The issue of robust multi-input multi-output (MIMO) radar waveform design is investigated in the presence of imperfect clutter prior knowledge to improve the worst-case detection performance of space-time adaptive processing (STAP). Robust design is needed because waveform design is often sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, a robust waveform covariance matrix (WCM) design is formulated in this work with the criterion of maximization of the worst-case output signal-interference-noise-ratio (SINR) under the constraint of the initial parameter estimation errors to ease this sensitivity systematically and thus improve the robustness of the detection performance to the uncertainties in the initial parameter estimates. To tackle the resultant complicated and nonlinear robust waveform optimization issue, a new diagonal loading (DL) based iterative approach is developed, in which the inner and outer optimization problems can be relaxed to convex problems by using DL method, and hence both of them can be solved very effectively. As compared to the non-robust method and uncorrelated waveforms, numerical simulations show that the proposed method can improve the robustness of the detection performance of STAP.

In conventional multiview video systems using progressive download, a user downloads videos of all viewpoints of one content to realize smooth view switching. This, however, increases the video traffic, and if the available download rate is low, the video quality suffers. Downloading only the desired viewpoint is one approach for reducing the traffic. However, in this case, playback stalls will occur after view switching. These stalls degrade the user's satisfaction for the application. In this paper, we aim at two objectives: 1) to achieve reduction in video traffic and 2) to minimize the number of playback stalls. To this end, we propose a new multiview video delivery scheme for progressive download. The main idea of the proposed scheme is that the user downloads a part of viewpoints only, which will be played back by the user with a high probability, to realize both traffic reduction and smooth view switching. In addition, we propose two download-scheduling algorithms to prevent playback stalls even at low download rates. The first algorithm prevents stalls in the cases with frequent view switching, such as zapping, while the second prevents stalls in gazing cases. Evaluations using a Joint Multiview Video Coding (JMVC) encoder and multiview video sequences show that our scheme achieves not only reduced video traffic but also decreased number of playback stalls, regardless of the user's view-switching model or download rate. In addition, we demonstrate that the proposed method does not cause playback stalls irrespective of high and low motion video contents.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms.

Network Functions Virtualization (NFV) is expected to provide network systems that offer significantly lower cost and greatly flexibility to network service providers and their users. Unfortunately, it is extremely difficult to implement Virtualized Network Functions (VNFs) that can equal the performance of Physical Network Functions. To realize NFV systems that have adequate performance, it is critical to accurately grasp VNF workload. In this paper, we focus on the virtual firewall as a representative VNF. The workload of the virtual firewall is mostly determined by firewall rule processing and the Access Control List (ACL) configurations. Therefore, we first reveal the major factors influencing the workload of the virtual firewall and some issues of monitoring CPU load as a traditional way of understanding the workload of virtual firewalls through preliminary experiments. Additionally, we propose a new workload metric for the virtual firewall that is derived by mathematical models of the firewall workload in consideration of the packet processing in each rule and the ACL configurations. Furthermore, we show the effectiveness of the proposed workload metric through various experiments.

This paper presents a novel circuit for impedance matching to a load moving along a transmission line. This system is called FERMAT: Far-End Reactor MATching. The FERMAT consists of a power transmission line and a variable reactor at its far-end. The proposed system moves standing-wave antinodes to the position of the vehicle in motion. Therefore, the moving vehicle can be fed well at any position on the line. As a theoretical result, we derive adjustable matching conditions in FERMAT. We verified that the experimental result well agrees with the theory.

In this paper, we propose a low-complexity widely-linear minimum mean square error (WL-MMSE) signal detection based on the Chebyshev polynomials accelerated symmetric successive over relaxation (SSORcheb) algorithm for uplink (UL) over-loaded large-scale multiple-input multiple-output (MIMO) systems. The technique of utilizing Chebyshev acceleration not only speeds up the convergence rate significantly, and maximizes the data throughput, but also reduces the cost. By utilizing the random matrix theory, we present good estimates for the Chebyshev acceleration parameters of the proposed signal detection in real large-scale MIMO systems. Simulation results demonstrate that the new WL-SSORcheb-MMSE detection not only outperforms the recently proposed linear iterative detection, and the optimal polynomial expansion (PE) WL-MMSE detection, but also achieves a performance close to the exact WL-MMSE detection. Additionally, the proposed detection offers superior sum rate and bit error rate (BER) performance compared to the precision MMSE detection with substantially fewer arithmetic operations in a short coherence time. Therefore, the proposed detection can satisfy the high-density and high-mobility requirements of some of the emerging wireless networks, such as, the high-mobility Internet of Things (IoT) networks.

