The space–air–ground-integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS)-assisted AmBC system. In the single-backscatter device (BD) AmBC scenario, we first give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on the iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

Thorough respiratory assessments involving auscultation of breath sounds are essential for healthcare providers to accurately diagnose disease. However, parents and caregivers may lack the essential knowledge or skills to discern whether or not early signs and symptoms of respiratory disease, such as alterations in breath sounds or patterns, are problematic. In this paper, we present a novel respiration recognition and classification system for children. Young children’s breath sound signals have been collected in data sets and pre-processed as the input data. A new time-varying acoustic feature is designed and collected from the data sets. The proposed novel classifiers can recognize and classify children’s breath sounds based on the features of interest. Clinical data were used to design and verify the proposed approaches. Experiments show that the proposed approaches offer accurate results.

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for the mobile broadband service with gigabyte per second data rate in the UAV-aided fifth generation (5G) IoT system. However, Doppler frequency shift (DFS) deteriorates the link performance of UAV-aided 5G system in the highly dynamic and mobile scenarios. Therefore, a data-aided DFS estimation and compensation approach is proposed to optimize the DFS estimation process using historical estimation results, aiming to achieve a fast and accurate DFS compensation. The performance of the proposed DFS estimation algorithm is evaluated by both cost function of accuracy based on frame structure and Cramer-Rao lower bound in terms of the mean-squared error and signal-to-noise ratio. Furthermore, an adaptive frequency-domain DFS compensation algorithm is designed by leveraging DFS estimation results to enhance the quality of communication link for UAV-aided 5G system, achieving an optimal tradeoff between accuracy and complexity. Finally, both link-level simulation platform and hardware testbed are designed and developed to evaluate the performance of our proposed data-aided approach over other conventional algorithms.

The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.

Nonintrusive load monitoring (NILM) is a technique that infers appliance-level energy consumption patterns and operation state changes based on feeder power signals. With the availability of fine-grained electric load profiles, there has been increasing interest in using this approach for demand-side energy management in smart grids. NILM is a multilabel classification problem due to the simultaneous operation of multiple appliances. Recently, deep learning based techniques have been shown to be a promising approach to solving this problem, but annotating the huge volume of load profile data with multiple active appliances for learning is very challenging and impractical. In this article, a new semisupervised multilabel deep learning based framework is proposed to address this problem with the goal of mitigating the reliance on large labeled datasets. Specifically, a temporal convolutional neural network is used to automatically extract high-level load signatures for individual appliances. These signatures can be efficiently used to improve the feature representation capability of the framework. Case studies conducted on two open-access NILM datasets demonstrate the effectiveness and superiority of the proposed approach.

Due to the three dimensional mobility advantage of unmanned aerial vehicles (UAVs), a swarm of UAVs can be conveniently deployed and work cooperatively to enlarge the coverage and relay data for environment monitoring and emergency communication scenarios. However, the high mobility of UAV brings new challenges to guarantee the robust and efficient routing in a dynamic changing network topology. Therefore, a mobility prediction based virtual routing (MPVR) algorithm is proposed to enhance the performances of connectivity and routing lifetime among cooperative UAVs. And the probability density function of UAV movements is theoretically achieved using the Gaussian distribution model. Moreover, an optimal virtual routing model is designed to select the optimal relay node with the minimum distance between UAVs. Finally, our proposed MPVR algorithm is evaluated by simulation results, which can improve the average routing lifetime, the average link delay, and the packet delivery ratio compared to other conventional algorithms.

One of the most common reasons for children consulting a general practitioner is respiratory morbidity. For healthcare providers, listening to breath sounds is an important diagnose method for respiration system diseases. However, parents and caregivers dont always have the required knowledge and experience to identify children's various breath sounds. Also, it is extremely hard to obtain feedback from young children about their physical conditions. Therefore, it is necessary to provide a tool to monitor young children healthy condition. In this paper, we propose novel approaches to recognize and classify children's breath sounds. We obtain and analyze audio features of young children's breath sound signals in time and frequency domains, based on these features, classify breath signals to specific breath segments. Nearest neighborhood (NN) and artificial neural network (ANN) were used for pattern recognition and classification. Clinical data were used to design and verify the proposed approaches. Experiments show that the proposed approaches offer accurate results.

