In multiuser cognitive radio (CR) networks, we address the problem of joint transmit beamforming (BF) and power control (PC) for secondary users (SUs) when they are allowed to transmit simultaneously with primary users (PUs). The objective is to optimize the network sum rate under the interference constraints of PUs, which is a nonconvex problem. Iterative dual subgradient (IDuSuG) algorithm is proposed by iteratively performing BF and PC to optimize the sum rate, among which minimum mean square error (MMSE) or virtual power-weighed projection (VIP2) is used to design beamformers and subgradient method is used to control the power. VIP2 algorithm is devised for the case in which the interference caused by MMSE beamformer exceeds the threshold. Moreover, channel uncertainty due to lack of cooperation is considered. A closed-form worst-case expression is derived, with which the uncertainty optimization problem is transformed into a certain one. A robust algorithm based on IDuSuG is provided by modifying updates in iterative process. Furthermore, second-order cone programming approximation (SOCPA) method is proposed as another robust algorithm. Typical network models are approximated to SOCP problems and solved by interior-point method. Finally the network sum rates for different PU and SU numbers are assessed for both certainty and uncertainty channel models by simulation.

In this paper, a low complexity hybrid smart antenna system with directional elements and reduced-size digital beamformer is proposed to combat the inter-symbol interference (ISI) problem over frequency-selective fading channel. For the conventional smart antenna system with omni-directional elements, it utilizes the full-size digital beamformer to suppress interference and obtain the optimum performance. However, the proposed hybrid smart antenna system with directional elements can be split the linear array receiver for two branches. One branch is the subarray system with non ISI interference, which can be used for maximum ratio combiner (MRC). Another branch is the reduced-size subarray system with the ISI interference, which can use the reduced-size optimum beamformer to suppress interference. Finally, the output signals of the two branches can be combined to detect the transmitted signals. Simulation results confirm that the proposed low complexity system can provide robust performance under the multipath fading channel.

This paper presents a 65-nm CMOS 8-antenna array transmitter operating in 117–130-GHz range for short range and portable millimeter-wave (mm-wave) active imaging applications. Each antenna element is a new on-chip antenna located on the top metal. By using on-chip transformer, pulse output of each resistor-less mm-wave pulse generators (PG) are sent to each integrated antenna. To adjust pulse delays for the purpose of pulse beam-forming, a 7-bit digitally programmable delay circuit (DPDC) is added to each of PGs. Moreover, in order to dynamically adjust pulse delays among eight SW's outputs, we implemented on-chip jitter and relative skew measuring circuit with 20-bit digital output to achieve cumulative distribution (CDF) and probability density (PDF) functions from which DPDC's input codes are decided to align eight antenna's output pulses. Two measured radiation peaks after relative skew alignment are obtained at (θ; φ) angles of (-56; 0) and (+57; 0). Measurement results shows that beam-forming angles of the fully integrated antenna array can be adjusted by digital input codes and by the on-chip skew adjustment circuit for active imaging applications.

The doubly constrained robust Capon beamformer (DCRCB), which employs a spherical uncertainty set of the steering vector together with the constant norm constraint, can provide robustness against arbitrary array imperfections. However, its performance can be greatly degraded when the uncertainty bound of the spherical set is not properly selected. In this paper, combining the DCRCB and the weight-vector-norm-constrained beamformer (WVNCB), we suggest a new robust adaptive beamforming method which allows us to overcome the performance degradation due to improper selection of the uncertainty bound. In WVNCB, its weight vector norm is limited not to be larger than a threshold. Both WVNCB and DCRCB belong to a class of diagonal loading methods. The diagonal loading range of WVNCB, which dose not consider negative loading, is extended to match that of DCRCB which can have a negative loading level as well as a positive one. In contrast to the conventional DCRCB with a fixed uncertainty bound, the bound in the proposed method varies such that the weight vector norm constraint is satisfied. Simulation results show that the proposed beamformer outperforms both DCRCB and WVNCB, being far less sensitive to the uncertainty bound than DCRCB.

In this paper, a geometric source separation system using nonnegative matrix factorization (NMF) is proposed. The adaptive beamformer is the best method for geometric source separation, but it suffers from a “target signal cancellation” problem in multi-path situations. We modified the HALS-NMF algorithm for decomposition into bases, and developed an interference suppression module in order to cancel the interference bases. A performance comparison between the proposed and subband GSC-RLS algorithm using a MATLAB® simulation was executed; the results show that the proposed system is robust in multi-path situations.

