In this paper, we investigate the problem of medium access control in 60GHz mmWave directional wireless networks, and propose an adaptive STDMA scheduling scheme for throughput enhancement. By taking advantage of dire...
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We propose Software Defined Path (SDP) architecture based on OpenFlow (OF) technique, to achieve optical layer optimization and control flexibility. The experimental result shows the optimization and flexibility along...
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We study the transmission capacity of two overlaid wireless ad hoc networks, where a primary network and a secondary network operate in the same geographic region and share the same spectrum. The primary network has a...
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Femtocells have attracted growing attentions in academia, industry, and standardization forums in recent years. However, most of existing works on femtocell networks are focused on spectrum efficiency and interference...
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ISBN:
(纸本)9781467362351
Femtocells have attracted growing attentions in academia, industry, and standardization forums in recent years. However, most of existing works on femtocell networks are focused on spectrum efficiency and interference mitigation, energy efficiency aspect is neglected. In this paper, we investigate the maximization of energy efficiency of downlink OFDMA dense femtocell networks by efficient resource allocation. To decrease the complexity, joint subchannel allocation and power control are decomposed into two steps. Power control has been modeled as a non-cooperative game, a closed-form best response of transmit power is obtained. Considering fairness and low complexity, a fair time-averaged subchannel allocation metric have been derived out. Based on that, we propose a distributed suboptimal subchannel allocation and optimal power control algorithm. Simulation results show that the proposed algorithm has a low complexity with slight loss of energy efficiency compared with Round-Robin Scheduling and a noncooperative energy-efficient power optimization algorithm.
We investigate the energy efficiency in the downlink of a two-tier heterogeneous cellular network with quality of service consideration. By modeling the distributions of the macrocell and small cell base stations foll...
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The evolution of modern communication technologies is steadily advancing the physical layer (PHY) data rate in WLANs. However, because of the current Medium Access Control (MAC) protocols, which allocate the whole cha...
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ISBN:
(纸本)9781849197267
The evolution of modern communication technologies is steadily advancing the physical layer (PHY) data rate in WLANs. However, because of the current Medium Access Control (MAC) protocols, which allocate the whole channel as a single resource, the overhead of the MAC grows along with the PHY data rates. Ultimately, the data throughput efficiency of the system degrades. This paper puts forward a multi-channel MAC protocol, which argues that the channel should be divided into several separate sub-channels, so that multiple stations can contend for and transmit messages on different sub-channels simultaneously, thereby increasing the throughput of the system. Finally, the multi-channel MAC protocol is compared with the traditional MAC protocol by theoretical analysis and simulation, and the results show that the multi-channel MAC protocol can get a better system throughput.
The research of Hadoop is an important part of cloud computing industry, and Hadoop performance research is a key research direction. The Hadoop performance analysis as a basic work can provide important reference for...
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In cellular networks, cell zooming with the variation of the traffic load is considered as one of promising technologies for energy saving. Cells with less traffic load can be turned into sleep mode and the other cell...
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Energy conservation in Wireless Sensor networks (WSNs) has always been a crucial issue and has received increased attention in the recent years. A transmission scheme for energy-constrained WSNs is proposed in this pa...
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Energy conservation in Wireless Sensor networks (WSNs) has always been a crucial issue and has received increased attention in the recent years. A transmission scheme for energy-constrained WSNs is proposed in this paper. The scheme, called MIHOP (MIMO and Multi-hop), combines cluster-based virtual MIMO and multi-hop technologies. The multihop mode is employed in transmitting data when the related sensors are located within a specific number of hops from the sink, and the virtual MIMO mode is used in transmitting data from the remaining sensor nodes. We compare the energy consumption of different transmission schemes and propose an algorithm for determining the optimal hop count in MIHOP. A controllable mobile sink that reduces the energy consumed in sensor transmission is also adopted for data collection. The theoretical analysis and the Monte Carlo simulation demonstrate that the proposed scheme significantly outperforms individual virtual MIMO, multi-hop technologies, and double-string networks in terms of energy conservation. The energy consumption levels under the MIHOP scheme are approximately 12.98%, 47.55% and 48.30% less than that under virtual MIMO schemes, multi-hop networks and doublestring networks, respectively.
We present an approach to optimize the MapReduce architecture, which could make heterogeneous cloud environment more stable and efficient. Fundamentally different from previous methods, our approach introduces the mac...
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We present an approach to optimize the MapReduce architecture, which could make heterogeneous cloud environment more stable and efficient. Fundamentally different from previous methods, our approach introduces the machine learning technique into MapReduce framework, and dynamically improve MapReduce algorithm according to the statistics result of machine learning. There are three main aspects: learning machine performance, reduce task assignment algorithm based on learning result, and speculative execution optimization mechanism. Furthermore, there are two important features in our approach. First, the MapReduce framework can obtain nodes' performance values in the cluster through machine learning module. And machine learning module will daily calibrate nodes' performance values to make an accurate assessment of cluster performance. Second, with the optimization of tasks assignment algorithm, we can maximize the performance of heterogeneous clusters. According to our evaluation result, the cluster performance could have 19% improvement in current heterogeneous cloud environment, and the stability of cluster has greatly enhanced.
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