consumption and the mismatch in feedback information. In order to mitigate these problems, the structure of simultaneous PU sensing and data transmission is introduced. This structure is equipped with antenna isolation and self-interference cancellation in which the communication and the sensing radios are already assumed to be significantly isolated. This approach is designed so that the SU transmitter can sense PU signals and transmit data signals at the same time by dividing its spatial resources. Expanding on this work, we propose a concept of "TranSensing" which adaptively uses spatial resource according to the surrounding environments. To effectively use TranSensing, we propose a two-stage algorithm (TSA). Finally, the impact of residual interference on TranSensing is investigated. Simulation results show that TranSensing with TSA enhances the SU capacity over the conventional quiet or active sensing.

We consider energy harvesting cognitive radio networks to improve both energy efficiency and spectral efficiency. The goal of this paper is to analyze the theoretically achievable throughput of the secondary transmitter, which harvests energy from ambient sources or wireless power transfer systems while opportunistically accessing the spectrum licensed to the primary network. By modeling the temporal correlation of the primary traffic according to a time-homogeneous discrete Markov process, we derive the upper bound on the achievable throughput as a function of the energy arrival rate, the temporal correlation of the primary traffic, and the detection threshold for a spectrum sensor. The optimal detection threshold is then derived to maximize the upper bound on the achievable throughput under an energy causality constraint and a collision constraint. The energy causality constraint mandates that the total consumed energy should not exceed the total harvested energy, while the collision constraint is required to protect the primary network from secondary transmission. Analytical results show the temporal correlation of the primary traffic to enable efficient usage of the harvested energy by preventing the secondary transmitter from accessing the spectrum that may be occupied by the primary network.

We consider a clustered wireless network composed of energy-harvesting access points (APs) to improve both spectrum efficiency and energy efficiency. Each AP only uses the harvested energy for transmission and becomes inactive when the harvested energy is less than the required energy for transmission. The number of inactive APs increases as the number of users being served by an AP increases. Considering the relationship between the number of served users and the active AP density, we propose a spectrum-efficient operating policy designed to ensure that an AP serves the closest n users rather than all users in the cluster. We derive the outage probability and the area spectral efficiency (ASE) for an energy-harvesting clustered wireless network in closed form using stochastic geometry. Numerical results show that there is an optimal number of served users per AP that maximizes the ASE, which verifies that the ASE can be increased by the proposed spectrum-efficient operating policy.

This paper proposes an optimal transmit power control policy designed to maximize the average throughput of an energy-harvesting secondary network. As the transmit power increases, the energy-harvesting secondary network can enhance its throughput during a slot, but the probability of being active decreases due to the energy shortage. The probability of colliding with the primary signal also decreases as the transmit power increases. The optimal transmit power can be obtained by analyzing the effect of the transmit power on performance, especially as a function of statistics of harvested energy.

Portable unlicensed devices operating in TV white space must adhere to FCC rules designed to protect licensed devices. These rules decrease the achievable throughput for the portable unlicensed devices because the portable unlicensed devices are required to perform a channel availability query (CAQ) before transmitting. In order to reduce the number of CAQs, the Federal Communications Commission allows a region based mobility procedure (RMP). In this paper, we propose a new-RMP using directional information in order to enhance the achievable throughput. Directional information enables an unlicensed portable device to increase its achievable throughput by considering a uni- directional region that is smaller than an omni-directional region. Simulation results show that the proposed RMPs enhance the achievable throughput compared to the conventional RMPs. In addition, the achievable throughput for the proposed RMP using a navigation system is greatly enhanced compared to the conventional RMPs.

The bandwidth packing problem (BWP) concerns the selection of calls from a given set and the assignment of one path to each selected call. The ultimate aim of the BWP is to maximize profit while the routings of the selected calls observe the capacity constraints of the links. Here, we additionally consider queueing delays in the network, which may cause a deterioration in the quality of service to users if they exceed the acceptable limits. The integer programming formulation for the BWP with the queueing delay restriction contains a nonlinear constraint that is intrinsic to the model. We apply the Dantzig-Wolfe decomposition to this nonlinear constraint, and since the Dantzig-Wolfe decomposition has exponentially many variables, we propose the branch-and-price procedure to find optimal solutions. We also propose a generalized Dantzig-Wolfe reformulation based on the aggregation of variables, which makes our branch-and-price algorithm more competitive. Computational results on cases of randomly generated networks and some real-life telecommunication networks demonstrate that our algorithm performs well for large networks.

