Download Radio Resource Management for Mobile Traffic Offloading in by Yuan Wu, Li Ping Qian, Jianwei Huang, Xuemin (Sherman) Shen PDF

By Yuan Wu, Li Ping Qian, Jianwei Huang, Xuemin (Sherman) Shen

This SpringerBrief bargains concrete layout examples for site visitors offloading. the 1st is an optimum source allocation for small-cell dependent site visitors offloading that goals at minimizing cellular clients’ info expense. the second one is an optimum source allocation for device-to-device assisted site visitors offloading that still minimizes the full power intake and mobile hyperlink utilization (while delivering an summary of the hard issues). either examples illustrate the significance of right source allocation to the luck of site visitors offloading, exhibit the ensuing functionality merits of executing optimum source allocation, and current the methodologies to accomplish the corresponding optimum offloading answer for site visitors offloading in heterogeneous mobile networks. The authors additionally comprise an outline of heterogeneous mobile networks and clarify various site visitors offloading paradigms starting from uplink site visitors offloading via small cells to downlink site visitors offloading through cellular device-to-device cooperation.
This short is a wonderful source for postgraduate scholars learning advanced-level subject matters in instant communications and networking. Researchers, engineers and execs operating in similar fields also will locate this short a useful source instrument.

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Additional resources for Radio Resource Management for Mobile Traffic Offloading in Heterogeneous Cellular Networks

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For the sake of clear presentation, we plot the MUs’ positions in an enlarged view. 7 s from 2 to 8 Mbps (as Problem (CMP) becomes infeasible when Ri ≥ 9 Mbps). 00GB RAM). 3 Due to the non-convexity of Problem (CMP), LINGO’s globalsolver consumes a very long time to compute the result. 89%), while consuming a significantly less computational time than LINGO. The key reason for such an advantage is that our Algorithm (Cen) exploits the layered-structure of Problem (SINR-M-P) and especially the monotonic property of Problem (SINR-M-TopP) to compute the optimal offloading solution efficiently.

5 Illustration of optimal offloading solution under different traffic demands (4-MU scenario). Top-subplot Traffic to the AP and BS. Middle-subplot Transmit-power to the AP and BS. Bottomsubplot Offloading ratio and the total cost figures show that the MUs’ traffic offloading decisions and the transmit-power allocations are strongly correlated due to the mutual interference at the AP. , Ri ≥ 8 Mbps), MU 1 (whose channel gain g1B is the largest) first starts to redirect its traffic to the BS, and then MU 2 (whose g2B is the second largest) follows.

Andrews, “User association for load balancing in heterogeneous cellular networks,” IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 2706–2716, 2013. 13. C. Ho, D. Yuan, and S. Sun, “Data offloading in load coupled networks: A utility maximization framework,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 1912–1931, 2014. 14. X. Chen, J. Wu, Y. Cai, H. Zhang, and T. Chan, “Energy-efficiency oriented traffic offloading in wireless networks: A brief survey and a learning approach for heterogeneous cellular networks,” IEEE Journal on Selected Areas in Communications, vol.

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