Context-Aware Nanoscale Modeling of Multicast Multihop Cellular Networks

Lorenzo, Beatriz; Glisic, Savo

doi:10.1109/tnet.2012.2199129

Cited by 19 publications

(31 citation statements)

References 16 publications

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“…Considering multi-hop communications between nodes and base stations, some works such as [3] investigate the capacity of multihop cellular networks, which has been shown to be much higher than that of traditional cellular networks. The works [28], [29] also show that multihop transmissions can improve the multicast throughput of cellular networks. However, these works only consider the case where nodes share the cellular channels and have not exploited the local available secondary channels as we propose in this study.…”

Section: Related Workmentioning

confidence: 98%

Energy Consumption Optimization for Multihop Cognitive Cellular Networks

Huang

et al. 2015

IEEE Trans. on Mobile Comput.

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Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC 2 N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a Non-Linear Programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ǫ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ǫ) and larger than (1 − ǫ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ǫ-bounded approximate results and the optimal result as well.

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Section: Related Workmentioning

confidence: 98%

Energy Consumption Optimization for Multihop Cognitive Cellular Networks

Huang

et al. 2015

IEEE Trans. on Mobile Comput.

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“…As the focus of this work is on high dense networks, the channel model considered includes the propagation losses, but not the effects of fading due to the proximity between the users [36]. Thus, if we denote by the transmission power of user , then the received power at AP is (1) where is the channel gain between user and AP , is the transmission distance between them, and is the path-loss factor.…”

Section: A Network Modelmentioning

confidence: 99%

“…The short transmission distance enabled by the availability of additional access points will significantly reduce the impact of fading. The issue of channel defading has been recently discussed in [36]. It was argued that in multihop wireless networks, by reducing the distance between the receiver/transmitter pairs, a point should be reached where the multipath component can be neglected, and the general channel coefficient that includes the fading can be approximated by the attenuation due to distance only .…”

Section: A Network Modelmentioning

confidence: 99%

A Framework for Dynamic Network Architecture and Topology Optimization

Shafigh

Lorenzo

Glisic

et al. 2016

IEEE/ACM Trans. Networking

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Abstract-A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

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“…For example, architectural aspects and routing protocols were studied in [16]- [19], and different relay selection schemes were recently proposed in [20]- [22].…”

Section: Related Workmentioning

confidence: 99%

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

Perez-Romero

Sánchez–González

Agustí

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and in which mobile devices are enabled with device-to-device communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station or by connecting through another UE that acts as an access point and relays the traffic from a cellular base station. The paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multi-cell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power, while exhibiting much lower computational requirements.

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Context-Aware Nanoscale Modeling of Multicast Multihop Cellular Networks

Cited by 19 publications

References 16 publications

Energy Consumption Optimization for Multihop Cognitive Cellular Networks

Energy Consumption Optimization for Multihop Cognitive Cellular Networks

A Framework for Dynamic Network Architecture and Topology Optimization

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

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