Time-Frequency Resource Conversion Based Scheduling for On-Demand Data Services

Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws spectrum resources strategically from connection(s) not focused on by the user, to relieve network congestion effectively. Aiming at supporting the exponentially increasing traffic volume, especially on-demand data services, in this work we propose TFRC-based scheduling techniques. Considering an LTE-type cellular network, we formulate the problem of service scheduling as a joint request, channel, and slot allocation problem, which is a mixed integer nonlinear programming (MINLP) problem. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the MINLP problem. Simulation results not only demonstrate the efficiency of the proposed algorithm in terms of quality-of-service (QoS) provisioning and network resource utilization, but also show the effectiveness of the proposed TFRC-based scheduling techniques when integrating with the existing scheduling strategies such as first in first served (FIFS) and earliest deadline first (EDF).