The nth partial sums of a classical Fourier series have large oscillations near the jump discontinuities. This behaviour is the well-known Gibbs phenomenon. Recently, the inverse polynomial reconstruction method (IPRM) has been successfully implemented to reconstruct piecewise smooth functions by reducing the effects of the Gibbs phenomenon for Fourier series. This paper addresses the 2-D fractional Fourier series (FrFS) using the same approach used with the 1-D fractional Fourier series and finds that the Gibbs phenomenon will be observed in 1-D and 2-D fractional Fourier series expansions for functions at a jump discontinuity. The existing IPRM for resolution of the Gibbs phenomenon for 1-D and 2-D FrFS appears to be the same as that used for Fourier series. The proof of convergence provides theoretical basis for both 1-D and 2-D IPRM to remove Gibbs phenomenon. Several numerical examples are investigated. The results indicate that the IPRM method completely eliminates the Gibbs phenomenon and gives exact reconstruction results.

In a cognitive radio (CR) network, the channel sensing scheme used to detect the existence of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to the inefficiency of the sensing scheme. This may yield primary user interference and low system performance. In this paper, we present a learning-based scheme for channel sensing in CR networks. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. Simulation results show the effectiveness and efficiency of our proposed scheme.

Radio over Fiber (RoF) is a promising technology that is suitable for broadband wireless access systems to cover in-building areas and outdoor dead-spots. However, one issue in RoF transmission that should be considered is the nonlinear distortion caused by Electrical/Optical (E/O) converters. Multicarrier RF (Radio Frequency) signal formats such as Orthogonal Frequency Division Multiplexing (OFDM), which are commonly employed in broadband wireless communications, are weak against nonlinearities. To enable the linear transmission of OFDM signal in RoF channel, we propose to employ the Envelop Pulse Width Modulation (EPWM) transmission scheme for RoF channel. Two commonly used E/O converters, Mach-Zehnder modulator and direct-modulation of Distributed Feedback Laser Diode (DFB LD), are employed to validate the proposal. Based on the measured nonlinearities of the E/O converters, they are mathematically modeled and their transmission performance are analyzed. A modified Rapp model is developed for the modeling of the DFB LD. Through simulations and experiments, the proposed scheme is shown to be effective in dealing with the nonlinearities of the E/O converters.

This paper presents indoor experimental results on 4-by-2 multi-user (MU)-MIMO transmission with carrier aggregation (90-MHz bandwidth) achieving real-time 1-Gbps data transmission using adaptive modulation and coding (AMC) in the LTE-Advanced downlink employing OFDMA radio access. In the experiments, eigenvalue decomposition (EVD)-based channel state information (CSI) feedback based on subband unit for MU-MIMO operation and inter-user interference whitening filter applied before maximum likelihood detection (MLD) are employed to achieve such a high data rate with realistic numbers of transmitter and receiver antennas. The indoor experiments are conducted in a conference room under line-of-sight conditions with multiple reflected waves where one mobile station (MS) travels at walking speed and the other MS is static. The experimental results show that the total throughput for the 2 MSs is greater than 1Gbps at the average received signal-to-interference plus noise power ratio (SINR) of approximately 25 and 17dB for the first and second streams of each MS, respectively, when the moving speed is up to approximately 1km/h. The results also show that a centralized transmitter antenna arrangement is more effective in order to achieve a high data rate such as 1Gbps compared to a distributed antenna arrangement for the measurement environment.

Most peer-to-peer (P2P) systems build their own overlay networks for implementing peer selection strategies without taking into account the locality on the underlay network. As a result, a large quantity of traffic crossing internet service providers (ISPs) or autonomous systems (ASes) is generated on the Internet. Controlling the P2P traffic is therefore becoming a big challenge for the ISPs. To control the cost of the cross-ISP/AS traffic, ISPs often throttle and/or even block P2P applications in their networks. In this paper, we propose a router-aided approach for localizing the P2P traffic hierarchically; it features the insertion of additional delay into each P2P packet based on geographical location of its destination. Compared to the existing approaches that solve the problem on the application layer, our proposed method does not require dedicated servers, cooperation between ISPs and P2P users, or modification of existing P2P application software. Therefore, the proposal can be easily utilized by all types of P2P applications. Experiments on P2P streaming applications indicate that our hierarchical traffic localization method not only reduces significantly the inter-domain traffic but also maintains a good performance of P2P applications.

A decentralized secure protocol for casting trust rating in reputation systems (StR protocol) is lately proposed by Dimitriou and Michalas, and the StR protocol is verified to be faster than the previous work providing anonymous feedback. In this letter, we present new enhanced scheme of StR. Compared with StR protocol, our new approach attains the exactly same security, but requires less processing time and about half communication overheads. Therefore, we improve the performance without sacrificing any security, especially the communication delay is dramatically reduced.