To address the issue of multi-scale detection, current detectors usually generate hierarchical feature pyramid by a naive combination of top-down features with lateral features. Due to the limited effective receptive fields in the top-down pathway, the generated regions are only associated with the neighbor regions of the coarse feature maps, which is harmful for pyramidal feature generation. And considering the weak representation of simply merging of top-down and lateral pathways, the pyramidal feature maps with strong semantics are difficult to obtain. In this paper, we present the Residual Dilation based Feature Pyramid Network (RDFPN) to exploit the inherent correlation of regions in feature pyramid. The goal of RDFPN is to produce more appropriate hierarchical feature maps for multi-scale detection. With Residual-50 network in Faster R-CNN framework, RDFPN outperforms the original FPN on the challenging COCO dataset without bells and whistles.

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

Indoor fingerprint location is widely used in passenger flow analysis, location based service, and security monitoring. RSS is usually used as signal fingerprint. However, in these applications the acquisition process relies on the uplink data transmission of the target device. These signals are often non-normally distributed and fluctuate greatly when the target moves. In order to cope with the above difficulties, this paper proposes a hidden markov model (HMM) framework based on targets maximum speed. It combines the speed parameters with path constraints to improve the calculation of state transition matrix. The kernel density estimation method based on the Wiener process is also used to solve the confusion matrix of HMM, which improves the accuracy of estimating the RSS distribution of the uplink signal. The performance of the algorithm are compared with the traditional HMM and NB methods in an actual indoor scenario. The influence of the target moving speed and the length of the observation sequence on the positioning accuracy is also analyzed.

Feature selection (FS) is a key factor for the performance of machine learning algorithms, as not all data and hence features are related to the various tasks. In this paper, we propose a novel scheme for convolutional FS for machine learning algorithms in computer vision. As not all the convolutional features are related to visual tracking, removing the unrelated ones will dramatically reduce the complexity and improve the algorithm performance. However, how to identify and select features related to the visual tracking task is still a challenge for machine learning algorithms. In the proposed scheme, a novel adaptive weights-objective function approach is established to evaluate and select the features. Furthermore, a quadratic programming method is introduced which improves the optimization efficiency. The experimental results demonstrate that our proposed scheme achieves superior performance compared to the state-of-art trackers on the challenging benchmarks in computer vision.

In face of the explosive service demands, solving the problem of spectrum scarcity is becoming more important than ever. To utilize the spectrum resources more thoroughly and efficiently, virtualization technologies have been proposed, which can be a means to mitigating resource granularity and increasing efficiency in heterogeneous network environments. In this paper, a traffic-oriented resource virtualization with demand-supply dynamic analysis is proposed for optimized resource allocation of heterogeneous networks with multiple types of services. On the supply side, i.e., the network side, a low-complexity matching game approach is introduced with the novel “Match-Degree” conception, which could be defined with the Grey relational analysis. The complexity of generating preference list can be reduced by unifying various dimensions of network parameters. On the demand side, i.e., the user side, bandwidth allocation algorithm is designed to consider the comprehensive network traffic characteristics, energy consumption, and network price factors, to maximize the overall utility. Except from theoretic analysis, simulation has also been employed to compare the proposed scheme with prior and traditional ones. To further verify the practicability, tractability, and effectiveness of the proposed demand-supply scheme, a test bed is designed and developed in this paper.

In recent years, to meet the increasing demandsfor vehicular communications, cognitive radio(CR) technology have been used to make the communication more effectively. In this paper, we propose a spectrum sensing channel model based on cooperative interference and interleaved channel which consists of two stages transceiver in vehicular network. This model makes use of CR technology with switching matrix to build a compressed channel between primary user(PU) and secondary user(SU). Additionally, we investigate and analyze factors consist of compression ratio, detection probability, false alarm rate, PU occupancy probability and power control parameters which effect channel capacity significantly by simulation and the experimental results show good performances to validate our method.

Most Wi-Fi based localization algorithms are cooperative as user device is required to associate with an AP. However, user may not associate with AP in scenarios such as supermarkets which calls for non-cooperative localization. In this paper, the probe request (PR) frame sent by device is analyzed and the weighted kernel density estimation assisted Bayes (w-KAB) algorithm is utilized for localization. The PR frame is sent in a sparse manner in time and the probability distribution of its receiving signal strength is complicated due to channel misalignment. Therefore kernel density estimation is adopted in the training stage to estimate the distribution of signal strength accurately with a limited amount of training data. In the localization stage, a weighted naive Bayes algorithm is used to estimate the location of user. Experiments are also conducted using off the shelf devices to validate the performance of the proposed algorithm.

