Aperture synthesis technology represents an effective approach to millimeter-wave radiometers for high-resolution observations. However, the application of synthetic aperture imaging radiometer (SAIR) is limited by its large number of antennas, receivers and correlators, which may increase noise and cause the image distortion. To solve those problems, this letter proposes a compressive regularization imaging algorithm, called CRIA, to reconstruct images accurately via combining the sparsity and the energy functional of target space. With randomly selected visibility samples, CRIA employs l1 norm to reconstruct the target brightness temperature and l2 norm to estimate the energy functional of it simultaneously. Comparisons with other algorithms show that CRIA provides higher quality target brightness temperature images at a lower data level.

Heterogeneous Small Cell Network (HSCN) will have wide application given its ability to improve system capacity and hot spot coverage. In order to increase the efficiency of spectrum and energy, a great deal of research has been carried out on radio resource management in HSCN. However, it is a remarkable fact that the user experience in terms of traffic rate demands has been neglected in existing research with excessive concentration on network capacity and energy efficiency. In this paper, we redefined the energy efficiency (EE) and formulate the joint optimization problem of user experience and energy efficiency maximization into a mixed integer non-linear programming (MINLP) problem. After reformulating the optimization problem, the joint subchannel (SC) allocation and power control algorithm is proposed with the help of cluster method and genetic algorithm. Simulation results show that the joint SC allocation and power control algorithm proposed has better performance in terms of user experience and energy consumption than existing algorithms.

Effective emergency resource allocation is essential to guarantee a successful emergency disposal, and it has become a research focus in the area of emergency management. Emergency event logs are accumulated in modern emergency management systems and can be analyzed to support effective resource allocation. This paper proposes a novel approach for efficient emergency resource allocation by mining emergency event logs. More specifically, an emergency event log with various attributes, e.g., emergency task name, emergency resource type (reusable and consumable ones), required resource amount, and timestamps, is first formalized. Then, a novel algorithm is presented to discover emergency response process models, represented as an extension of Petri net with resource and time elements, from emergency event logs. Next, based on the discovered emergency response process models, the minimum resource requirements for both reusable and consumable resources are obtained, and two resource allocation strategies, i.e., the Shortest Execution Time (SET) strategy and the Least Resource Consumption (LRC) strategy, are proposed to support efficient emergency resource allocation decision-making. Finally, a chlorine tank explosion emergency case study is used to demonstrate the applicability and effectiveness of the proposed resource allocation approach.

To guarantee QoS for multicast transmission, admission control for multicast sessions is expected. Probe-based multicast admission control (PBMAC) scheme is a scalable and simple approach. However, PBMAC suffers from the subsequent request problem which can significantly reduce the maximum number of multicast sessions that a network can admit. In this letter, we describe the subsequent request problem and propose an enhanced PBMAC scheme to solve this problem. The enhanced scheme makes use of complementary probing and remarking which require only minor modification to the original scheme. By using a fluid-based analytical model, we are able to prove that the enhanced scheme can always admit a higher number of multicast sessions. Furthermore, we present validation of the analytical model using packet based simulation.

This letter proposes a simple and efficient random-binning based distributed source coding (DSC) scheme for application to remote source estimation in wireless sensor networks. The scheme jointly encodes data from multiple sensors with side information. It achieves high coding efficiency and reduces power and bandwidth consumption.

Limited energy is a big challenge for large scale wireless sensor networks (WSN). Previous research works show that modulation scaling is an efficient technique to reduce energy consumption. However, the impacts of using modulation scaling on packet delivery latency and loss are not considered, which may have adverse effects on the application qualities. In this paper, we study this problem and propose control schemes to minimize energy consumption while ensuring application qualities. We first analyze the relationships of modulation scaling and energy consumption, end-to-end delivery latency and packet loss ratio. With the analytical model, we develop a centralized control scheme to adaptively adjust the modulation levels, in order to minimize energy consumption and ensure the application qualities. To improve the scalability of the centralized control scheme, we also propose a distributed control scheme. In this scheme, the sink will send the differences between the required and measured application qualities to the sensors. The sensors will update their modulation levels with the local information and feedback from the sink. Experimental results show the effectiveness of energy saving and QoS guarantee of the control schemes. The control schemes can adapt efficiently to the time-varying requirements on application qualities.

This paper considers the problem of providing relative service differentiation in IEEE 802.11 Wireless LAN by using different Medium Access Control (MAC) parameters for different service classes. We present an analytical model which predicts the saturation throughput of IEEE 802.11 Distributed Coordination Function with multiple classes of service. This model allows us to show that relative service differentiation can be achieved by varying the initial contention window alone. In this case, the saturation throughput of a station can be shown to be approximately inversely proportional to the initial contention window size being used by that station. The simulation results validate our analytical model.