In this letter, we investigate the price-based power allocation with rate proportional fairness constraint in downlink non-orthogonal multiple access (NOMA) systems. The Stackelberg game is utilized to model the interaction between the base station (BS) and users. The revenue maximization problem of the BS is first converted to rate allocation problem, then the optimal rate allocation for each user is obtained by variable substitution. Finally, a price-based power allocation with rate proportional fairness (PAPF) algorithm is proposed based on the relationship between rate and transmit power. Simulation results show that the proposed PAPF algorithm is superior to the previous price-based power allocation algorithm in terms of fairness index and minimum normalized user (MNU) rate.

This paper reports on the relationships between the performance of retro-reflectors and the sharpness of an aerial image formed with aerial imaging by retro-reflection (AIRR). We have measured the retro-reflector divergence angle and evaluated aerial image sharpness by use of the contrast-transfer function. It is found that the divergence angle of the retro-reflected light is strongly related to the sharpness of the aerial image formed with AIRR.

Wireless technologies that offer high data rate are generally energy-consuming ones while low-energy technologies commonly provide low data rate. Both kinds of technologies have been integrated in a single mobile device for different services. Therefore, if the service does not always require high data rate, the low energy technology, i.e., Bluetooth, can be used instead of the energy-consuming one, i.e., Wi-Fi, for saving energy. It is obvious that energy savings are maximized by turning the unused technology off. However, when active sessions of ongoing services migrate between different technologies, the network-layer connectivity must be maintained, or a vertical handover (VHO) between different networks is required. Moreover, when the networks are not interconnected, the VHO must be fully controlled by the device itself. The device typically navigates traffic through the firmware of the wireless network interface cards (WNIC) using their drivers, which are dependent on the vendors. To control the traffic navigation between WNICs without any modification of the WNICs' drivers, Software-Defined Networking (SDN) can be applied locally on the mobile device, the so called local SDN. In the local SDN architecture, a local SDN controller (SDNC) is used to control a virtual OpenFlow switch, which turns WNICs into its switch ports. Although the SDNC can navigate the traffic, it lacks the global view of the network topology. Hence, to correctly navigate traffic in a VHO process, an extended SDN controller (extSDNC) was proposed in a previous work. With the extSDNC, the SDNC can perform VHO based on a link layer trigger but with a significant packet loss rate. Therefore, in this paper, a framework named esVHO is proposed that executes VHO at the network layer to reduce the packet loss rate and reduce energy consumption. Experiments on VHO performance prove that esVHO can reduce the packet loss rate considerably. Moreover, the results of an energy saving experiment show that esVHO performs high energy saving up to 4.89 times compared to the others.

A fully-integrated system-on-chip (SoC) for Bluetooth Low Energy (BLE) with 3.2mA RX and 3.5mA TX current consumption is presented. To achieve both low current consumption and high performance, the SoC employs a sliding-IF architecture with high tolerance against out-of-band-blocking signals, a power management unit with improved efficiency, and techniques to reduce current in core circuits. The SoC achieves RX sensitivity of -93dBm and maximum output power of 0dBm. The SoC is in compliance with version 4.2 of the Bluetooth specifications and with the radio regulations of the FCC, ETSI, and ARIB. The SoC achieves the minimum level of current consumption for both RX and TX modes in the published product-level SoCs.

The IEEE 802.15.4 standard enables a short range, low data rate and low power communication between devices in wireless sensor networks (WSNs). In IEEE 802.15.4, a slotted carrier sensing multiple access with collision avoidance (CSMA/CA) algorithm is employed to coordinate a large number of sensor devices. Unlike IEEE 802.11 wireless LAN (WLAN), energy consumption requirements enable it to use fewer number of backoffs, which adversely increase collisions, resulting in degradation of energy consumption. In this letter, we devise an adaptive backoff scheme in WSN whose backoff range is adjusted depending on the contention level, and present its Markov model for mathematical analysis. The proposed scheme is analyzed and its efficiency is validated by ns-2 simulation in respect to network throughput and energy consumption. Its performance is also compared with the standard and previous works, showing that it outperforms them for a whole range of arrival rate.