As traditional spectrum sensing approaches unable to deal with the contradiction between detection accuracy and complexity in cognitive radio network, a novel q-weighed sequential cooperative energy detection method for spectrum sensing in time varying channel is proposed in this paper to achieve better performance with lower complexity. By adding the q- weighted log likelihood ratio (LLR) of the past local observations from previous sensing slots to the current LLR sequentially, cognitive radio nodes can aggregate the current and previous received energy values to yield the improvement of sensing performance. Moreover, we pose a q-weighted K-out of-N voting rule at the fusion center to minimize the total error probability. For different probability of primary signal for turning its state from active to idle, we employ corresponding different weighted value q to make the sensing scheme more flexible and efficient.

Pedestrian detection systems are receiving increasing attention in both industry and academia with the rapid development of autonomous automobiles which employ artificial intelligence. These systems must detect specific classes of objects such as pedestrians rather than generic objects. In this paper, we present a faster RCNN based pedestrian detection system which improves upon previous solutions. The proposed model takes arbitrary size images as inputs and generates bounding boxes and confidence scores for pedestrians. The system achieves good performance and is faster than the well known and frequently used methods in the literature.

Automatic modulation classification (AMC) conducted by a single receiver plays a crucial role in spectrum monitoring and signal interception. To improve the accuracy of feature based AMC, a novel multi-cumulant based modulation classification scheme using uniform linear array is proposed in this paper. Moreover, two methods are formulated to combine the signal from different antenna branches, i.e., DOAC (Direction of arrival estimation based Combination) and CC (Cooperative Combination). With an estimate of the incident angle of the signal, DOAC combines signals from different branches using maximum ratio combining. CC calculates the feature value of each branch independently and utilizes the average feature value of all branches for classification. Simulation results prove that using multiple cumulants yields performance gain over traditional methods using a single cumulant. Moreover, the influence of antenna number and sample length on performance is also explored.

Recent studies suggest nodes in practical networks are more likely to communicate with nearby nodes than far away nodes, which is referred to as proximity preference. In this paper, we model proximity preference by assuming the probability of communication follows a power law distribution with respect to distance and analyze its influence on the throughput of a hybrid network. Moreover, L-maximum-hop routing strategy is adopted to enforce delay constraints. Throughput is derived as a function of maximum hop L, proximity preference index α and the number of base stations (BSs) m. It is also found that per-node throughput changes with α. When 0 ≤ α ≤ 2, proximity has no influence on throughput. When 2 ≤ α ≤ 3, the throughput increases with α. Otherwise, the throughput reaches its maximum and remains constant. Our results demonstrate the interplay of various networks parameters with proximity preference and provide guidelines for the design of practical networks.

Abstract For the problem of spectrum scarcity and wastage, cognitive radio (CR) technology provides a solution to utilizing the vacant spectrum more efficiently. As one of the most promising techniques to obtain the cognitive information in TV white spaces, geo‐location database approach has attracted a lot of recent attentions, with its goal of enhancing the efficiency of spectrum usage and avoiding the interference to TV receivers. However, existing works mainly focus on the construction and applications of geo‐location database, and seldom consider how to deliver the cognitive information from the database to TV band devices. In this paper, we investigate the tradeoff between increasing the accuracy of cognitive information delivery and reducing the overhead. We design two mesh fusion algorithms to reduce the redundancy of cognitive information and improve the efficiency of cognitive information delivery. Finally, we verify our analysis and evaluate the efficiency of the proposed mesh fusion algorithms through numerical studies. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we study the relation between network capacity and the density of micro base stations in heterogeneous networks (HetNets) scenario consisting of macro base station (MaBS) and micro base station (MiBS) tiers. First, the distribution of the distance between a typical user and its serving base station (BS) is derived in a stochastic geometry model. Assuming users access the BS with the strongest received signal, we obtain the probability of users' association with the MiBS tier as a function of MiBS density. Then the impact of BS density on the interference inside the MiBS tier is also achieved in closed form. Finally, we derive the closed form solution of the network capacity as a function of BS density. We find that although there are more available channels with higher MiBS density, the rate of each channel is degraded because of stronger interference. Therefore the problem of maximizing network capacity with respect to MiBS density is formulated and the optimal MiBS density is obtained. Simulations are provided to verify the correctness of our analysis. One interesting finding is that network capacity doesn't increase monotonously with BS density. Thus deploying more MiBS may not always be a good choice1.

The heterogeneous smart grid (SG) poses two major challenges for wireless networks, namely, providing sufficient bandwidth for a wide variety of applications and high reliability for critical real-time applications. To address these challenges, the impact of communication outage on the demand response management as a typical SG application is analyzed in this paper. A dynamic spectrum management (DSM) technique is proposed to allocate resources, considering the QoS and application priorities. Vacant digital TV frequency bands are utilized to support SG applications. An algorithm to estimate the SG capacity is introduced, which can be applied to various user distributions and SG environments. This is used in conjunction with a low-complexity coloring theory algorithm to allocate the spectrum. The results presented show that DSM provides better performance than traditional fixed spectrum management, in terms of QoS and secondary spectrum utilization.

Traditional cellular network standards require users downlink(DL) and uplink(UL) associated to the same BS. However, due to the power gap between BSs and users and different transmission environment(e.g. the path gain) of DL and UL, a serving BS with the strongest downlink would yield to a non-serving BS in the uplink. In this paper we propose the DL/UL splitting user association framework under a two-tier heterogeneous network, in which femtocell base stations are randomly located over a macro base station in a specific area. Downlink and Uplink network ergodic throughput are analysed according to our DL/UL splitting association model. Simulation results show a great throughput promotion compared with the traditional DL/UL coupled association manner. We believe downlink and uplink splitting as a flexible connection mode between users and base stations would be an essential approach to realize the high capacity requirements, which is a evolution of the network connection art.

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area has been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is equation. In this paper, we show that by exploiting directional spectrum opportunities in secondary networks, the SU throughput can be improved. If the main lobe of the SU antenna pattern can be as narrow as possible, then the SUs can achieve a per-node throughput of equation which is Θ(log n) times higher than the the throughput without directional transmission. If we consider practical constraints and assume the minimum angle of the main lobe is δth, then the SU throughput gain is equation compared with the throughput without directional transmission. We also explore the statistics of directional spectrum holes in this paper.

This paper considers a heterogeneous communication network consisting of a Smart Grid Neighborhood Area Network (SG-NAN) and a Digital Video Broadcasting (DVB) system. A Dynamic Spectrum Management (DSM) technique is proposed for this network to allocate resources to the SG-NAN and DVB systems to satisfy SG-NAN QoS requirements while limiting interference to the DVB system. A novel algorithm for estimating SG-NAN capacity is introduced which can be applied to various user distributions and hybrid service situations. The DVB capacity is also calculated under interference constraints. Based on these capacities, spectrum is allocated using a coloring theory algorithm to reduce the complexity. Simulation results are presented which show that DSM provides better performance than traditional fixed spectrum management (FSM) in terms of QoS and secondary spectrum utilization.

Efficient spectrum utilization is of great importance in cognitive networks, however current spectrum sharing techniques in the temporal or spatial domains all have deficiencies. In this paper, we propose a joint spatial and temporal spectrum sharing scheme based on the concept of regions. We define four regions, namely a primary exclusive region (PER), a temporal spectrum sharing region (T-SSR), a joint spectrum sharing region (J-SSR), and a spatial spectrum sharing region (S-SSR). TSSR and J-SSR are proposed to utilize temporal spectrum holes which have not previously been exploited. Cooperative spectrum sensing is employed to expand the J-SSR, which is beneficial for system design. Closed-form bounds for the four regions are obtained, and conditions on T-SSR existence and the presence of a transition zone between the JSSR and S-SSR are determined. Both analytical and simulation results are presented which show how the key factors, including primary user interference constraints, spectrum sensing factors, and secondary user coverage constraints, influence these bounds. This has great practical value in improving spectrum efficiency in cognitive networks.

In this paper, we investigate the connectivity of random cognitive radio networks with different routing schemes. Two coexisting ad hoc networks are considered with m primary users (PUs) and n secondary users (SUs) randomly distributed in a unit area. The relation between n and m is assumed to be n = mβ. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when β > 1, and the connectivity of a single secondary path can be guaranteed when β > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network (CRAHN), we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when β > 1. Thus to achieve the connectivity of secondary networks, the density of SUs should be larger (asymptotically) than that of the PUs. A smart routing scheme can also improve the connectivity of CRAHN. Our results serve as a guide to deployment and routing design for cognitive radio networks.

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.

In order to improve the spectrum utilization in cognitive radio networks, the spectrum holes in space-time-frequency multiple dimensions should be exploited accurately and efficiently. Therefore, a novel three region scheme, which includes the black region, grey region and white region, has been designed and proposed with one primary transmitter at the center, taking into account key interference factors from secondary users (SUs) and the miss detection and false alarm probabilities in spectrum sensing. Between the black region where only primary users (PUs) have exclusive right to use the spectrum and the white region where SUs can utilize the same spectrum without causing severe interference to PUs, the grey region has been designed, which has temporal spectrum access opportunities in time dimension once neglected by existing works. Moreover, the condition of the existence of a transition zone between grey region and white region is analyzed with theoretical results, where power control should be applied to SUs. The closed-form bounds of three regions are obtained, which can be used in the space-time spectrum sensing and access in cognitive radio networks.

Heterogeneous cellular networks have emerged as a new paradigm in the wireless network to increase cellular capacity and coverage. Future heterogeneous networks will have a mixed deployment of high power and low power nodes. This creates complicated interference scenarios that require more tight resource coordination among neighbouring nodes than in the traditional cellular networks. In this paper we consider a heterogeneous LTE relay network and investigate two downlink resource coordination schemes in the time/frequency/power domains in such a network. We perform detailed simulation study on the two proposed schemes, and compare them with a third scheme that does not have frequency or power coordination. Our study shows that by allowing tight time/frequency/power coordination, the system can achieve much higher system capacity and better received downlink signal quality.

Fitting noisy measurements to a circle is a classic statistical estimation problem. In this paper, we make two contributions to the study of this problem. First, we propose a novel formulation of the maximum likelihood (ML) estimator for identifying the center and radius of the circle from noisy measurements. This new estimator uses the unknown true values of the measurement points as the nuisance parameter to obtain an exact ML formulation. We then examine the Karush-Kuhn-Tucker (KKT) conditions for the optimum solution to the ML estimator. We show analytically that this new estimator is in fact equivalent to the well-known least squares (LS) form of the circle fitting problem. Second, from the insights gained in deriving the optimum solution, a computationally simple circle fitting algorithm based on greedy search is proposed. Performance results are given to illustrate the performance of the proposed algorithm.

The Internet of Things (IoT) is a new concept that refers to an Internet connecting not just computer systems but a plethora of systems, devices, and objects, collectively referred to as “Things”, and encompasses technologies for identification and tracking, sensing and actuation, both wired and wireless communications, and also, intelligence and cognition. Wireless communications, which is an integral part of IoT, suffers from radio irregularity - a phenomenon referring to radio waves being selectively absorbed, reflected or scattered by objects in their paths, e.g., human bodies that comprises liquid, bone and flesh. Radio irregularity is often regarded as a problem in wireless communications but, with the envisioned pervasiveness of IoT, we aim to exploit radio irregularity as a means to detect people. We demonstrate how radio signal fluctuations arising from radio irregularity can be used to provide a low-cost alternative to dedicated sensing systems for indoor automated people counting.

Femtocell networks have great potential for mobile applications. However, interference due to the co-existence of macrocells and femtocells is a serious problem. In addition, dense femtocells introduce severe inter-system interference. In this paper, a graph theory based dynamic sub-band allocation technique is presented to avoid downlink interference. We model the cells and their mutual interference as graph elements, nodes and weighted edges, respectively. To maintain a tolerable interference level, the total bandwidth is divided into a number of sub-bands, and these are assigned to the femtocells using a graph coloring algorithm. The division is optimized to minimize the bandwidth used to meet user traffic requirements, and minimize the femto-to-macro interference. In addition, sub-bands are assigned to the femtocells to avoid inter-femto interference. An iterative spectrum management algorithm is also introduced.

