Spectrum sensing is one of the methods to identify available white spaces for secondary usage which was specified by the regulators. However, signal quality to be sensed can plunge to a very low signal-to-noise-ratio due to signal propagation and hence readings from individual sensors will be unreliable. Distributed sensing by the cooperation of multiple sensors is one way to cope with this problem because the diversity gain due to the combining effect of data captured at different position will assist in detecting signals that might otherwise not be detected by a single sensor. In effect, the probability of detection can be improved. We have implemented a distributed sensing system to evaluate the performance of different cooperative sensing algorithms. In this paper we describe our implementation and measurement experience which include the system design, specification of the system, measurement method, the issues and solutions. This paper also confirms the performance enhancement offered by distributed sensing algorithms, and describes several ideas for further enhancement of the sensing quality.

This paper presents the design and proof-of-concept validation of a novel network-assisted spectrum coordination (NASCOR) service for improved radio coexistence in future shared spectrum bands. The basic idea is to create an overlay network service for dissemination of spectrum usage information between otherwise independent radio devices and systems, enabling them to implement decentralized spectrum coexistence policies that reduce interference and improve spectrum packing efficiency. The proposed method is applicable to unlicensed band and shared spectrum systems in general (including femtocells), but is particularly relevant to emerging TV white spaces and cognitive radio systems which are still in need of scalable and accurate solutions for both primary-to-secondary and secondary-to-secondary coordination. Key challenges in enabling a network layer spectrum coordination service are discussed along with the description of our system architecture and a detailed case-study for a specific example of spectrum coordination: client-AP association optimization in dense networks. Performance gains are evaluated through large-scale simulations with multiple overlapping networks, each consisting of 15-35 access points and 50-250 clients in a 0.5×0.5 sq.km. urban setting. Results show an average of 150% improvement in random deployments and upto 7× improvements in clustered deployments for the least-performing client throughputs with modest reductions in the mean client throughputs.

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

Designing a medium access control (MAC) protocol is a key for implementing any practical wireless network. In general, a MAC protocol is responsible for coordinating users in accessing spectrum resources. Given that a user in cognitive radio(CR) networks do not have priority in accessing spectrum resources, MAC protocols have to perform dynamic spectrum access (DSA) functions, including spectrum sensing, spectrum access, spectrum allocation, spectrum sharing and spectrum mobility, beside conventional control procedure. As a result, designing MAC protocols for CR networks requires more complicated consideration than that needed for conventional/primary wireless network. In this paper, we focus on two major perspectives related to the design of a CR-MAC protocol: dynamic spectrum access functions and network infrastructure. Five DSA functions are reviewed from the point of view of MAC protocol design. In addition, some important factors related to the infrastructure of a CR network including network architecture, control channel management, the number of radios in the CR device and the number of transmission data channels are also discussed. The remaining challenges and open research issues are addressed for future research to aim at obtaining practical CR-MAC protocols.

Cognitive Radio (CR) is aimed at increasing the efficiency of spectrum utilization by allowing unlicensed users to access, in an opportunistic and non-interfering manner, some licensed bands temporarily and/or spatially unoccupied by the licensed users. The analysis of CR systems usually requires the spectral activity of the licensed system to be represented and characterized in a simple and tractable, yet accurate manner, which is accomplished by means of spectrum models. In order to guarantee the realism and accuracy of such models, the use of empirical spectrum occupancy data is essential. In this context, this paper explains the complete process of spectrum modeling, from the realization of field measurements to the obtainment of the final validated model, and highlights the main relevant aspects to be taken into account when developing spectrum usage models for their application in the context of the CR technology.

Ultra-wideband pulse radar exhibits high range resolution, and excellent capability in penetrating dielectric media. With that, it has great potential as an innovative non-destructive inspection technique for objects such as human body or concrete walls. For suitability in such applications, we have already proposed an accurate permittivity estimation method for a 2-dimensional dielectric object of arbitrarily shape and clear boundary. In this method, the propagation path estimation inside the dielectric object is calculated, based on the geometrical optics (GO) approximation, where the dielectric boundary points and its normal vectors are directly reproduced by the range point migration (RPM) method. In addition, to compensate for the estimation error incurred using the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated, where an initial guess of the relative permittivity and dielectric boundary are employed for data regeneration. This study introduces the 3-dimensional extension of the above permittivity estimation method, aimed at practical uses, where only the transmissive data are effectively extracted, based on quantitative criteria that considers the spatial relationship between antenna locations and the dielectric object position. Results from a numerical simulation verify that our proposed method accomplishes accurate permittivity estimations even for 3-dimensional dielectric medium of wavelength size.

In this paper, we focus on a centralized spectrum access strategy in a cognitive radio network, in which a single licensed spectrum with one primary user (PU) and several secondary users (SUs) (multiple input streams) are considered. We assume the spectrum can be divided into multiple channels and the packets from variable SUs can arrive at the system simultaneously. Taking into account the priority of the PU, we suppose that one PU packet can occupy the whole licensed spectrum, while one SU packet will occupy only one of the channels split from the licensed spectrum when that channel is not used. Moreover, in order to reduce the blocking ratio of the SUs, a buffer with finite capacity for the SUs is set. Regarding the packet arrivals from different SUs as multiple input streams, we build a two-dimensional Markov chain model based on the phase of the licensed spectrum and the number of SU packets in the buffer. Then we give the transition probability matrix for the Markov chain. Additionally, we analyze the system model in steady state and derive some important performance measures for the SUs, such as the average queue length in the buffer, the throughput and the blocking ratio. With the trade-off between different performance measures, we construct a net benefit function. At last, we provide numerical results to show the change trends of the performance measures with respect to the capacity of the SU buffer under different network conditions, and optimize the capacity of the SU buffer accordingly.

A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.

In this paper, we investigate how to achieve call admission control (CAC) for guaranteeing call dropping probability QoS which is caused by handoff timeout in cognitive radio (CR) networks. When primary user (PU) appears, spectrum handoff should be initiated to maintain secondary user (SU)'s link. We propose a novel virtual queuing (VQ) scheme to schedule spectrum handoff requests sent by multiple SUs. Unlike the conventional first-come-first-served (FCFS) scheduling, resuming transmission in the original channel has higher priority than switching to another channel. It costs less because it avoids the cost of signaling frequent spectrum switches. We characterize the handoff delay on the effect of PU's behavior and the number of SUs in CR networks. And user capacity under certain QoS requirement is derived as a guideline for CAC. The analytical results show that call dropping performance can be greatly improved by CAC when a large amount of SUs arrives fast as well as the VQ scheme is verified to reduce handoff cost compared to existing methods.

Cooperative wireless communication is a communication mechanism to attain diversity through virtual antenna array that is formed by sharing resources among different users. Different strategies of resource utilization such as amplify-and-forward (AF) and decode-and-forward (DF) already exist in cooperative networks. Although the implementation of these strategies is simple, their utilization of the channel state information (CSI) is generally poor. As a result, the outage and bit error rate (BER) performances need much more improvement in order to satisfy the upcoming high data rate demands. For that to happen the spectral efficiency supported by a wireless system at a very low outage probability should be increased. In this paper a new approach, based on the previously existing ones, called CSI directed estimate and forward (CDEF) with a reduced estimation domain is proposed. A closed form solution for the optimal signal estimation at the relay using minimum mean square error (MMSE) as well as a possible set reduction of the estimation domain is given. It will be shown that this new strategy attains better symbol error rate (SER) and outage performance than AF or DF when the source relay link is comparatively better than the relay destination link. Simulation results also show that it has got better spectral efficiency at low outage probability for a given signal to noise ratio (SNR) as well as for a fixed outage probability in any operating SNR range.

In cognitive radio networks, the dynamic traffic of the primary user can lead to not only the spectrum sensing performance degradation, but also co-channel interference between primary user and secondary user, and, furthermore, the secondary system throughput can be decreased. Taking into account the impact of the dynamic primary-user traffic on spectrum sensing performance and the secondary throughput, we study the optimization problem of maximizing the secondary throughput under the constraints of probability of detection, average interference and transmit power budget, and derive its optimal solution. The optimal power allocation scheme and the algorithm that can find the optimal sensing time are also proposed. The proposed algorithm is of great practical significance in the scenario where primary-user traffic varies very quickly, for example, in public safety spectrum band.

This paper studies channel sounding for selfish dynamic spectrum control (S-DSC) in which each link dynamically maps its spectral components onto a necessary amount of discrete frequencies having the highest channel gain of the common system band. In S-DSC, it is compulsory to conduct channel sounding for the entire system band by using a reference signal whose spectral components are sparsely allocated by S-DSC. Using nonuniform sampling theory, this paper exploits the finite impulse response characteristic of frequency selective fading channels to carry out the channel sounding. However, when the number of spectral components is relatively small compared to the number of discrete frequencies of the system band, reliability of the channel sounding deteriorates severely due to the ill-conditioned problem and degradation in channel capacity of the next frame occurs as a result. Aiming at balancing frequency selection diversity effect and reliability of channel sounding, this paper proposes an S-DSC which allocates an appropriate number of spectral components onto discrete frequencies with low predicted channel gain besides mapping the rest onto those with high predicted channel gain. A numerical analysis confirms that the proposed S-DSC gives significant enhancement in channel capacity performance.

