D4.3 Final Report on Network-Level Solutions

Research activities in METIS reported in this document focus on proposing solutions to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond. This document provides the final findings on several network-level aspects and groups of solutions that are considered essential for designing future 5G solutions. Specifically, it elaborates on: -Interference management and resource allocation schemes -Mobility management and robustness enhancements -Context aware approaches -D2D and V2X mechanisms -Technology components focused on clustering -Dynamic reconfiguration enablers These novel network-level technology concepts are evaluated against requirements defined by METIS for future 5G systems. Moreover, functional enablers which can support the solutions mentioned aboveare proposed. We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675

Dense wireless network design and evaluation – an aircraft cabin use case

One of the key requirements of fifth generation (5G) systems is having a connection to mobile networks without interruption at anytime and anywhere, which is also known as seamless connectivity. Nowadays, fourth generation (4G) systems, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A), are mature enough to provide connectivity to most terrestrial mobile users. However, for airborne mobile users, there is no connection that exists without interruption. According to the regulations, mobile connectivity for aircraft passengers can only be established when the altitude of the aircraft is above 3000 m. Along with demands to have mobile connectivity during a flight and the seamless connectivity requirement of 5G systems, there is a notable interest in providing in-flight wireless services during all phases of a flight. In this thesis, many issues related to the deployment and operation of the onboard systems have been investigated. A measurement and modelling procedure to investigate radio frequency (RF) propagation inside an aircraft is proposed in this thesis. Unlike in existing studies for in-cabin channel characterization, the proposed procedure takes into account the deployment of a multi-cell onboard system. The proposed model is verified through another set of measurements where reference signal received power (RSRP) levels inside the aircraft are measured. The results show that the proposed model closely matches the in-cabin RSRP measurements. Moreover, in order to enforce the distance between a user and an interfering resource, cell sectorization is employed in the multi-cell onboard system deployment. The proposed propagation model is used to find an optimum antenna orientation that minimizes the interference level among the neighbouring evolved nodeBs (eNBs). Once the optimum antenna deployment is obtained, comprehensive downlink performance evaluations of the multi-cell, multi-user onboard LTE-A system is carried out. Techniques that are proposed for LTE-A systems, namely enhanced inter-cell interference coordination (eICIC) and carrier aggregation (CA), are employed in the system analysis. Different numbers of eNBs, antenna mounting positions and scheduling policies are examined. A scheduling algorithm that provides a good tradeoff between fairness and system throughput is proposed. The results show that the downlink performance of the proposed onboard LTE-A system achieves not only 75% of the theoretical limits of the overall system throughput but also fair user data rate performance, irrespective of a passenger’s seat location. In order to provide the seamless connectivity requirement of 5G systems, compatibility between the proposed onboard system deployment and the already deployed terrestrial networks is investigated. Simulation based analyses are carried out to investigate power leakage from the onboard systems while the aircraft is in the parked position on the apron. According to the regulations, the onboard system should not increase the noise level of the already deployed terrestrial system by 1 dB. Results show that the proposed onboard communication system can be operated while the aircraft is in the parked position on the apron without exceeding the 1 dB increase in the noise level of the already deployed terrestrial 4G network. Furthermore, handover parameters are obtained for different transmission power levels of both the terrestrial and onboard systems to make the transition from one system to another without interruption while a passenger boards or leaves the aircraft. Simulation and measurement based analyses show that when the RSRP level of the terrestrial system is below -65 dBm around the aircraft, a boarding passenger can be smoothly handed over to the onboard system and vice versa. Moreover, in order to trigger the handover process without interfering with the data transmission, a broadcast control channel (BCCH) power boosting feature is proposed for the in-cabin eNBs. Results show that employing the BCCH power boosting feature helps to trigger the handover process as soon as the passengers step on board the aircraft

Optimized distributed inter-cell interference coordination (ICIC) Scheme using projected subgradient and network flow optimization

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial-time distributed algorithm that is based on the primal-decomposition, the projected-subgradient, and the network flow optimization methods. In comparison with baseline schemes, simulation results show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin