Toward Traffic Patterns in High-speed Railway Communication Systems: Power Allocation and Antenna Selection

TL;DR

This paper investigates dynamic power allocation and antenna selection in high-speed railway communication systems, considering various channel conditions and traffic types to optimize system performance.

Contribution

It introduces a joint optimization framework for power allocation and antenna selection tailored to different scattering environments and traffic patterns in HSR systems.

Findings

PAWAS generalizes channel-inversion for delay-sensitive traffic in sparse terrains.

Water-filling is optimal for SIMO in delay-insensitive traffic.

Hybrid traffic can be partitioned for tailored PAWAS strategies.

Abstract

In high-speed railway (HSR) communication systems, distributed antenna is usually employed to support frequent handover and enhance the signal to noise ratio to user equipments. In this case, dynamic time-domain power allocation and antenna selection (PAWAS) could be jointly optimized to improve the system performances. This paper consider this problem in such a simple way where dynamic switching between multiple-input-multiple-output (MIMO) and single-input-multiple-output (SIMO) is allowed and exclusively utilized, while the channel states and traffic demand are taken into account. The channel states includes sparse and rich scattering terrains, and the traffic patterns includes delay-sensitive and delay-insensitive as well as hybrid. Some important results are obtained in theory. In sparse scattering terrains, for delay-sensitive traffic, the PAWAS can be viewed as the generalization of channel-inversion associated with transmit antenna selection. On the contrary, for delay-insensitive traffic, the power allocation with MIMO can be viewed as channel-inversion, but with SIMO, it is traditional water-filling. For the hybrid traffic, the PAWAS can be partitioned as delay-sensitive and delay-insensitive parts by some specific strategies. In rich scattering terrains, the corresponding PAWAS is derived by some amendments in sparse scattering terrains and similar results are then presented.