Joint Transmitter Power Control and Mobile Cache Management in Wireless Computing

Gitzenis, Savvas; Bambos, Nicholas

doi:10.1109/tmc.2007.70755

Cited by 10 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, note that obtaining V (X), X ∈F K ×Š K requires to solve a system of (F + 1) F C K equations given in (10), while obtainingV 1 k (X 1 k ), X 1 k ∈F × F , k ∈ K only requires to solve a system of KF (F + 1) equations given in (15). Therefore, under the value function approximation in (14), the non-polynomial computational complexity is eliminated.…”

Section: A Value Approximationmentioning

confidence: 99%

“…In most cases, the assumption cannot be satisfied, and hence the proposed joint design has limited applications. To address this problem, [10]- [15] consider joint pushing and caching based on statistical information of content requests (e.g., content popularity), while [16] considers online learning-aided joint design adaptive to instantaneous content requests and without priori knowledge of statistical information of content requests. Specifically, [10] optimizes joint pushing and caching to maximize the network capacity in a push-based converged network with limited user storage.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in [10]- [14], temporal correlation of a demand process is not captured, and hence the potential of joint pushing and caching cannot be fully unleashed. In contrast, [15] and [16] exploit temporal correlation in the joint designs. In particular, [15] investigates efficient transmission power control and caching to minimize both the access delay and the transmission cost using MDP.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], the authors maximize the average reward obtained by proactively serving user demands and propose an online learning-aided control algorithm. However, in [15] and [16], only a single user setup is considered without reflecting asynchronous demands for common contents from multiple users, and hence the proposed joint designs may not be directly applied to practical networks with multiple users. Moreover, the pushing policy in [16] can predownload contents only one time slot ahead.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

Sun

Cui

Liu

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

View full text Add to dashboard Cite

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, by exploiting storage resources at end users and predictability of user demand processes, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers.In particular, we formulate the stochastic optimization problem as an infinite horizon average cost Markov Decision Process (MDP), for which there generally exist only numerical solutions without many insights. By structural analysis, we show how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. In addition, we show that the optimal average transmission cost decreases with the cache size, revealing a tradeoff between the cache size and the bandwidth utilization. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a decentralized policy with polynomial complexity w.r.t. the numbers of users and files as well as cache size, by a linear approximation of the value function and optimization relaxation techniques. Next, we propose an online decentralized algorithm to implement the proposed low-complexity decentralized policy using the technique of Q-learning, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled content-centric wireless networks.

show abstract

Section: A Value Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

Sun

Cui

Liu

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

View full text Add to dashboard Cite

show abstract

“…Future network condition prediction enable the postponing of data transfers for more favorable conditions. For example, the authors of [22] employ data prefetching when more energy-efficient networks are available, in order to lower the overall energy cost. These works are complementary to Context-for-Wireless.…”

Section: Related Workmentioning

confidence: 99%

Context-Based Network Estimation for Energy-Efficient Ubiquitous Wireless Connectivity

Rahmati

Lin

2011

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Abstract-Context information brings new opportunities for efficient and effective system resource management of mobile devices. In this work, we focus on the use of context information to achieve energy-efficient, ubiquitous wireless connectivity. Our field-collected data show that the energy cost of network interfaces poses a great challenge to ubiquitous connectivity, despite decent availability of cellular networks. We propose to leverage the complementary strengths of Wi-Fi and cellular interfaces by automatically selecting the most efficient one based on context information. We formulate the selection of wireless interfaces as a statistical decision problem. The challenge is to accurately estimate Wi-Fi network conditions without powering up the network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, to statistically estimate Wi-Fi network conditions with negligible overhead. We evaluate several context-based algorithms for the estimation and prediction of current and future network conditions. Simulations using field-collected traces show that our network estimation algorithms can improve the average battery lifetime of a commercial mobile phone for an ECG reporting application by 40 percent, very close to the estimated theoretical upper bound of 42 percent. Furthermore, our most effective algorithm can predict Wi-Fi availability for one and ten hours into the future with 95 and 90 percent accuracy, respectively.Index Terms-Network architecture and design, local and wide-area networks.

show abstract

P2P Caching Schemes for Jointly Minimizing Memory Cost and Transmission Delay over Information-Centric Networks

Zhang

Zhu

2016

2016 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

Joint Transmitter Power Control and Mobile Cache Management in Wireless Computing

Cited by 10 publications

References 25 publications

Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

Context-Based Network Estimation for Energy-Efficient Ubiquitous Wireless Connectivity

P2P Caching Schemes for Jointly Minimizing Memory Cost and Transmission Delay over Information-Centric Networks

Contact Info

Product

Resources

About