This paper proposes a novel method for adaptively controlling the admission of interference to users in our previously proposed layered partially non-orthogonal block diagonalization (BD) precoding method for downlink multiuser multiple-input multiple-output (MIMO) transmission that employs cooperation among multiple base stations (BSs). The proposed method is applicable when some of the instantaneous channel state information (CSI) feedback between the user equipment and the respective BSs is missing if the path loss between the user equipment and BS is higher than a predetermined threshold. The proposed method suppresses the loss in the transmitter diversity (beam forming) gain caused by the perfect nulling of inter-user interference in BD. By allowing the inter-user interference from a link that has a high average path loss, the overall throughput performance of simple BD is enhanced. We show that the combination of layered transmission that restricts the set of BSs used for the signal transmission and adaptive control of interference admission significantly increases the throughput of BS cooperative multiuser MIMO with partial CSI feedback.

This paper deals with carrier frequency offset (CFO) estimation based on the minimum variance distortionless response (MVDR) criterion without using specific training sequences for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. In the presence of large CFOs, the estimator is proposed to find a new CFO vector based on the first-order Taylor series expansion of the one initially given. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which allows one to readily solve it. Since raising the accuracy of residual CFO estimation can provide more accurate residual CFO compensation, this paper also present a decision-directed MVDR approach to improve the CFO estimation performance. However, the proposed estimator can estimate CFOs with less computation load. Several computer simulation results are provided for illustrating the effectiveness of the proposed blind estimate approach.

A recursive construction of (k+1)-ary error-correcting signature code is proposed to identify users for MAAC, even in the presence of channel noise. The recursion is originally from a trivial signature code. In the (j-1)-th recursion, from a signature code with minimum distance of 2j-2, a longer and larger signature code with minimum distance of 2j-1 is obtained. The decoding procedure of signature code is given, which consists of error correction and user identification.

In this paper, we propose a synthesizable LDPC decoder IP core for the WiMAX system with high parallelism and enhanced error-correcting performance. By taking the advantages of both layered scheduling and fully-parallel architecture, the decoder can fully support multi-mode decoding specified in WiMAX with the parallelism much higher than commonly used partial-parallel layered LDPC decoder architecture. 6-bit quantized messages are split into bit-serial style and 2bit-width serial processing lines work concurrently so that only 3 cycles are required to decode one layer. As a result, 12∼24 cycles are enough to process one iteration for all the code-rates specified in WiMAX. Compared to our previous bit-serial decoder, it doubles the parallelism and solves the message saturation problem of the bit-serial arithmetic, with minor gate count increase. Power synthesis result shows that the proposed decoder achieves 5.83pJ/bit/iteration energy efficiency which is 46.8% improvement compared to state-of-the-art work. Furthermore, an advanced dynamic quantization (ADQ) technique is proposed to enhance the error-correcting performance in layered decoder architecture. With about 2% area overhead, 6-bit ADQ can achieve the error-correcting performance close to 7-bit fixed quantization with improved error floor performance.

In this letter, we present a novel interference-aware clustering scheme for cell broadcasting service. The proposed approach is based on a genetic algorithm for re-clustering. Using the genetic algorithm, the suggested method efficiently re-clusters the user nodes when the relays fail in receiving the cell broadcasting message from the base station. The simulation results exhibit that the proposed clustering scheme can maintain much higher capacity than the conventional clustering scheme in the cases of relay outage. The re-clustering method based on genetic algorithm also shows lower complexity than the re-clustering approach based on exhaustive search.

In the Cloud computing era, users could have their data outsourced to cloud service provider (CSP) to enjoy on-demand high quality service. On the behalf of the user, a third party auditor (TPA) which could verify the real data possession on CSP is critically important. The central challenge is to build efficient and provably secure data verification scheme while ensuring that no users' privacy is leaked to any unauthorized party, including TPA. In this paper, we propose the first identity-based public verification scheme, based on the identity-based aggregate signature (IBAS). In particular, by minimizing information that verification messages carry and TPA obtains or stores, we could simplify key management and greatly reduce the overheads of communication and computation. Unlike the existing works based on certificates, in our scheme, only a private key generator (PKG) has a traditional public key while the user just keeps its identity without binding with certificate. Meanwhile, we utilize privacy-preserving technology to keep users' private data off TPA. We also extend our scheme with the support of batch verification task to enable TPA to perform public audits among different users simultaneously. Our scheme is provably secure in the random oracle model under the hardness of computational Diffie-Hellman assumption over pairing-friendly groups and Discrete Logarithm assumption.

