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Proactive small cell networks
 in In Proc. of Int’l Conf. on Telecommunications (ICT
"... Abstract—Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but ha ..."
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Abstract—Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but have limited capacity backhaul links. We then describe the model and define a Quality of Experience (QoE) metric in order to satisfy a given file request. The optimization problem is formulated in order to maximize this QoE metric for all requests under the capacity constraints. We solve this problem by introducing an algorithm, called PropCaching (proactive popularity caching), which relies on the popularity statistics of the requested files. Since not all requested files can be cached due to storage constraints, the algorithm selects the files with the highest popularities until the total storage capacity is achieved. Consecutively, the proposed caching algorithm is compared with random caching. Given caching and sufficient capacity of the wireless links, numerical results illustrate that the number of satisfied requests increases. Moreover, we show that PropCaching performs better than random caching in most cases. For example, for R = 192 number of requests and a storage ratio γ = 0.25 (storage capacity over sum of length of all requested files), the satisfaction in PropCaching is 85 % higher than random caching and the backhaul usage is reduced by 10%. Index Terms—Small cell networks, proactive caching, popularity caching I.
Cloud Storage for Small Cell Networks
, 2012
"... Cell Networks (SCNs) is a promising way of increasing capacity. Interestingly, by having such a huge amount of user devices as well as small cells deployed in indoor or outdoor areas, one can take benefit of such distributed network for storage purposes. Hence, Depending on the deployed scenario, th ..."
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Cited by 3 (2 self)
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Cell Networks (SCNs) is a promising way of increasing capacity. Interestingly, by having such a huge amount of user devices as well as small cells deployed in indoor or outdoor areas, one can take benefit of such distributed network for storage purposes. Hence, Depending on the deployed scenario, these storage units can also be used for content caching to relieve the backhaul constraints and increase the peak rate. In this work, by extending concepts of cloud storage to SCNs, we discuss the theoretical challenges in order to embed small cells with storage capabilities. We also briefly introduce Open Cloud Protocol (OCP) as a unified software storage framework.
Proactive data download and user demand shaping for data networks,” submitted to
 IEEE Transactions on Information Theory, [Online] arXiv
"... AbstractIn this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on human behavioral patterns, and the emergence of highly capable handheld devices (such as smart phones), our framework aims to smooth out the ne ..."
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AbstractIn this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on human behavioral patterns, and the emergence of highly capable handheld devices (such as smart phones), our framework aims to smooth out the network traffic over time. Such a load balance minimizes the total cost required for data delivery. The framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the offpeak hours based on a statistical prediction demand profile for each user, whereas smart content recommendation assigns modified valuations to data item so as to render the users' demand less uncertain. Hence, it boosts the performance of proactive services. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales with the number of users as the cost function itself does. Further, we prove that demand shaping through smart recommendation strictly reduces the incurred cost even below that of proactive data service only.
Proactive Multicasting with Predictable Demands
"... Abstract—In a recent work, we have introduced the notion of proactive resource allocation in wireless networks whereby the predictability of user demands are leveraged to significantly enhance the spectral efficiency of the network in outage limited regimes. In this paper, we expand the horizon to t ..."
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Abstract—In a recent work, we have introduced the notion of proactive resource allocation in wireless networks whereby the predictability of user demands are leveraged to significantly enhance the spectral efficiency of the network in outage limited regimes. In this paper, we expand the horizon to the important scenario of multicast traffic. Our analysis reveals two additional types of gains that can be leveraged in this proactive multicast scenario. The first can be attributed to the basic nature of multicast traffic in which each request would represent a data source rather than a user, as it would in the unicast case. The second is the demand alignment phenomenon whereby the predictive network would wait to gather as much requests as possible and serve them altogether using the same resources. We analytically derive the impact of these advantages on the system diversity gain, which quantifies the exponential decay rate of the outage probability, and further illustrate the resulting gains via numerical results. I.
Author manuscript, published in "20th International Conference on Telecommunications (ICT), casablanca: Morocco (2013)" DOI: 10.1109/ICTEL.2013.6632164
, 2014
"... Abstract—Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but ha ..."
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Abstract—Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but have limited capacity backhaul links. We then describe the model and define a Quality of Experience (QoE) metric in order to satisfy a given file request. The optimization problem is formulated in order to maximize this QoE metric for all requests under the capacity constraints. We solve this problem by introducing an algorithm, called PropCaching (proactive popularity caching), which relies on the popularity statistics of the requested files. Since not all requested files can be cached due to storage constraints, the algorithm selects the files with the highest popularities until the total storage capacity is achieved. Consecutively, the proposed caching algorithm is compared with random caching. Given caching and sufficient capacity of the wireless links, numerical results illustrate that the number of satisfied requests increases. Moreover, we show that PropCaching performs better than random caching in most cases. For example, for R = 192 number of requests and a storage ratio γ = 0.25 (storage capacity over sum of length of all requested files), the satisfaction in PropCaching is 85 % higher than random caching and the backhaul usage is reduced by 10%. Index Terms—Small cell networks, proactive caching, popularity caching I.
EnergyEfficiency and Future Knowledge Tradeoff in Small Cells PredictionBased Strategies
"... Abstract—Predictive small cells networks and proactive resource allocation are considered as one of the key mechanisms for increasing the longterm energyefficiency of communication networks. Learning techniques exploit repetitive patterns in human behavior to predict some future transmission cont ..."
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Abstract—Predictive small cells networks and proactive resource allocation are considered as one of the key mechanisms for increasing the longterm energyefficiency of communication networks. Learning techniques exploit repetitive patterns in human behavior to predict some future transmission contexts of the network. In this paper, we target to improve the energy efficiency of delaytolerant transmissions by enabling flexibility in resource allocation with predictionbased strategies. We study the performance, in terms of energy efficiency of several scenarios of future knowledge ranging from zero to perfect knowledge of the future context, but also partial knowledge scenarios (shortterm predictions, longterm statistics or partial knowledge). An iterative process, approaching the optimal strategies in each scenario, is described. In some cases, closedform expressions of the optimal strategies to be implemented can be obtained and the performance in each scenario is computed. Our analytical and numerical results assess the potential benefit of exploiting the knowledge of the future in the case of a delaytolerant transmission and show how the system may benefit from a provided piece of information about the future transmission context. I.
Proactive Source Coding
"... AbstractA coding problem, over a slotted system, is introduced where the sender has to transmit one out of several packets to the receiver, but learns the request only at the beginning of each slot with prior statistical information about which packet is needed at the receiver. There is an associa ..."
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AbstractA coding problem, over a slotted system, is introduced where the sender has to transmit one out of several packets to the receiver, but learns the request only at the beginning of each slot with prior statistical information about which packet is needed at the receiver. There is an associated cost of sending bits at each slot, and the goal is to minimize the expected cost of the communication. A proactive coding scheme is proposed, where the source proactively communicates with the receiver before the receiver requests the message. This way, by designing a cost optimal side information at the receiver, the scheme is able to minimize the expected cost of the communication. Numerical results are provided demonstrating the gains obtained by proactive coding over the conventional coding technique.