This paper proposes a novel Dynamic Spectrum Management (DSM) scheme for Wideband Code Division Multiple Access (WCDMA) / Digital Video Broadcasting (DVB) heterogeneous systems. Capacity estimation algorithms for both WCDMA and DVB are developed which consider both the user distribution and characteristics of the hybrid services. Based on these algorithms, a new dynamic spectrum allocation scheme is presented which allows for optimum allocation of resources and maximum secondary spectrum usage. Coloring theory is used to significantly reduce DSM complexity while providing near-optimal performance. Numerical results are given which show that the proposed DSM scheme has better performance than Fixed Spectrum Management (FSM).

Dynamic spectrum access has become a promising technique to fully utilize the scarce spectrum resources. However, spectrum allocation schemes with high efficiency and Quality of Service (QoS) guarantee for the primary users have yet to be designed. In this paper, two novel dynamic spectrum access schemes are proposed. The proposed schemes are based on continuous-time Markov chains (CTMC), through which the interactions between primary and secondary users are explicitly modeled. The effects of sensing errors (i.e. miss-detection and false alarm) are taken into consideration. Since miss-detection may lead to collision between primary and secondary users and false alarm will leave spectrum opportunities unused, we derive the optimal access probabilities for each secondary user, so that the QoS of primary user in terms of collision probability constraint is guaranteed, and the missing spectrum opportunities caused by false alarm can be utilized by secondary users. Simulation results show that the proposed schemes can guarantee primary user''s QoS effectively. Moreover, the scheme with buffer can improve the channel occupancy remarkably.

With the increasing convergence and cooperation among wireless networks, network awareness of user equipment (UE) has become very important. A practical solution for network information delivery for UE, Cognitive Pilot Channel (CPC), has recently been proposed. It can provide UE with the necessary network information for network selection by using the public signaling channel. In this paper, a cognitive multicast CPC scheme is proposed. It can greatly improve the on-demand CPC delivery efficiency, and also reduce the time delay of information delivery. The cognitive characteristics of the proposed multicast CPC scheme can easily be adapted to different heterogeneous network architectures.

In this paper, a new technique for dynamic spectrum management of a Wideband Code Division Multiple Access (WCDMA)/Digital Video Broadcasting (DVB) heterogeneous system is introduced. The solution is obtained using coloring theory, and significantly reduces the complexity of spectrum management.

Wireless communication provides network access free of the limitations of wired networks. However, some emerging applications with high bandwidth requirements and delay constraints, such as real-time video-on-demand and IPTV, suffer poor performance due to the high compression rate of video frames and/or high packet loss rate in the wireless access networks. In this paper, we propose a novel optimization algorithm referred to as the network state dependent video compression rate (NSDVCR) algorithm, which determines the compression rates depending on the video characteristics and the network condition. The proposed NSDVCR algorithm is able to achieve optimal video transmission rate for a given network state characterized by packet loss rate. The simulation results of the proposed mechanism show that significant improvement of the video quality measured in terms of peak-signal-to-noise ratio (PSNR), is achieved compared with standard compression mechanisms.

Millimeter wave wireless communications are opening the way towards high data rate applications for WPAN scenarios. Multi-Gbps wireless systems are currently defined by standardization bodies. This paper presents a complete 60 GHz OFDM PHY layer proposal fulfilling the requirements of the IEEE 802.15 Task Group 3c. Data rates are ranging from 335 Mbps to 3 Gbps, with a high level of scalability. The proposed system is designed to operate in the 57 to 66 GHz frequency band, with a possibility to use channel sizes of 1 GHz and 2 GHz. The OFDM PHY layer allows scalable spectrum efficiency to achieve high data rates with flexible radio coverage. For a nominal channel bandwidth of 1 GHz, the system uses an FFT size of 1024, convolutional coding and advanced interleaving schemes. Simulation results show that this system is well suited for very high data rate applications in realistic scenarios.

In this paper, we study the problem of optimal pulse shaping for pulse position modulation (PPM) ultra-wideband (UWB) systems with a recently proposed sparsity-driven signal detection method. This signal detection method offers superior performance over traditional matched filter based detection through the representation of additive white Gaussian noise (AWGN) and the UWB pulses via atoms from a Hadamard Walsh matrix and a pulse constellation dictionary, respectively. Recognizing the possibility to further improve the performance with sparsity-driven signal detection through shaping the UWB pulses to be dissimilar to AWGN, we formulate an optimal pulse shaping problem considering the federal communication commission (FCC) emission mask. We also develop a relaxation method to approximate the objective function, and solve the relaxed problem with classical nonlinear programming. Design examples are given to show the resulting pulse shape, its dissimilarity to the channel noise, and its compliance with the mandatory FCC emission mask.