Abstract We consider a network design problem in which flow bifurcations are allowed. The demand data are assumed to be uncertain, and the uncertainties of demands are expressed by an uncertainty set. The goal is to install facilities on the edges at minimum cost. The solution should be able to deliver any of the demand requirements defined in the uncertainty set. We propose an exact solution algorithm based on a decomposition approach in which the problem is decomposed into two distinct problems: (1) designing edge capacities; and (2) checking the feasibility of the designed edge capacities with respect to the uncertain demand requirements. The algorithm is a special case of the Benders decomposition method. We show that the robust version of the Benders subproblem can be formulated as a linear program whose size is polynomially bounded. We also propose a simultaneous cut generation scheme to accelerate convergence of the Benders decomposition algorithm. Computational results on real‐life telecommunication problems are reported, and these demonstrate that robust solutions with very small penalties in the objective values can be obtained. © 2012 Wiley Periodicals, Inc. Networks, 2013.

In this paper, we investigate the effects of outdated channel state information (CSI) on an opportunistic relaying scheme (ORS) consisting of relay selection and data transmission phases. The outage probability and average symbol error rate (ASER) of the ORS are derived in a closed-form expression for an arbitrary link signal-to-noise ratio (SNR) when the CSIs in the source-relay and relay-destination links are outdated. We also analyze the diversity order and coding gain in terms of the outage probability and ASER. Analytical and numerical results show that when the CSIs in either the source-relay or relay-destination links are outdated, the diversity order is reduced to one. The overall performance of the ORS is mainly degraded by inaccurate CSI in the relay-destination link. Conversely, CSI accuracy in the source-relay link has a negligible effect on the performance of the ORS unless the CSI is perfect.

We consider energy harvesting cognitive radio networks in which a secondary transmitter harvests energy from ambient sources or wireless power transfer systems while opportunistically accessing the spectrum licensed to the primary network. The primary traffic is modeled as a time-homogeneous discrete Markov process, and the secondary transmitter may not be able to operate continuously due to sporadic and unstable energy sources. At the beginning of each time slot, the secondary transmitter thus needs to determine whether to remain idle so as to conserve energy, or to execute spectrum sensing to acquire knowledge of the current spectrum occupancy state. It also needs to configure the spectrum sensor detection threshold to achieve an effective tradeoff between false alarms and misdetections. This sequential decision-making, done to maximize the expected total throughput, requires the joint design of a spectrum sensing policy and a detection threshold under the energy causality and collision constraints. We formulate this stochastic optimization problem as a constrained partially observable Markov decision process (POMDP), and then convert it to a computationally tractable unconstrained POMDP. Numerical results show that the proposed approach enables efficient usage of the harvested energy by exploiting the temporal correlation of the primary traffic.

We consider a cognitive radio network with an energy-harvesting secondary transmitter to improve both energy efficiency and spectral efficiency. The goal of this paper is to determine an optimal spectrum sensing policy that maximizes the expected total throughput subject to an energy causality constraint and a collision constraint. The energy causality constraint comes from the fact that the total consumed energy should be equal to or less than the total harvested energy, while the collision constraint is required to protect the primary user. We first show that the system can be divided into a spectrum-limited regime and an energy-limited regime depending on where the detection threshold for the spectrum sensor lies. Assuming infinite battery capacity, we derive the optimal detection threshold that maximizes the expected total throughput subject to the energy causality constraint and the collision constraint. Analytical and numerical results show that the system is energy-limited if the energy arrival rate is lower than the expected energy consumption for a single spectrum access. They also show that a decreasing probability of accessing the occupied spectrum does not always result in decreased probability of accessing the idle spectrum in the energy-limited regime.

