Concepts on Train-to-Ground Wireless Communication System for Hyperloop: Channel, Network Architecture, and Resource Management

Hyperloop is envisioned as a novel transportation way with merits of ultra-high velocity and great traveling comforts. In this paper, we present some concepts on the key technologies dedicated to the train-to-ground communication system based on some prevailing fifth-generation communication (5G) technologies from three aspects: wireless channel, network architecture, and resource management. First, we characterize the wireless channel of the distributed antenna system (DAS) using the propagation-graph channel modelling theory. Simulation reveals that a drastic Doppler shift variation appears when crossing the trackside antenna. Hence, the leaky waveguide system is a promising way to provide a stable receiving signal. In this regard, the radio coverage is briefly estimated. Second, a cloud architecture is utilized to integrate several successive trackside leaky waveguides into a logical cell to reduce the handover frequency. Moreover, based on a many-to-many mapping relationship between distributed units (DUs) and centralized units (CUs), a novel access network architecture is proposed to reduce the inevitable handover cost by using the graph theory. Simulation results show that this scheme can yield a low handover cost. Then, with regards to the ultra-reliable and low latency communication (uRLLC) traffic, a physical resource block (PRB) multiplexing scheme considering the latency requirements of each traffic type is exploited. Simulation presents that this scheme can maximize the throughput of non-critical mission communication services while guaranteeing the requirements of uRLLC traffic. Finally, in terms of the non-critical mission communication services, two cache-based resource management strategies are proposed to boost the throughput and reduce the midhaul link burden by pre-fetching and post-uploading schemes. Simulation demonstrates that the cache-based schemes can boost the throughput dramatically.

Increasing LTE-Advanced Network Capacity Using The Inter-band Carrier Aggregation (Downlink Side) Method

According to the identification of the Operating Support System (OSS) by the Smartfren cellular operator in the Central Bandung area, six sites are found to have high traffic capacity with the physical resource block (PRb) percentage of 82.6 %. The use of PRb > 80 % is included in the warning indicator 2 based on the operator’s standards. It is also strengthened by the condition of the existing sites with the average Reference Signal Receive Power (RSRP) of -103.3 dBm, Signal to Interference Noise Ratio (SINR) of 6.28 dB, and throughput of 27.78 Mbps, thus resulting in non-optimal network performance in the area. Therefore, in this study, the inter-band Carrier Aggregation (CA) was applied by combining the 40 Time Division Duplex (TDD) band (2300 MHz) and band 5 Frequency Division Duplex (FDD) (850 MHz). One of the advantages of applying this method is that it can increase the user network capacity by maximizing the resources owned by the operator. The predetermined scenario taking into account the initial network condition indicated a decrease in the PRb percentage by 44.50 % and an increase in the average RSRP value by 12.8 dBm, SINR by 5.14 dB, and throughput by 34.59 Mbps.

Design of a cell selection mechanism to mitigate interference for cell-edge macro users in femto-macro heterogeneous network

The Femto-Macro heterogeneous network is a promising solution to improve the network capacity and coverage in mobile network. However interference may rise due to femtocell deployment nearby to macro user equipment (MUE) within macrocell network coverage. Femtocell offers main priority in resource allocation to its subscribed femto user equipment (FUE) rather than unsubscribed MUE. MUEs will suffer severe interference when they are placed near or within the femtocell area range especially at the cell edge. This phenomenon occurs due to the distance is far from its serving macro base station (MBS) to receive good signal strength. This paper presents a design of cell selection scheme for cell-edge MUE to select an optimal femto base station (FBS) as its primary serving cell in physical resource block allocation. In this study, the proposed cell selection consists of four main elements: measuring the closest FBS distance, Signal to Interference-plus- Noise-Ratio (SINR), physical resource block (PRB) availability and node density level for the selected base station. The main goal is to ensure celledge MUE has priority fairly with FUE in physical resource block allocation per user bandwidth demand to mitigate interference. Hence, the cell-edge MUE has good experienced on receiving an adequate user data rate to improve higher network throughput.

A Mobile Network Planning Tool Based on Data Analytics

Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity of layers, applications, and Radio Access Technologies (RAT). In this context, a network planning tool capable of dealing with this complexity is highly convenient. The objective is to exploit the information produced by and already available in the network to properly deploy, configure, and optimise network nodes. This work presents such a smart network planning tool that exploits Machine Learning (ML) techniques. The proposed approach is able to predict the Quality of Service (QoS) experienced by the users based on the measurement history of the network. We select Physical Resource Block (PRB) per Megabit (Mb) as our main QoS indicator to optimise, since minimizing this metric allows offering the same service to users by consuming less resources, so, being more cost-effective. Two cases of study are considered in order to evaluate the performance of the proposed scheme, one to smartly plan the small cell deployment in a dense indoor scenario and a second one to timely face a detected fault in a macrocell network.