Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devic...Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devices. This is because an optimal resource allocation scheme which attempts to multiplex the available bandwidth in order to maximize Quality of service(QoS), will pose an exponential computational burden at eNodeB. In order to minimize such computational overhead, this work proposes a hybrid offline-online resource allocation strategy which effectively allocates all the available resources among flows such that their QoS requirements are satisfied. The flows are firstly classified into priority buckets based on real-time criticality factors. During the offline phase, the scheduler attempts to maintain the system load within a pre-specified safe threshold value by selecting an appropriate number of buckets. This offline selection procedure makes use of supervisory control theory of discrete event systems to synthesize an offline scheduler. Next, we have devised an online resource allocation strategy which runs on top of the offline policy and attempts to minimize the impact of the inherent variability in wireless networks. Simulation results show that the proposed scheduling framework is able to provide satisfactory QoS to all end users in most practical scenarios.展开更多
基金partially supported by TATA Consultancy Services(TCS),India,through TCS Research Fellowship Program
文摘Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devices. This is because an optimal resource allocation scheme which attempts to multiplex the available bandwidth in order to maximize Quality of service(QoS), will pose an exponential computational burden at eNodeB. In order to minimize such computational overhead, this work proposes a hybrid offline-online resource allocation strategy which effectively allocates all the available resources among flows such that their QoS requirements are satisfied. The flows are firstly classified into priority buckets based on real-time criticality factors. During the offline phase, the scheduler attempts to maintain the system load within a pre-specified safe threshold value by selecting an appropriate number of buckets. This offline selection procedure makes use of supervisory control theory of discrete event systems to synthesize an offline scheduler. Next, we have devised an online resource allocation strategy which runs on top of the offline policy and attempts to minimize the impact of the inherent variability in wireless networks. Simulation results show that the proposed scheduling framework is able to provide satisfactory QoS to all end users in most practical scenarios.
