Traffic models impact on OFDMA scheduling design

Abstract

This article studies the impact on the design of scheduling algorithms for Orthogonal Frequency Division Multiple Access (OFDMA) systems of two traffic models described in the evaluation methodology proposals from standardization bodies: the full buffer and the finite buffer traffic models. The analysis concentrates on utility-based scheduling with an α-fair utility function for Non-Real Time (NRT) services. The results show that a gradient scheduling algorithm is able to maximize the aggregate utility over all the users when the less realistic full buffer model is adopted; but not when the finite buffer model is applied. The results also show that with the full buffer model a gradient scheduler exhibits a trade-off between average user throughput and the user throughput at 5% outage, but it does not when the more realistic finite buffer is used. Therefore, it is concluded that designs of scheduling algorithms for NRT services for OFDMA systems carried out under the full buffer model assumption may fail to provide the desired performance benefits in realistic scenarios. Based on the results presented, a recommendation on scheduling design is provided.

An analysis of the video capabilities of multiple antenna LTE networks

“…Thus, due to a low spatial correlation of interference, spectral efficiency will be higher for users with low SINR (Signal to Interference Noise Ratio) as seen in Figure 2, which shows the CDF (Cumulative Distribution Function) for 2x2 and 4x2 MIMO. Also, the limitation to the highest possible MCS (Modulation Coding Schemewhich is chosen by the eNodeB following measurements made by the terminal) that will be supported, will reduce the spectral efficiency gains of users with high SINR [13]. Table 4 shows the gains in spectral efficiency reported by MIMO 4x2 versus 2x2.…”

Interference characterization and mitigation benefit analysis for LTE-A macro and small cell deployments

“…This allows us to increase the number of OFDMA symbols per TTI from 11 to 13 to improve the data rate. The value of β in (6) for the GPF scheduler is fixed to 0.6 as recommended in [20]. The link to-system level modelling is according to [25].…”

“…On the one hand, closed-loop full-buffer models consider a constant number of users with unlimited data to transmit. In contrast, finite-buffer models (also known as FTP traffic models) include user arrival (birth) and departure (death) processes, and it is assumed that the users have a limited amount of data to transmit, and they leave the network once they have done so [20]. These models can be of the closed-loop or open-loop types, depending on whether the number of users in the network is fixed or variable, respectively.…”

“…PF has been found to offer a good trade-off between scheduling gains and fairness, especially under full-buffer traffic models. In addition, a modified gradient search β-fair scheduler algorithm, known as Generalized PF (GPF), will be included as it was found to be more attractive for scenarios with a birthdeath traffic model in [20]. Finally, we will present results for the Blind Equal Throughput (BET) scheduler, with α = 0, β = 1, targeted to serving users with an equal average throughput [24].…”

A performance comparison of in-band full duplex and dynamic TDD for 5G indoor wireless networks

“…For instance, if we have a total of 20 active MSs, with ψ u = 0.1, this means that on average, in every time slot, there will be 2 UL MSs and 18 DL MSs. A full buffer traffic model is assumed, where the MSs always have data to transmit or receive [27]. The instantaneous S-TDD system transmission mode (i_state) is decided in an alternating manner, such that the system alternates DL and UL in every instance, each AP decides the MS to serve if there is any, and the selection is performed with equal probability among all the MSs with a traffic demand similar to the system's transmission mode.…”