In processing stream data, time is one of the most significant facts not only because the size of data is dramatically increased but because the characteristics of data is varying over time. To learn stream data evolving over time effectively, it is required to detect the drift of concept. We present a window adaptation function on domain value (WAV) to determine the size of windowed batch for learning algorithms of stream data and a method to detect the change of data characteristics with a criterion function utilizing correlation. When applying our adaptation function to a clustering task on a multi-stream data model, the result of learning synopsis of windowed batch determined by it shows its effectiveness. Our criterion function with correlation information of value distribution over time can be the reasonable threshold to detect the change between windowed batches.

In cellular systems, particular in the cell edge, the user terminals (UTs) are suffered from the attenuation of the signal from their target base station (BS) and the relatively strong interferences from BSs of other users. This paper investigates the performance improvement under this bad situation by BS cooperation (BSC) in the downlink scenario using multiantenna transmission assuming the perfect channel state information (CSI), and compares the effectiveness of several strategies based on a three cell model. Through computer simulations, the performance improvement by BSC is verified. Then the result is extended to multiple stream transmission utilizing the feature of multiantenna, and advantage of BSC with data sharing is shown.

In this study, we propose a one dimensional (1D) based successive generalized sidelobe canceller (GSC) structure for the implementation of 2D adaptive beamformers using a uniform rectangular antenna array (URA). The proposed approach takes advantage of the URA feature that the 2D spatial signature of the receive signal can be decomposed into an outer product of two 1D spatial signatures. The 1D spatial signatures lie in the column and the row spaces of the receive signal matrix, respectively. It follows that the interferers can be successively eliminated by two rounds of 1D-based GSC structure. As compared to the conventional 2D-GSC structure, computer simulations show that in addition to having significantly low computational complexity, the proposed adaptive approach possesses higher convergence rate.

In this paper, we present a cognitive relay network with two primary transceivers that communicate via several distributed relay terminals. Spectrum sensing is deployed at the relays to sense the absence/presence of the primary transceivers based on energy detection. The primary network utilizes a two-step two-way amplify-and-forward (AF) scheme by using the cognitive radio (CR) terminals as its relay nodes when the primary network is not in operation, in contrast, the CRs communicate with their own base station (BS). In the first relaying step, the primary transceivers send their signal to the CRs/relays. Distributed beamforming is then performed in the second relaying step. Our aim is to set the beamforming weights so as to minimize the total power dissipated in the relay network while satisfying a target signal-to-noise ratio (SNR) at the primary transceivers and at the cognitive BS. This is achieved by solving an optimization problem that we formulate as a nonconvex quadratically constrained quadratic program (QCQP). This problem is solved efficiently by semidefinite relaxation (SDR) and Lagrangian duality. Simulation results are provided to demonstrate the superiority of our proposed technique, compared to classical beamforming techniques, in terms of power reduction.

This letter proposes a method for choosing the best quantized beamforming vector that represents a subcarrier group, for coordinated beamforming in the downlink of multiuser multiple input multiple output-orthogonal frequency division multiplexing systems. The correlation between subcarriers is exploited for reducing the feedforward overhead, while maximizing the sum rate.

This paper presents a novel chunk selection strategy for peer-to-peer video streaming, called enr-first selection policy, which simultaneously considers both block rarity and playback deadline. The policy intends to boost overall network-wide streaming performance, while ensuring good playback continuity of individual peers. The efficacy of the proposed scheme is validated through our performance evaluation study that demonstrates a substantial gain.

This paper presents novel hybrid push-pull protocols for peer-to-peer video streaming. Our approaches intend to reap the best of push- and pull-based schemes by adaptively switching back and forth between the two modes according to video chunk distributions. The efficacy of the proposed protocols is validated through an evaluation study that demonstrates substantial performance gains.

Recently, video streaming services using P2P (Peer-to-Peer) have attracted attention to solve the problem of load concentration on servers and to reduce large latency. Many P2P streaming systems, like Coolstreaming, however, take a complicated approach to control playback timing severely. This leads to less churn resiliency and less adaptability to fluctuation of network traffic. Therefore, we focus on a simple and robust approach to realize “pseudo” streaming with high quality, which is based on BitTorrent. In the existing methods with the simple approach, peers download pieces just closer to playback timing to decrease the playback discontinuity. However, these methods do not consider the constitution of video structure in sophisticated manner. P2P streaming system must consider several important metrics for high-quality and fair distribution. Therefore, in this paper, we propose a new P2P video streaming system considering the cooperation of three important metrics; video structure, playback timing, and piece dispersion on network. In this system, users vary three piece selections to suit the delivery status. Specifically, users preferentially download pieces which affect the video quality, which are closer to playback timing, and which improve the delivery efficiency. Moreover, we show the effectiveness of the proposed method by computer simulation.