In this letter, we first introduce a stronger notion of the optimistic achievable coding rate and discuss a coding theorem. Next, we give a necessary and sufficient condition under which the coding rates of all the optimal FF codes asymptotically converge to a constant.

We consider two methods for constructing high rate punctured convolutional codes. First, we present the best high rate R=(n-1)/n punctured convolutional codes, for n=5,6,…,16, which are obtained by exhaustive searches. To obtain the best code, we use a regular convolutional code whose weight spectrum is equivalent to that of each punctured convolutional code. We search these equivalent codes for the best one. Next, we present a method that searches for good punctured convolutional codes by partial searches. This method searches the codes that are derived from rate 1/2 original codes obtained in the first method. By this method, we obtain some good punctured convolutional codes relatively faster than the case in which we search for the best codes.

In this paper we consider fixed-to-fixed length (FF) coding of a general source X with vanishing error probability and define two kinds of optimalities with respect to the coding rate and the redundancy, where the redundancy is defined as the difference between the coding rate and the symbolwise ideal codeword length. We first show that the infimum achievable redundancy coincides with the asymptotic width W(X) of the entropy spectrum. Next, we consider the two sets $mCH(X)$ and $mCW(X)$ and investigate relationships between them, where $mCH(X)$ and $mCW(X)$ denote the sets of all the optimal FF codes with respect to the coding rate and the redundancy, respectively. We give two necessary and sufficient conditions corresponding to $mCH(X) subseteq mCW(X)$ and $mCW(X) subseteq mCH(X)$, respectively. We can also show the existence of an FF code that is optimal with respect to both the redundancy and the coding rate.

Conventional CPM signals employ information sequence with time-unlimited phase shaping pulse (PSP) to achieve power and bandwidth efficient transmission. On the contrary, information sequence using time-limited PSP was believed to produce power-wasting data-independent discrete spectral lines in CPM spectra, and was suggested to be avoided. In this paper, we revisit this problem and adopt the time-limited PSP to replace the one with time-unlimited, it turns out to have an alternative solution to the CPM scheme. We first modify the spectral computing formula for the CPM with time-limited PSP (or CPM-TL) from conventional CPM formula and show that the discrete spectral lines appeared in the power density spectrum of CPM-TL signals can be diminished or become negligible by appropriately choosing PSP. We also show that this class of CPM can use any real number modulation index (h) and the resultant trellis structure of CPM guarantees the maximum constraint length allowed by the number of states in the MLSD receiver. Finally, the energy-bandwidth performance of CPM using time-limited PSP is investigated and compared with conventional CPM with time-unlimited PSP. From numerical results we show that, under the same number of states in the MLSD receiver and bandwidth occupancy, this subclass of CPM could outperform the conventional CPM up to 6dB coding gain, for h<1, in many cases.

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, 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.

In this letter, we present a novel cooperative spectrum sensing scheme for cognitive radio systems. The proposed approach is based on a consensus algorithm. Using the received signals, we set up a formula for the consensus algorithm, which guarantees a convergence to an agreement value. The simulation results exhibit that the performance of the consensus-based cooperative scheme is much better than that of the conventional cooperative technique in the case that the cooperative nodes for spectrum sensing are sparsely distributed in cognitive radio systems.

In the recently developed Flexible Wireless System (FWS), the same platform needs to deal with different wireless systems. This increases nonlinear distortion in its wideband power amplifier (PA) because the PA needs to concurrently amplify multi-band signals. By taking higher harmonics as well as inter- and cross-modulation distortion into consideration, we have developed a method to analytically evaluate the adjacent channel leakage power ratio (ACPR) and error vector magnitude (EVM) on the basis of the PA's nonlinear characteristics. We devise a novel method for modeling the PA amplifying dual-band signals. The method makes it possible to model it merely by performing a one-tone test, making use of the Volterra series expansion and the general Wiener model. We then use the Mehler formula to derive the closed-form expressions of the PA's output power spectral density (PSD), ACPR, and EVM. The derivations are based on the assumption that the transmitted signals are complex Gaussian distributed in orthogonal frequency division multiplexing (OFDM) transmission systems. We validate the method by comparing measurement and simulation results and confirm it can appropriately predict the ACPR and EVM performance of the nonlinear PA output with OFDM inputs. In short, the method enables correct modeling of a wideband PA that amplifies dual-band signals merely by conducting a one-tone test.