In cellular network environments, where users are not evenly distributed across cells, overloaded base stations handling many users have difficulties in providing effective and fair services with their limited resources. Additionally, users at the cell edge may suffer from the potential problems resulting from low signal-to-interference ratio owing to the incessant interference from adjacent cells. In this paper, we propose a relay-assisted load balancing scheme to resolve these traffic imbalance. The proposed scheme can improve the performance of the overall network by utilizing relay stations to divert heavy traffic to other cells, and by adopting a partial frequency-reuse scheme to mitigate inter-cell interference. Each user and relay station calculates its own utility influence in the neighboring candidates for reassociation and decides whether to stay or move to another cell presenting the maximum total network utility increment. Simulation results show that the proposed scheme improves the overall network fairness to users by improving the performance of cell boundary users without degrading the total network throughput. We achieve a system performance gain of 16 ∼ 35% when compared with conventional schemes, while ensuring fairness among users.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

In the case of drive-by download attacks, most malicious web sites identify the software environment of the clients and change their behavior. Then we cannot always obtain sufficient information appropriate to the client organization by automatic dynamic analysis in open services. It is required to prepare for expected incidents caused by re-accessing same malicious web sites from the other client in the organization. To authors' knowledge, there is no study of utilizing analysis results of malicious web sites for digital forensic on the incident and hedging the risk of expected incident in the organization. In this paper, we propose a system for evaluating the impact of accessing malicious web sites by using the results of multi-environment analysis. Furthermore, we report the results of evaluating malicious web sites by the multi-environment analysis system, and show how to utilize analysis results for forensic analysis and risk hedge based on actual cases of analyzing malicious web sites.

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

Many-core architecture is becoming an attractive design choice in high-end embedded systems design. There are, however, many important design issues, and load balancing is one of them. In this work, we take the approach of diffusive load balancing which enables autonomic load distribution in many-core systems. We improve the existing schemes by adding the concept of simulated annealing for more effective load distribution. The modified scheme is also capable of managing a situation of non-uniform granularity of task loading, which the existing ones cannot. In addition, the suggested scheme is extended to be able to handle dependencies existing in task graphs where tasks have communications between each other. As experiments, we tried various existing schemes as well as the proposed one to map synthetic applications and real world applications on a many-core architecture with 21 cores and 4 memory tiles. For the applications without communications, the experiments show that the proposed scheme gives the best results in terms of peak load and standard deviation. For real applications such as mp3 decoder and h.263 encoder which have communications between tasks, we show the effectiveness of our communication-aware scheme for load balancing in terms of throughput.

An incident response organization such as a CSIRT contributes to preventing the spread of malware infection by analyzing compromised websites and sending abuse reports with detected URLs to webmasters. However, these abuse reports with only URLs are not sufficient to clean up the websites. In addition, it is difficult to analyze malicious websites across different client environments because these websites change behavior depending on a client environment. To expedite compromised website clean-up, it is important to provide fine-grained information such as malicious URL relations, the precise position of compromised web content, and the target range of client environments. In this paper, we propose a new method of constructing a redirection graph with context, such as which web content redirects to malicious websites. The proposed method analyzes a website in a multi-client environment to identify which client environment is exposed to threats. We evaluated our system using crawling datasets of approximately 2,000 compromised websites. The result shows that our system successfully identified malicious URL relations and compromised web content, and the number of URLs and the amount of web content to be analyzed were sufficient for incident responders by 15.0% and 0.8%, respectively. Furthermore, it can also identify the target range of client environments in 30.4% of websites and a vulnerability that has been used in malicious websites by leveraging target information. This fine-grained analysis by our system would contribute to improving the daily work of incident responders.

This paper investigates the performance of an overloaded multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) system with and without antenna selection. In the overloaded MIMO-OFDM system, even if only a small amount of feedback is available, performance can be improved by selecting the transmit antennas. Thus, this paper compares the performance of an overloaded MIMO system with and without antenna selection under different code rates. It is shown that the performance of the MIMO-OFDM system for six signal streams with QPSK modulation is about 2.0dB better than that for three signal streams with 16QAM modulation while it is about 5.0dB better than that of the MIMO-OFDM system for two signal streams with 64QAM modulation at a bit error rate (BER) of 10-3. However, it is also shown that the performance of the overloaded MIMO system is worse if the code rate of the repetition code increases.

