Abstract
Long Term Evolution (LTE) Carrier Aggregation (CA) was introduced by the Release-10 3GPP specifications. CA allows aggregation of up to 5 cells for a terminal; both downlink (DL) CA and uplink (UL) CA are supported by the 3GPP specifications. However, the first commercial deployments focus on the aggregation of two cells in the downlink. The benefits of LTE CA are increased terminal peak data rates, aggregation of fragmented spectrum and fast load balancing. In this paper, we analyze different strategies of DL scheduling for LTE CA including centralized, independent and distributed schedulers, we provide the corresponding simulation results considering UE data rate limitations and different traffic models. Also, we compare the performance of a single LTE carrier with LTE CA using the same total bandwidth.
Highlights
Carrier Aggregation is one of the Long Term Evolution Advanced features introduced by 3GPP in order to meet IMT-Advanced requirements of peak data rates of up to 1 Gbit/s in the DL and 500 Mbit/s in the UL [1,2,3]
The distributed and coordinated schedulers per cell can achieve better performance for Carrier Aggregation compared to independent schedulers [10], the reason being that distributed schedulers can exploit frequency diversity over all aggregated cells in a similar way as the centralized scheduler
The performances of downlink intra-band Carrier Aggregation (CA) and single-carrier operation are analyzed without Physical Downlink Control Channel (PDCCH) overhead
Summary
Carrier Aggregation is one of the Long Term Evolution Advanced features introduced by 3GPP in order to meet IMT-Advanced requirements of peak data rates of up to 1 Gbit/s in the DL and 500 Mbit/s in the UL [1,2,3]. In [7], performance results with high number of DL aggregated cells are provided; UL CA simulations results are reported in [8]. In this paper we focus on the aggregation of two DL cells since this is the first commercial deployment scenario for CA. One centralized scheduler for all aggregated cells. Independent schedulers per aggregated cell [9, 10]. We compare the above principles taking into account real-life effects such as traffic models and UE data rate limitations.
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