Scheduling for Improving System Capacity in Multiservice 3GPP LTE

Abstract

We study the impact of scheduling algorithms on the provision of multiple services in the long term evolution (LTE) system. In order to measure how well the services are provided by the system, we use the definition of joint system capacity. In this context, we claim that scheduling strategies should consider the current satisfaction level of each service and the offered load to the system by each service. We propose a downlink-scheduling strategy according to these ideas named capacity-driven resource allocation (CRA). The CRA scheduler dynamically controls the resource sharing among flows of different services such as delay-sensitive and rate demanding ones. Moreover, CRA scheduler exploits the channel-quality knowledge to utilize the system resources efficiently. Simulation results in a multicell scenario show that the CRA scheduler is robust regarding channel quality knowledge and that it provides significant gains in joint system capacity in single and mixed service scenarios.

Robust scheduling algorithm for guaranteed bit rate services

“…The HARQ FDPS is one of the well-known packet scheduling algorithms developed to provide support for TB (re)transmission within multi-carrier mobile communication systems (Lima et al, 2010;Vukadinovic and Karlsson, 2010). TD and FD schedulers of the HARQ FDPS use the PF and multi-carrier PF algorithms, respectively, in this performance comparison.…”

Joint Resource Block Assignment and Power Allocation Problem for Rate Maximization With QoS Guarantees in Multiservice Wireless

“…We assume that, at the current TTI, terminal j has a data rate requirement equal to t j . It is important to mention here that long-term data rate requirements can be mapped to instantaneous data rate requirements [27]. The minimum satisfaction constraints for each service are represented by the parameter k s which is the minimum number of terminals from service s that should be satisfied.…”

Spectral and energy efficiency with satisfaction constraints

“…j∈Js u n∈N r j,n · x j,n , t j ≥ k s , ∀s ∈ S, (1d) 2 Note that the transmit power is present in the definition of r j,n .…”

“…RRA algorithms are responsible for managing the scarce radio resources such as power, time slots, spatial channels and frequency chunks [2]. The rational and efficient use of these resources in the radio access interface has the potential to optimize even further the aforementioned objectives such as spectral and energy efficiency, and QoS.…”