In a real-time system, tasks are required to be completed before their deadlines. Under normal workload conditions, a scheduler with a proper scheduling policy can make all the tasks meet their deadlines. However, in practical environment, system workload may vary widely. Once system workload becomes too heavy, so that there does not exist a feasible schedule can make all the tasks meet their deadlines, we say the system is overloaded under which some tasks will miss their deadlines. To alleviate the degrees of system performance degradation caused by the missed deadline tasks, the design of scheduling is crucial. Many design objectives can be considered. In this paper, we first focus on maximizing the total number of tasks that can be completed before their deadlines. A scheduling method based on satisfiability modulo theories (SMT) is proposed. In the method, the problem of scheduling is treated as a satisfiability problem. The key work is to formalize the satisfiability problem using first-order language. After the formalization, a SMT solver (e.g., Z3, Yices) is employed to solve this satisfiability problem. An optimal schedule can be generated based on the solution model returned by the SMT solver. The correctness of this method and the optimality of the generated schedule can be verified in a straightforward manner. The time efficiency of the proposed method is demonstrated through various simulations. Moreover, in the proposed SMT-based scheduling method, we define the scheduling constraints as system constraints and target constraints. This means if we want to design scheduling to achieve other objectives, only the target constraints need to be modified. To demonstrate this advantage, we adapt the SMT-based scheduling method to other design objectives: maximizing effective processor utilization and maximizing obtained values of completed tasks. Only very little changes are needed in the adaption procedure, which means the proposed SMT-based scheduling method is flexible and sufficiently general.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.

By installing the various types of cells, imbalance in traffic load and excessive handover among cells in a heterogenous network can be prevalent. To deal with this problem, we propose a mobility-based cell association algorithm for load balancing in a heterogenous network. By defining a dynamic system load as a function of the mobility of mobile stations (MSs) and the transmit powers of cells, the proposed algorithm is designed such that it can optimize a utility function based on the fairness of the dynamic system load. Simulation results verify that the proposed algorithm improves the user perceived rate of MSs located at cell edges with slight increase in the number of handovers compared to a conventional cell association based on received signal strength.

In this paper, a novel slow-wave half mode substrate integrated waveguide (SW-HMSIW) structure is presented and experimentally demonstrated, and some interesting slow-wave propagation effects are obtained. The SW-HMSIW enables the cutoff frequency reduction and phase velocity to decrease without sacrificing its performance at the same lateral dimension, which equivalently reduces the lateral dimension and longitudinal size at the same frequency. Specifically, with the different loading microstrip width, a cutoff frequency reduction of 16%, 25%, 30% is achieved compared to the conventional HMSIW at the same lateral dimension. Both lateral and longitudinal size reductions significantly extend the operating range of SIW structures to low frequency region.

This paper presents an analytical model for network throughput of WLANs, taking into account heterogeneous conditions, namely network nodes transmit different length frames with various offered load individually. The airtime concept, which is often used in multi-hop network analyses, is firstly applied for WLAN analysis. The proposed analytical model can cover the situation that there are saturation and non-saturation nodes in the same network simultaneously, which is the first success in the WLAN analyses. This paper shows the network throughput characteristics of four scenarios. Scenario 1 considers the saturation throughputs for the case that one or two length frames are transmitted at the identical offered load. Scenarios 2 and 3 are prepared for investigating the cases that all network nodes transmit different length frames at the identical offered load and identical length frames at the different offered loads, respectively. The heterogeneous conditions for not only frame length but also offered load are investigated in Scenario 4.

A broadband miniature GaAs p-HEMT MMIC Doherty power amplifier (DPA) with a series connected load operating at the C band has been developed. To minimize the circuit size, a lumped-element load modulation circuit without a quarter wavelength transmission line has been introduced to MMIC technology. For both an input and output power divider/combiner circuit, two baluns are used to reduce the length of the phase adjuster circuit without causing instability. An inherent DPA instability problem related with the degenerated sub-harmonic frequency has been analyzed with the S and T parameters of DPA circuit components, resulting in a novel stabilized circuit. The developed stabilized DPA delivered a maximum power added efficiency (PAE) of 49% and a maximum output power of 23.4dBm. Greater than 40% PAE below a 10-dB input back-off from a saturated output power is obtained for a frequency range of 6.1 to 6.8GHz.