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Abstract-In long term evolution-advanced (LTE-A) networks, the carrier aggregation technique is incorporated for user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs) for achieving higher transmission rate. Many research works for LTE-A systems with carrier aggregation configuration have concentrated on the radio resource management problem for downlink transmission, including mainly CC assignment and packet scheduling. Most previous studies have not considered that the assigned CCs in each UE can be changed. Furthermore, they also have not considered the modulation and coding scheme constraint, as specified in LTE-A standards. Therefore, their proposed schemes may limit the radio resource usage and are not compatible with LTE-A systems. In this paper, we assume that the scheduler can reassign CCs to each UE at each transmission time interval and formulate the downlink radio resource scheduling problem under the modulation and coding scheme constraint, which is proved to be NP-hard. Then, a novel greedy-based scheme is proposed to maximize the system throughput while maintaining proportional fairness of radio resource allocation among all UEs. We show that this scheme can guarantee at least half of the performance of the optimal solution. Simulation results show that our proposed scheme outperforms the schemes in previous studies.

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In comparison with individual testing, group testing is more efficient in reducing the number of tests and potentially leading to tremendous cost reduction. There are two key elements in a group testing technique: (i) the pooling matrix that directs samples to be pooled into groups, and (ii) the decoding algorithm that uses the group test results to reconstruct the status of each sample. In this paper, we propose a new family of pooling matrices from packing the pencil of lines (PPoL) in a finite projective plane. We compare their performance with various pooling matrices proposed in the literature, including 2Dpooling, P-BEST, and Tapestry, using the two-stage definite defectives (DD) decoding algorithm. By conducting extensive simulations for a range of prevalence rates up to 5%, our numerical results show that there is no pooling matrix with the lowest relative cost in the whole range of the prevalence rates. To optimize the performance, one should choose the right pooling matrix, depending on the prevalence rate. The family of PPoL matrices can dynamically adjust their construction parameters according to the prevalence rates and could be a better alternative than using a fixed pooling matrix.

Abstract-Current traffic engineering in SDN mostly focuses on unicast. By contrast, compared with individual unicast, multicast can effectively reduce network resources consumption to serve multiple clients jointly. Since many important applications require reliable transmissions, it is envisaged that reliable multicast plays a crucial role when an SDN operator plans to provide multicast services. However, the shortest-path tree (SPT) adopted in current Internet is not bandwidth-efficient, while the Steiner tree (ST) in Graph Theory is not designed to support reliable transmissions since the selection of recovery nodes is not examined. In this paper, therefore, we propose a new reliable multicast tree for SDN, named Recover-aware Steiner Tree (RST). The goal of RST is to minimize both tree and recovery costs, while finding an RST is very challenging. We prove that the RST problem is NPHard and inapproximable within k, which is the number of destination nodes. Thus, we design an approximate algorithm, called Recover Aware Edge Reduction Algorithm (RAERA), to solve the problem. The simulation results on real networks and large synthetic networks, together with the experiment on our SDN testbed with real YouTube traffic, all manifest that RST outperforms both SPT and ST. Also, the implementation of RAERA in SDN controllers shows that an RST can be returned within a few seconds and thereby is practical for SDN networks.

Abstract-The wireless sensor network is an emerging technology that may greatly facilitate human life by providing ubiquitous sensing, computing, and communication capability. However, to maintain sufficient coverage and to achieve long system lifetime are two contradicting factors in designing the topology of a sensor network. In this paper, we propose several decentralized protocols that schedule sensors' active and sleeping periods to prolong the network lifetime while maintain the sensing field sufficiently covered. The proposed protocols are based on a model similar to that of [8], but improve the results of [8] in several senses. First, our approach can significantly reduce the computational complexity incurred, and at the same time achieve better accuracy in determining the coverage of the sensing area. Second, we further enhance the results of [8] by proposing an optimization mechanism to further balance or reduce sensors' energy expenditure.

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