Free-space optical (FSO) communication, which offers better data rate at a lower cost compared to radio-frequency (RF) backhauls, and is much easier to setup and maintain than optical cables, is gaining attention as an attractive substitute. Average capacity is one of the main performances metrics to understand the connectivity and data rates of a communication system but the performance analysis for mixed RF/FSO link is not straightforward as the RF link and the FSO link experiences different atmospheric perturbations. In this paper, we have investigated the ergodic capacity of a dual-hop mixed RF/FSO communication system realized with an average power scaling (APS) based amplify and forward (AF) relay. Assuming moderate to strong atmospheric turbulence, the FSO link is modeled by gamma-gamma distribution while it is assumed that the RF link experiences multipath Rayleigh fading. Simple analytical methods have been devised for obtaining concise closed-form expressions for ergodic capacity under four different rate/ power adaptation policies and are validated through extensive Monte Carlo simulations.

Improving energy efficiency is critical for embedded systems in our rapidly evolving information society. Near real-time data processing tasks, such as multimedia streaming applications, exhibit a common fact that their deadline periods are longer than their input intervals due to buffering. In general, executing tasks at lower performance is more energy efficient. On the other hand, higher performance is necessary for huge tasks to meet their deadlines. To minimize the energy consumption while meeting deadlines strictly, adaptive task scheduling including dynamic performance mode selection is very important. In this work, we propose an energy efficient slack-based task scheduling algorithm for such tasks by adapting to task size variations and applying DVFS with the help of statistical analysis. We confirmed that our proposal can further reduce the energy consumption when compared to oracle frame-based scheduling.

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods — a way in which one can represent heterogeneous data sets into a single form: as kernel matrices. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the EM algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the (completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices.

We focus on the construction of the digital signature scheme for local broadcast, which allows the devices with limited resources to securely transmit broadcast message. A multi-group authentication scheme that enables a node to authenticate its membership in multi verifiers by the sum of the secret keys has been proposed for limited resources. This paper presents a transformation which converts a multi-group authentication into a multi-group signature scheme. We show that the multi-group signature scheme converted by our transformation is existentially unforgeable against chosen message attacks (EUF-CMA secure) in the random oracle model if the multi-group authentication scheme is secure against impersonation under passive attacks (IMP-PA secure). In the multi-group signature scheme, a sender can sign a message by the secret keys which multiple certification authorities issue and the signature can validate the authenticity and integrity of the message to multiple verifiers. As a specific configuration example, we show the example in which the multi-group signature scheme by converting an error correcting code-based multi-group authentication scheme.

This paper studies the multi-link multi-antenna amplify-and-forward (AF) relay system, in which multiple source-destination pairs communicate with the aid of an energy harvesting (EH)-enabled relay and the relay utilizes the power splitting (PS) protocol to accomplish simultaneous EH and information forwarding (IF). Specifically, independent PS, i.e., allow each antenna to have an individual PS factor, and cooperative power allocation (PA) i.e., adaptively allocate the harvested energy to each channel, are proposed to increase the signal processing degrees of freedom and energy utilization. Our objective is to maximize the minimum rate of all source-destination pairs, i.e., the max-min rate, by jointly optimizing the PS and PA strategies. The optimization problem is first established for the ideal channel state information (CSI) model. To solve the formulated non-convex problem, the optimal forwarding matrix is derived and an auxiliary variable is introduced to remove the coupling of transmission rates in two slots, following which a bi-level iteration algorithm is proposed to determine the optimal PS and PA strategy by jointly utilizing the bisection and golden section methods. The proposal is then extended into the partial CSI model, and the final transmission rate for each source-destination pair is modified by treating the CSI error as random noise. With a similar analysis, it is proved that the proposed bi-level algorithm can also solve the joint PS and PA optimization problem in the partial CSI model. Simulation results show that the proposed algorithm works well in both ideal CSI and partial CSI models, and by means of independent PS and cooperative PA, the achieved max-min rate is greatly improved over existing non-EH-enabled and EH-enabled relay schemes, especially when the signal processing noise at the relay is large and the sources use quite different transmit powers.