Ultra-Wideband (UWB) transmission is a promising technology for high speed wireless networks such as wireless personal area networks (WPANs). The detection of UWB signals is a key issue for system performance and capacity. In this paper, we propose a novel signal detection technique for multiple access UWB systems. Our approach is based on an overcomplete dictionary signal representation as well as the concept of successive interference cancellation for multi-user detection. We introduce the algorithm for multiple access UWB signal detection, and present guidelines to generate overcomplete dictionaries for the detection of signals in noise. The proposed detection algorithm for multiple access UWB systems can eliminate noise as well as multi-user interference (MUI). Signal detection is achieved by solving an l1norm minimization problem, which can be calculated using low complexity linear programming algorithms.

Ultra-wideband (UWB) transmission is a promising technology for future high speed wireless networks. In this paper, we propose a novel signal detection approach with noise suppression for UWB systems. This approach is based on an overcomplete dictionary signal representation and a sparsity-driven optimization algorithm. We introduce general approaches for UWB signal detection, and present guidelines to generate overcomplete dictionaries for signals in noise. UWB signal detection is achieved by solving an l1norm minimization problem, which can be solved using low complexity linear programming algorithms. Examples are given to demonstrate the performance of the proposed approach

In this letter, system capacity and error probability of orthogonal space-time block coding (STBC) is considered for PAM/PSK/QAM modulation in correlated Nakagami fading channels. The approach is based on an equivalent scalar AWGN (additive white Gaussian noise) channel with a channel gain proportional to the Frobenius norm of the matrix channel for the STBC. Closed form capacity and error probability expressions are derived for Nakagami fading channels. Numerical results are given to illustrate the theory.

Recently, pulse position amplitude modulation (PPAM) has been proposed for ultra-wideband (UWB) communication systems. PPAM combines pulse position modulation (PPM) and pulse amplitude modulation (PAM) to provide better performance and higher system capacity with low computational complexity. A RAKE receiver can make use of the rich multipath of UWB systems to improve system performance and capacity. In this paper we present the performance and capacity of a time hopping (TH) UWB system with PPAM modulation and RAKE reception over a practical multipath fading channel. Error probability and a union bound on performance, single user capacity and a capacity/distance bound based on FCC Part 15 rules, are derived. This analysis is extended to the multiple access case. Numerical results are given to illustrate the analysis.

In this paper we consider the performance of a RAKE receiver in a code division multiple access (CDMA) system employing maximal-ratio combining. A simple select and combine algorithm is introduced in which the RAKE receiver combines only the synchronization path and those resolvable paths with a signal-to-noise ratio (SNR) larger than a given threshold. We analyze its performance based on moment generating functions of the SNR of the combined signal. We study the cases with equal and unequal average SNR for different diversity paths. In particular, closed-form expressions for the average combined SNR and symbol error probability with the proposed RAKE receiver over block fading channels are derived. We also study the outage probability of the system and the number of channels estimated. Numerical results are given to illustrate the analytical results.

In this paper, the error probability of orthogonal space-time block coding (STBC) is considered in correlated flat fading channels. The approach is based on an equivalent scalar AWGN (additive white Gaussian noise) channel. Closed form error probability expressions are derived for PSK/PAM/QAM on correlated fading channels.

In this paper, the capacity of orthogonal space-time block coding (STBC) is considered for PAM/PSK/QAM modulation in correlated flat fading channels. The approach is based on an equivalent scalar AWGN (additive white Gaussian noise) channel with a channel gain proportional to the Frobenius norm of the matrix channel for STBC.

In this letter, we propose a new modulation scheme called pulse position amplitude modulation (PPAM) for ultra-wideband (UWB) communication systems. PPAM combines pulse position modulation and pulse amplitude modulation to provide good system performance and low computational complexity. The channel capacity of PPAM is determined for a time-hopping multiple-access UWB communication system. The error probability and performance bounds are derived for a multiuser environment.