Dynamic Flow Control for Big Data Transmissions toward 5G Multi-hop Relaying Mobile Networks

Abstract

Cloud computing provides various diverse services for users accessing big data through high data rate cellular networks, e.g., LTE-A, IEEE 802.11ac, etc. Although LTE-A supports very high data rate, multi-hop relaying, and cooperative transmission, LTE-A suffers from high interference, path loss, high mobility, etc. Additionally, the accesses of cloud computing services need the transport layer protocols (e.g., TCP, UDP, and streaming) for achieving end-to-end transmissions. Clearly, the transmission QoS is significantly degraded when the big data transmissions are done through the TCP protocol over a high interference LTE-A environment. Thus, this paper proposes a cross-layer-based adaptive TCP algorithm to gather the LTE-A network states (e.g., AMC, CQI, relay link state, available bandwidth, etc.), and then feeds the state information back to the TCP sender for accurately executing the network congestion control of TCP. As a result, by using the accurate TCP congestion window (cwnd) under a high interference LTE-A, the number of timeouts and packet losses are significantly decreased. Numerical results demonstrate that the proposed approach outperforms the compared approaches in goodput and fairness, especially in high interference environment. Especially, the goodput of the proposed approach is 139.42% higher than that of NewReno The results can justify the claims of the proposed approach.