This letter studies the price-based power control algorithm for the spectrum sharing cognitive radio networks. The primary user (PU) profits from the secondary users (SUs) by pricing the interference power made by them. The SUs cooperate with each other to maximize their sum revenue with the signal-to-interference plus noise ratio (SINR) balancing condition. The interaction between the PU and the SUs is modeled as a Stackelberg game. Closed-form expressions of the optimal price for the PU and power allocation for the SUs are given. Simulation results show the proposed algorithm improves the revenue of both the PU and fairness of the SUs compared with the uniform pricing algorithm.

The rapid development of information techniques has lead to more and more high-dimensional datasets, making classification more difficult. However, not all of the features are useful for classification, and some of these features may even cause low classification accuracy. Feature selection is a useful technique, which aims to reduce the dimensionality of datasets, for solving classification problems. In this paper, we propose a modified bat algorithm (BA) for feature selection, called MBAFS, using a SVM. Some mechanisms are designed for avoiding the premature convergence. On the one hand, in order to maintain the diversity of bats, they are guided by the combination of a random bat and the global best bat. On the other hand, to enhance the ability of escaping from local optimization, MBAFS employs one mutation mechanism while the algorithm trapped into local optima. Furthermore, the performance of MBAFS was tested on twelve benchmark datasets, and was compared with other BA based algorithms and some well-known BPSO based algorithms. Experimental results indicated that the proposed algorithm outperforms than other methods. Also, the comparison details showed that MBAFS is competitive in terms of computational time.

Improved fractional variable tap-length adaptive algorithm that contains Sigmoid limited fluctuation function and adaptive variable step-size of tap-length based on fragment-full error is presented. The proposed algorithm can solve many deficiencies in previous algorithm, comprising small convergence rate and weak anti-interference ability. The parameters are able to modify reasonably on the basis of different situations. The Sigmoid constrained function can decrease the fluctuant amplitude of the instantaneous errors effectively and improves the ability of anti-noise interference. Simulations demonstrate that the proposed algorithm equips better performance.

The cloud radio access network (C-RAN) is embracing unprecedented popularity in the evolution of current RAN towards 5G. One of the essential benefits of C-RAN is facilitating cooperative transmission to enhance capacity and energy performances. In this paper, we argue that the conventional symmetric coordination in which all antennas participate in transmission does not necessarily lead to an energy efficient C-RAN. Further, the current assessments of energy consumption should be modified to match this shifted paradigm in network architecture. Towards this end, this paper proposes an asymmetric coordination scheme to optimize the energy efficiency of C-RAN. Specifically, asymmetric coordination is approximated and formulated as a joint antenna selection and power allocation problem, which is then solved by a proposed sequential-iterative algorithm. A modular power consumption model is also developed to convert the computational complexity of coordination into baseband power consumption. Simulations verify the performance benefits of our proposed asymmetric coordination in effectively enhancing system energy efficiency.

Numerous methods have been developed to handle lighting variations in the preprocessing step of face recognition. However, most of them only use the high-frequency information (edges, lines, corner, etc.) for recognition, as pixels lied in these areas have higher local variance values, and thus insensitive to illumination variations. In this case, information of low-frequency may be discarded and some of the features which are helpful for recognition may be ignored. In this paper, we present a new and efficient method for illumination normalization using an energy minimization framework. The proposed method aims to remove the illumination field of the observed face images while simultaneously preserving the intrinsic facial features. The normalized face image and illumination field could be achieved by a reciprocal iteration scheme. Experiments on CMU-PIE and the Extended Yale B databases show that the proposed method can preserve a very good visual quality even on the images illuminated with deep shadow and high brightness regions, and obtain promising illumination normalization results for better face recognition performance.

Artificial lighting is responsible for a large portion of total energy consumption and has great potential for energy saving. This paper designs an LED light control algorithm based on users' localization using multiple battery-less binary human detection sensors. The proposed lighting control system focuses on reducing office lighting energy consumption and satisfying users' illumination requirement. Most current lighting control systems use infrared human detection sensors, but the poor detection probability, especially for a static user, makes it difficult to realize comfortable and effective lighting control. To improve the detection probability of each sensor, we proposed to locate sensors as close to each user as possible by using a battery-less wireless sensor network, in which all sensors can be placed freely in the space with high energy stability. We also proposed to use a multi-sensor-based user localization algorithm to capture user's position more accurately and realize fine lighting control which works even with static users. The system is actually implemented in an indoor office environment in a pilot project. A verification experiment is conducted by measuring the practical illumination and power consumption. The performance agrees with design expectations. It shows that the proposed LED lighting control system reduces the energy consumption significantly, 57% compared to the batch control scheme, and satisfies user's illumination requirement with 100% probability.