Crosstalk-induced noise has become a key problem in interconnect optimization when technology improves, spacing diminishes, and coupling capacitance/inductance increases. Buffer insertion/sizing is one of the most effective and popular techniques to reduce interconnect delay and decouple coupling effects. It is traditionally applied to post-layout optimization. However, it is obviously infeasible to insert/size hundreds of thousands buffers during the post-layout stage when most routing regions are occupied. Therefore, it is desirable to incorporate buffer planning into floorplanning to ensure timing closure and design convergence. In this paper, we first derive formulae of buffer insertion for timing and noise optimization, and then apply the formulae to compute the feasible regions for inserting buffers to meet both timing and noise constraints. Experimental results show that our approach achieves an average success rate of 80.9% (78.2%) of nets meeting timing constraints alone (both timing and noise constraints) and consumes an average extra area of only 0.49% (0.66%) over the given floorplan, compared with the average success rate of 75.6% of nets meeting timing constraints alone and an extra area of 1.33% by the BBP method proposed previously.

We propose a new face relighting method using an illuminance template generated from a single reference portrait. First, the reference is wrapped according to the shape of the target. Second, we employ a new spatially variant edge-preserving smoothing filter to remove the facial identity and texture details of the wrapped reference, and obtain the illumination template. Finally, we relight the target with the template in CIELAB color space. Experiments show the effectiveness of our method for both grayscale and color faces taken from different databases, and the comparisons with previous works demonstrate a better relighting effect produced by our method.

The “Blind Men and an Elephant” is an old Indian story about a group of blind men who encounter an elephant and do not know what it is. This story describes the difficulties of understanding a large concept or global view based on only local information. Modern technologies enable us to easily obtain and retain local information. However, simply collecting local information does not give us a global view, as evident in this old story. This paper gives a concrete model of this story on the plane to theoretically and mathematically discuss it. It analyzes what information we can obtain from collected local information. For a convex target object modeling the elephant and a convex sensing area, it is proven that the size and perimeter length of the target object are the only parameters that can be observed by randomly deployed sensors modeling the blind men. To increase the number of observable parameters, this paper argues that non-convex sensing areas are important and introduces composite sensor nodes as an approach to implement non-convex sensing areas. The paper also derives a model on the discrete space and analyzes it. The analysis results on the discrete space are applicable to some network related issues such as link quality estimation in a part of a network based on end-to-end probing.

This paper presents a VLSI design of a Tomlinson-Harashima (TH) precoder for multi-user MIMO (MU-MIMO) systems. The TH precoder consists of LQ decomposition (LQD), interference cancellation (IC), and weight coefficient multiplication (WCM) units. The LQ decomposition unit is based on an application specific instruction-set processor (ASIP) architecture with floating-point arithmetic for high accuracy operations. In the IC and WCM units with fixed-point arithmetic, the proposed architecture uses an arrayed pipeline structure to shorten a circuit critical path delay. The implementation result shows that the proposed architecture reduces circuit area and power consumption by 11% and 15%, respectively.

Ultra-high luminance lamps emitting white light with a well-scattered blue spectrum from InGaN/GaN laser diodes and a phosphor-converted yellow spectrum show speckle contrast values as low as LED. Spectral behavior of the laser diodes is analyzed to find the reason why such low values are obtained. As a result, the PWM-driven, multi-longitudinal mode with dynamically broadened line-width is found to have a great effect on reducing speckle contrast. Despite using the lasers, such speckle-free lamps are considered to be very suitable for high-luminance and other various lighting applications.

In this paper, a practical antenna selection (AS) scheme is investigated for downlink multiuser massive multiple input multiple output (MIMO) networks where a base station (BS) is equipped with many antennas (N) and communicates with K mobile stations (MSs) simultaneously. In the proposed antenna selection technique, S antennas (S≤N) are selected for transmission based on the knowledge of channel coefficients of each MS for reducing the number of RF chains which mainly induce cost increase in terms of size, hardware, and power. In the proposed AS technique, a BS first ranks antenna elements according to the sum of their channel gains to all MSs. Then, the BS computes the downlink sum-rate with S consecutive antenna elements in the ordered set, where the subset consisting of S consecutive antennas is called a window. The BS selects the window resulting in the highest sum-rate. The selected S antenna elements are used for transmitting signals to multiple users, while the remaining (N-S) antenna elements are turned off for the time slot. Therefore, the proposed AS technique requires only (N-S+1) sum-rate computations, while the optimal AS technique involves $inom{N}{S}$ computations. We analyze downlink sum-rate with the proposed AS technique and compare it with that of a reference system with the same number of antenna elements without AS. Our results show that the proposed AS technique significantly outperforms the reference scheme.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

In this paper, we propose a novel content-aware image resizing method based on grid transformation. Our method focuses on not only keeping important regions unchanged but also keeping the aspect ratio of the main object in an image unchanged. The dual conditions can avoid distortion which often occurs when only using the former condition. Our method first calculates image importance. Next, we extract the main objects on an image by using image importance. Finally, we calculate the optimal grid transformation which suppresses changes in size of important regions and in the aspect ratios of the main objects. Our method uses lower and upper thresholds for transformation to suppress distortion due to extreme shrinking and enlargement. To achieve better resizing results, we introduce a boundary discarding process. This process can assign wider regions to important regions, reducing distortions on important regions. Experimental results demonstrate that our proposed method resizes images with less distortion than other resizing methods.