We propose online prediction algorithms for data streams whose characteristics might change over time. Our algorithms are applications of online learning with experts. In particular, our algorithms combine base predictors over sliding windows with different length as experts. As a result, our algorithms are guaranteed to be competitive with the base predictor with the best fixed-length sliding window in hindsight.

Since the Content Distribution Network (CDN) and IP multicast have heavy infrastructure requirements, their deployment is quite restricted. In contrast, peer-to-peer (P2P) streaming applications are independent on infrastructures and thus have been widely deployed. Emerging wireless ad-hoc networks are poised to enable a variety of streaming applications. However, many potential problems, that are trivial in wired networks, will emerge when deploying existing P2P streaming applications directly into wireless ad-hoc networks. In this paper, we propose a goodput optimization framework for P2P streaming over wireless ad-hoc networks. A two-level buffer architecture is proposed to reassign the naive streaming systems' data requests. The framework adopts a chunk size-varying transmission algorithm to obtain smooth playback experience and acceptable overhead and utilize limited bandwidth resources efficiently. The distinguishing features of our implementation are as follows: first, the framework works as a middleware and is independent on the streaming service properties; existing P2P streaming application can be deployed in wireless ad-hoc networks with minimum modifications and development cost; second, the proposed algorithm can reduce unnecessary communication overheads compared with traditional algorithms which gain high playback continuity with small chunk size; finally, our scheme can utilize low bandwidth transmission paths rather than discarding them, and thus improve overall performance of the wireless network. We also present a set of experiments to show the effectiveness of the proposed mechanism.

This correspondence contributes to some d-form functions and d-form sequences. A property of d-form functions is obtained firstly. Then we present a way to construct d-form sequences and extended d-form sequences with ideal autocorrelation. Based on our result, many sequences with ideal autocorrelation can be constructed by the corresponding difference-balanced d-form functions.

In this letter, we focus on the spectrum sharing cognitive radio system, wherein a single-input multi-output cognitive fading channel is considered. Subject to the joint average interference constraint and peak interference constraint at the primary receiver, the outage capacity of the cognitive channel involving joint beamforming and power control is analyzed. We derive the optimal beamforming and power control strategy and deduce the closed-form expression for the outage capacity under Rayleigh fading model, the functional regions of two kinds of interference constraints are discussed as well. Furthermore, considering zero-outage transmission, we investigate the delay-limited capacity and introduce a new concept called the zero-outage average interference wall. Extensive simulations corroborate our theoretical results.

Per-flow traffic measurement is essential for network management; billing, traffic engineering, mitigating denial of service attacks, to mention just a few. In this field, the fundamental problem is that the size of expensive SRAM is too small to hold traffic data from high-speed networks. In this paper, we propose a new method for per-flow traffic measurement, which is based on the virtual vector that was originally designed for the problem of spread estimation. We modify the original virtual vector and show that this simple change yields a highly effective per-flow traffic estimator. Experiments show that our proposed scheme outperforms the state-of-the-art method in terms of both processing time and space requirement.

Recently, Yu, Gong and Tang found new constructions of binary sequences of period 4N with optimal autocorrelation magnitude by different interleaved structure of sequences and sequences which had special correlation property, respectively. In this paper, we derive more results on binary sequences of period 4N which also have optimal autocorrelation.

A Boolean function is said to be correlation immune if its output leaks no information about its input values. Such functions have many applications in computer security practices including the construction of key stream generators from a set of shift registers. Finding methods for easy construction of correlation immune Boolean functions has been an active research area since the introduction of the notion by Siegenthaler. In this paper, we present several constructions of nonpalindromic correlation immune symmetric Boolean functions. Our methods involve finding binomial coefficient identities and obtaining new correlation immune functions from known correlation immune functions. We also consider the construction of higher order correlation immunity symmetric functions and propose a class of third order correlation immune symmetric functions on n variables, where n+1(≥ 9) is a perfect square.