This paper investigates an optimal mode selection policy for cognitive radio sensor networks powered by RF energy harvesting. The RF energy harvesting enables the sensor node to operate with a potentially perpetual lifetime. We assume that the sensor node harvests RF energy received from the primary network and it cannot carry out RF energy harvesting and opportunistic spectrum access at the same time. Therefore, the sensor node should decide whether to access the spectrum or to harvest RF energy in each time slot to maximize an expected total throughput. We develop the optimal mode selection policy by casting this decision making problem in the framework of partially observable Markov decision process (POMDP). Numerical results show that the developed optimal policy finds a balance between obtaining the immediate throughput and harvesting the RF energy for future use.

In this paper, we investigate the effects of outdated channel state information (CSI) in opportunistic relaying systems on the ergodic capacity. The CSI in the data transmission instant may be different from that used in the relay selection instant due to channel variation. When the CSI in the relay-destination link is outdated, the average signal-to-noise ratio (SNR) and the ergodic capacity of the reactive decode and forward (DF) relaying system are derived. In addition, the effective ergodic capacity which is the spectral efficiency of the successful transmissions excluding the outage events is also derived. The numerical results show that if the CSI in the relay-destination link is outdated, the loss in the ergodic capacity is remarkable compared to the perfect CSI case. However, the level of the inaccuracy of the CSI does not affect the performance of the ergodic capacity in the reactive DF relaying system. In other words, no capacity degradation occurs even if the CSI inaccuracy becomes much larger.

A new interference management strategy is proposed to enhance the overall capacity of cellular networks (CNs) and device-to-device (D2D) systems. We consider M out of K cellular user equipments (CUEs) and one D2D pair exploiting the same resources in the uplink (UL) period under the assumption of M multiple antennas at the base station (BS). First, we use the conventional mechanism which limits the maximum transmit power of the D2D transmitter so as not to generate harmful interference from D2D systems to CNs. Second, we propose a δD-interference limited area (ILA) control scheme to manage interference from CNs to D2D systems. The method does not allow the coexistence (i.e., use of the same resources) of CUEs and a D2D pair if the CUEs are located in the δD-ILA defined as the area in which the interference to signal ratio (ISR) at the D2D receiver is greater than the predetermined threshold, δD. Next, we analyze the coverage of the δD-ILA and derive the lower bound of the ergodic capacity as a closed form. Numerical results show that the δD-ILA based D2D gain is much greater than the conventional D2D gain, whereas the capacity loss to the CNs caused by using the δD-ILA is negligibly small.

A new interference management scheme is proposed to improve the reliability of a device-to-device (D2D) communication in the uplink (UL) period without reducing the power of cellular user equipment (UE). To improve the reliability of the D2D receiver, two conventional receive techniques and one proposed method are introduced. One of the conventional methods is demodulating the desired signal first (MODE1), while the other is demodulating an interference first (MODE2), and the proposed method is exploiting a retransmission of the interference from the base station (BS) (MODE3). We derive their outage probabilities in closed forms and explain the mechanism of receive mode selection which selects the mode guaranteeing the minimum outage probability among three modes. Numerical results show that by applying the receive mode selection, the D2D receiver achieves a remarkable enhancement of outage probability in the middle interference regime from the usage of MODE3compared to the conventional ways of using only MODE1or MODE2.

With the quantized channel state information (CSI), space division multiple access (SDMA) can extract the multiplexing gain under the limited feedback burden. However, huge signaling burden of feedback can still suffer SDMA system because the total feedback data of SDMA is linearly dependent on the number of users. Hence, we propose the user restriction scheme to control the feedback load. In this scheme, the cut-off level, which restricts the feedbacks of poor conditioned users, is suggested for the reduction of the feedback burden without the performance loss. From simulation results, then, we show that the proposed feedback scheme can achieve not only the sum-rate gain but also the reasonable feedback reduction.

