Femtocaching assisted multi-source D2D content delivery in cellular networks

Abstract

The influxes of diversified services and mass data lead to exponential growth of traffic load in mobile cellular networks. Cache-enabled device-to-device (D2D) communication provides a general framework to alleviate this situation. In contrast to previous single-source D2D models, this paper investigates a comprehensive content delivery framework based on a three-tier heterogeneous network (HetNet), where base station (BS), femtocaching auxiliary equipments (FAEs), and user terminals(UTs) are included. The cooperative D2D communication can be implemented from both FAEs and UTs to handle the ongoing explosive increase in ultra-dense scenario. Moreover, duplicate storage for requesting data in multiple neighbor nodes makes many-to-one D2D communication possible at the user layer. Considering the case that cellular users and D2D links reuse the same resources in the uplink period, the non-outage probability of the cellular communication is defined to guarantee the main communication quality. Under the constraints subject to cumulative interference, an optimization objective function based on multi-source D2D communication is deduced to achieve unprecedented average data rate. Numerical simulations show that our system yields network throughput exponentially while transferring traffic load of the BS reasonably.

Influence of Crowd Participation Features on Mobile Edge Computing

“…Figure 6a,b show the hit rate of four caching algorithms. In addition to the caching algorithm in [10], we also contrast our collaborative caching scheme against two classical caching algorithms: random caching and the most popular caching based on the file popularity. The algorithm in [10] combines the random caching with the most popular aching, i.e., fixed nodes like SBSs use the most popular algorithm, while UTs use random storage due to the mobility.…”

“…In addition to the caching algorithm in [10], we also contrast our collaborative caching scheme against two classical caching algorithms: random caching and the most popular caching based on the file popularity. The algorithm in [10] combines the random caching with the most popular aching, i.e., fixed nodes like SBSs use the most popular algorithm, while UTs use random storage due to the mobility. It can be observed that the proposed collaborative system brings greater caching gains owing to the dynamic and flexible characteristics, i.e., the edge nodes tend to have more great autonomy in collaborative caching.…”

“…For example, it changes the network topology over time, and the journey of content experiences intermittent connected links [8,9]. From a positive perspective, it brings many unprecedented opportunities ranging from better scalability of networks to low cost of deployment [10]. There are some works that consider the mobility or the sociality of nodes to form clusters, so as to exchange content among nodes efficiently [11,12].…”

Bat echolocation-based algorithm for device discovery in D2D communication

“…This approach initially figures the separation between the discoverer device situated at (X , Y) and the discovery devices situated at X ′ , Y ′ , also indicating the separation of the ith discoverer devices from the discovery device, b is a constant to define the state of the logarithmic, l is an arbitrary number in [−1, 1] , and ⋅ is a dot product [24].…”

“…where is a signal transfer factor, which signifies the effect of meeting probability by signal transfer. From (22) to (24), if device S and device D encounter each other more regularly in the network, the meeting probability among them becomes larger. When two devices encounter each other in the network, the discovery signal will be redirected to the device that meets more regularly with the destination device.…”