Ahmed Mohamed Magdy Ahmed <br/><br/>
Analysis and optimization of caching techniques in eireless networks :   reactive and proactive paradigm /  
Ahmed Mohamed Magdy Ahmed 
 -  2017 
 - 83 p.   ill.   21 cm.  
<br/><br/>Supervisor: Mohammed Nafie 
<br/><br/>Thesis (M.A.)—Nile University, Egypt, 2017 . 
<br/><br/>"Includes bibliographical references" 
<br/><br/>Contents:<br/>1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.1 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br/>2. Dynamic Proactive Caching in Relay Networks . . . . . . . . . . . . . . 9<br/>2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10<br/>2.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>2.3 Lower Bound and Proposed Policy . . . . . . . . . . . . . . . . . . 16<br/>2.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br/>2.4.1 Impact of number of files on the cost reduction gain . . . . 23<br/>2.4.2 Impact of Zipf parameter on cost reduction gain . . . . . . . 25<br/>2.4.3 Comparison with caching at end user . . . . . . . . . . . . . 26<br/>2.4.4 Impact of prediction window size on the expected cost . . . 29<br/>3. On Optimal Dynamic Caching in Relay Networks . . . . . . . . . . . . . 31<br/>3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32<br/>3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 34<br/>3.3 Lower Bound and Proposed Policy . . . . . . . . . . . . . . . . . . 36<br/>3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 41<br/>3.4.1 Time average cost across time horizon . . . . . . . . . . . . 41<br/>viii<br/>3.4.2 Impact of block size on time average cost . . . . . . . . . . 43<br/>3.4.3 Impact of number of files on time average cost . . . . . . . . 43<br/>3.4.4 Impact of Zipf parameter on time average cost . . . . . . . 45<br/>3.4.5 Impact of relative channel cost on time average cost . . . . 46<br/>3.4.6 Comparison with no caching and LRU schemes . . . . . . . 47<br/>4. Towards Optimal Resource Allocation in Caching Relay Networks . . . . 49<br/>4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50<br/>4.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 53<br/>4.2.1 Main Problem . . . . . . . . . . . . . . . . . . . . . . . . . 53<br/>4.2.2 Optimal Service Portion . . . . . . . . . . . . . . . . . . . . 53<br/>4.2.3 Final Problem . . . . . . . . . . . . . . . . . . . . . . . . . 57<br/>4.2.4 Proposed Caching Technique . . . . . . . . . . . . . . . . . 58<br/>4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 58<br/>5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64<br/>5.1 Finding and Observations . . . . . . . . . . . . . . . . . . . . . . . 65<br/>5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66<br/>Bibliography . . . . . . . . . . 
<br/><br/> Abstract:<br/>In this work, we explore the performance of caching in relay networks. First, we<br/>investigate the idea of proactive caching in relay networks. Second, we investigate<br/>dynamic content caching in non-proactive networks. Third, we investigate the optimal<br/>time average transmission energy resulting from caching in non-proactive relay<br/>networks.<br/>In the first part of this work, we investigate the performance of dynamic proactive<br/>caching in relay networks where an intermediate relay station caches content for<br/>potential future use by end users. A central base station proactively controls the<br/>cache allocation such that cached content remains fresh for consumption for a limited<br/>number of time slots called proactive service window. With uncertain user demand<br/>over multiple data items and dynamically changing wireless links, we consider the<br/>optimal allocation of relay station’s cache to minimize the time average expected<br/>service cost. We characterize a fundamental lower bound on the cost achieved by<br/>any proactive caching policy. Then we develop an asymptotically optimal caching<br/>policy that attains the lower bound as the proactive caching window size grows. We<br/>provide numerical simulations to validate our analytical findings and demonstrate<br/>performance merits.<br/>In the second part, we investigate dynamic content caching in relay networks where<br/>an intermediate relay station (RS) can adaptively cache data content based on their<br/>iv<br/>varying popularity. With the objective of minimizing the time average cost of content<br/>delivery, we formulate and study the problem of optimal RS cache allocation when<br/>the popularities of data content are unknown apriori to the network. While optimal<br/>dynamic cache control suffers the curse of dimensionality, we develop a fundamental<br/>lower bound on the achievable cost by any caching policy. Inspired by the structure of<br/>such lower bound, we develop a reduced-complexity policy that is shown numerically<br/>to perform close to the lower bound.<br/>In the third part of this work, we investigate the performance of caching in relay<br/>networks where an intermediate relay station (RS) caches content for future demand<br/>by end users. With uncertain user demand over multiple data items and dynamically<br/>changing wireless links, we characterize the optimal transmission time for serving data<br/>items, cached data portion allocation of relay station and optimal service portion, as<br/>a part from the cached portion, to minimize the total average transmission energy.<br/>We argue that under several settings fully caching the higher popular items is the<br/>optimal caching policy which minimizes the total expected transmission energy. 
<br/><br/><br/>Text in English, abstracts in English. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Wireless Technologies
<br/><br/><label>Index Terms--Genre/Form: </label><br/>Dissertation, Academic
<br/><br/><label>Dewey Class. No.: </label>005