In multiple-input multiple-output (MIMO) broadcast channel, the lattice-reduction-aided (LRA) precoder is the modified zero-forcing beamforming (ZFBF) scheme which perturbs data symbols to reduce the transmit power. In this paper, we propose the optimal user group selection (UGS) scheme for the LRA-precoder to maximize the sum-rate in MIMO broadcast systems. We derive the approximated sum-rate of the LRA-precoder and the optimal UGS scheme. We also propose a suboptimal UGS scheme which requires low-complexity. From the simulation results, we show that the sum-rate of the proposed LRA-precoder with the optimal UGS scheme outperforms that of the conventional ZFBF with its optimal UGS scheme, especially when the number of users is small. In addition, we also show that the proposed LRA-precoder with the suboptimal UGS scheme has a considerably larger sum-rate than the conventional ZFBF with its suboptimal scheme, especially when the transmit correlation exists.

This paper examines an orthogonal random beamforming-based cross-tier interference reduction scheme for two-tier femtocell networks. In order to improve the immunity of both macrocell and femtocell users to cross-tier interference, we adopt a macrocell beam subset selection strategy. This beam subset selection strategy maximizes the throughput of the macrocell by optimizing the trade-off between the multiplexing gain and the multiuser interference. Simultaneously, the max-throughput scheduler suppresses the cross-tier interference with an adaptively reduced number of beams. Since the average cross-tier interference from the macrocell gradually decreases as the number of beams decreases, the beam subset selection strategy is capable of providing a spatial opportunity to the femtocell network. This spatial opportunity enables the femtocell network to take advantage of the selectivity arising from the correlation between the interference channel and the precoding matrix. Therefore, we also propose opportunistic channel selection and distributed power control strategies for the femtocell network. Both analytical and numerical results show that the proposed strategies collaboratively reduce the cross-tier interference in two-tier femtocell networks.

We investigate a non-linear precoding techniques for multi-user multiple-input multiple-output (MU-MIMO) downlink. When assuming full knowledge of the channel state information at the transmitter, a vector perturbation (VP) is a promising precoding scheme that approaches sum capacity and has simple receiver. However, its encoding is nondeterministic polynomial time (NP)-hard problem. In the MU-MIMO downlink, this encoding problem becomes harder because the proper user selection must be considered. In this paper, we propose a new vector perturbation scheme based on eigenvalue normalization (VPEVN), which converts the NP-hard problem into a channel condition-based finite integer problem, resulting in a reduction of encoding complexity. In addition, it can select a good user set by simple maximum norm criterion owing to its orthogonalization effect.

We constructed a face database POSTECH face database (PF07). PF07 contains the true-color face images of 200 people, 100 men and 100 women, representing 320 various images (5 pose variations times 4 expression variations times 16 illumination variations) per person. All of the people in the database are Korean. We also present the results of face recognition experiments under various conditions using three baseline face recognition algorithms in order to provide an example evaluation protocol on the database. The database is expected to be used to evaluate the algorithm of face recognition for Korean people or for people with systematic variations.

This paper proposes a novel spontaneous facial expression classification method using the facial motion magnification which transforms the subtle facial expressions into the corresponding exaggerated facial expressions. Facial motion magnification consists of four steps: First, we perform the active appearance model (AAM) fitting to extract 70 facial feature points in the face image sequence. Second, we align the face image sequence using the static three feature points. Third, we estimate the motion vectors of 27 feature points using the feature point tracking method. Finally, we obtain the exaggerated facial expressions by magnifying the motion vectors of the 27 feature points. After facial motion magnification, we recognize the exaggerated facial expressions using the support vector machines (SVM) to classify the facial expression features. Experimental results of the subtle facial expression recognition show promising results of the proposed method.

Most existing works on orthogonal frequency division multiple access (OFDMA) multihop cellular networks have assumed half-duplex relay, which suffers from spectral inefficiency since it requires additional resources for relaying. In order to alleviate this half-duplex loss, a new relay transmission scheme using dirty paper coding (DPC) is proposed. By alternately forwarding the messages to two mobile stations (MSs) via two fixed relay stations (FRSs) for each subcarrier, the base station (BS) predicts and pre-cancels the inter-relay interference between FRSs in advance. This proposed DPC-aided transmission scheme matches well with the nature of the quasistatic fading channel between FRSs. We also propose a two step resource allocation algorithm to select the appropriate MSs and to maximize the achievable sum rate. Numerical results demonstrate that the achievable sum rate of the proposed DPC-aided multihop cellular networks is significantly increased by utilizing the multi-user and multi-relay diversity gain as well as pre-cancelling the inter-relay interference between FRSs.

