User Behavior-Aware Scheduling Based on Time-Frequency Resource Conversion

Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws and reutilizes spectrum resources strategically from connection(s) not being focused on by the user to relieve network congestion effectively. In this paper, we study downlink scheduling based on TFRC for a Long-Term Evolution (LTE)-type cellular network to maximize service delivery. The service scheduling of interest is formulated as a joint request, channel and slot allocation problem, which is NP-hard. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the problem. For practical implementation, we propose TFRC-enabled low-complexity yet online scheduling algorithms, which integrate prediction-based leaky bucket-like traffic shaping and modified Smith ratio or exponential capacity based utility function. Furthermore, by establishing a charging model for the relationship between TFRC-enabled scheduling and its TFRC-disabled counterpart, we analytically study the benefits of integrating TFRC with scheduling. Simulation results not only verify the analysis of impact of key parameters on the performance improvement but corroborate the benefits of integrating TFRC with scheduling techniques in terms of quality-of-service provisioning and resource utilization as well.