This paper studies the supervisory control of partially observed quantitative discrete event systems (DESs) under the fixed-initial-credit energy objective. A quantitative DES is modeled by a weighted automaton whose event set is partitioned into a controllable event set and an uncontrollable event set. Partial observation is modeled by a mapping from each event and state of the DES to the corresponding masked event and masked state that are observed by a supervisor. The supervisor controls the DES by disabling or enabling any controllable event for the current state of the DES, based on the observed sequences of masked states and masked events. We model the control process as a two-player game played between the supervisor and the DES. The DES aims to execute the events so that its energy level drops below zero, while the supervisor aims to maintain the energy level above zero. We show that the proposed problem is reducible to finding a winning strategy in a turn-based reachability game.

In heterogenous networks (HetNets), the deployment of small cells with the reuse of limited frequency resources to improve the spectral efficiency results in cross- and co-tier interference. In addition, the excessive power usage in such networks is also a critical problem. In this paper, we propose precoding and postcoding schemes to tackle interference and energy efficiency (EE) challenges in the two-tier downlink multiple-input-multiple-output (MIMO) HetNets. We propose transmission strategies based on hierarchical partial coordination (HPC) of the macro cell and small cells to reduce channel state information (CSI) exchange and guarantee the quality of service (QoS) in the upper tier with any change of network deployment in the lower tier. We employ the interference alignment (IA) scheme to cancel cross- and co-tier interference. Additionally, to maximize the EE, power allocation schemes in each tier are proposed based on a combination of Dinkelbach's method and the bisection searching approach. To investigate insights on the optimization problem, a theoretical analysis on the relationship between the maximum achievable EE and the transmit power is derived. Simulation results prove the superior EE performance of the proposed EE maximization scheme over the sum rate maximization approach and confirm the validity of our theoretical findings.

This paper presents a thin, compact beacon transmitter operating without needing battery replacement by using a photovoltaic (PV) film harvester. The beacon is formed of a power-control circuit (PCC) that can monitor small amounts of power from the harvester and properly control mode switching at low-power consumption. This leads to the realization of a maintenance-free beacon requiring no battery replacement. The beacon prototype is 55×20×2 mm in size and has a PV cell of 3 cm2. It allows a start-up operation from just 44-lux illuminance. The PV area required for the operation can be 1.7 times smaller than that of conventional beacons, thanks to the current saving with appropriate sequential control of the PCC. Since the beacon makes operation possible in emergency stairs, underground passages and other dark places, the application field for Internet of things (IoT) services can be expanded. Furthermore, a beacon equipped with a secondary battery (BSB: Beacon with Secondary Battery) can be configured by adding a charge-discharge power monitoring circuit. The BSB transmits an advertising packet during the daytime while charging surplus power, and works using the stored power during the night; this results in a continuous operation for one week with one transmission every 3 seconds even at 0-lux illuminance. Without developing a new radiofrequency chip or module, commercial low-power devices can be easily adjusted depending on the application by adding appropriate power-control circuits. We are convinced that this design scheme will be effective as a rapid design proposal for IoT services.

An output voltage-current equation of charge pump DC-DC voltage multiplier using diodes is provided to cover wide clock frequency and output current ranges for designing energy harvester operating at a near-threshold voltage or in sub-threshold region. Equivalent circuits in slow and fast switching limits are extracted. The effective threshold voltage of the diode in slow switching limit is also derived as a function of electrical characteristics of the diodes, such as the saturation current and voltage slope parameter, and design parameters such as the number of stages, capacitance per stage, parasitic capacitance at the top plate of the main boosting capacitor, and the clock frequency. The model is verified compared with SPICE simulation.