In this paper, we proposes an effective and novel approach to recognize subtle facial expression method which is facial expression deformation. The proposed method deforms subtle facial expressions into corresponding extreme facial expressions. Facial expression deformation processes by extracting subtle motion vector of the predefined feature points and amplifying them. By adding amplified motion vector to Active Appearance Models (AAMs) fitted feature points, the extreme facial expression images is recovered (obtained) by the piece-wise affine warping. After facial expression deformation, we extract the shape and appearance features by projecting deformed facial expression image to the AAM shape and appearance model. We use the multi-class Support Vector Machines (SVMs) to classify the shape and appearance features. The facial expression recognition performance shows promising results of the proposed method.

In this paper, a problem for efficient use of subcarriers in downlink OFDMA multi-hop cellular networks is studied based on quality of service (QoS) guarantee of each mobile station (MS). We consider three possible transmission schemes in multi- hop networks: single-hop, multi-hop, and multi-hop with spatial reuse of subcarriers. We verify that the required number of sub-carriers to guarantee the target BER and the target data rate is changed according to the transmission scheme and QoS requirements as well as the channel condition. Based on this result, we propose a new transmission scheme selection algorithm to select the best performance scheme for the efficient utilization of the limited subcarriers. Through the numerical results, the performance of this algorithm is demonstrated by the number of supported MSs in the system and the blocking probability.

In the distributed space-time block coded (DSTBC) orthogonal frequency division multiplexing (OFDM) system (DSTBC-OFDM), there may exist distributed carrier frequency offsets (CFOs) by sharing the transmission antennas belonging to different nodes. In this paper, the signal model of the DSTBC-OFDM system with distributed CFOs is presented. Then we propose a new equalization technique via the modification of the QR decomposition (MQRD) in order to eliminate ICI and ISI which are caused by CFOs. The proposed MQRD scheme guarantees transmit diversity gain. Several kinds of equalization techniques are introduced and compared in terms of bit error rate (BER) performance and transmit diversity order. Simulation results show that the proposed method outperforms conventional equalization techniques and achieves equal transmit diversity order with perfectly synchronized DSTBC-OFDM system.

We consider the routing and wavelength assignment (RWA) problem on WDM ring networks without wavelength conversion. When the physical network and required connections are given, RWA is the problem to select a suitable path and wavelength among the many possible choices for each connection such that no two paths using the same wavelength pass through the same link. We give an integer programming formulation of the problem and propose an algorithm to solve it. Although the formulation has exponentially many variables, we solve the linear programming relaxation of it by using the column generation technique. We solve the column generation problem efficiently by decomposing the problem into several subproblems. After solving the linear programming relaxation, we apply the branch-and-price procedure to get an optimal solution. We test the proposed algorithm on some randomly generated data. Test results show that the algorithm gives optimal solutions to almost all instances under the given node limit of the branch-and-bound tree.

We consider the single allocation problem in the interacting three-hub network with fixed hub locations. In the single allocation hub network, the hubs are fully interconnected and each nonhub node has to be connected to exactly one of the hubs. The flows between each pair of nodes are sent using the hubs as intermediate switching points. The problem is to find an optimal allocation of nonhub nodes to the hubs which minimizes the total flow cost. We show that the single allocation problem is NP-hard as soon as the number of hubs is three, although the problem in a two-hub system has polynomial time algorithms. This paper provides a mixed integer formulation of the problem and considers the polyhedral properties of it. The formulation can also be used for the single allocation problem with fixed costs for opening links, the three-terminal cut problem, and the three-processor distribution problem. Computational experiences are reported for data given in the literature and randomly generated problems. © 2000 John Wiley & Sons, Inc.