481 research outputs found
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Adaptive Cellular Layout in Self-Organizing Networks using Active Antenna Systems
The rapidly growing demand of capacity by wireless services is challenging the mobile industry with a need of new deployment strategies. Besides, the nature of the spatial and temporal distribution of user traffic has become heterogeneous and fluctuating intermittently. Those challenges are currently tackled by network densification and tighter spatial reuse of radio resources by introducing a heterogeneous deployment of small cells embedded in a macro cell layout. Since user traffic is varying both spatially and temporally, a so called busy hour planning is typically applied where enough small cells are deployed at the corresponding locations to meet the expected capacity demand. This deployment strategy, however, is inefficient as it may leave plenty of network resources under-utilized during non-busy hour, i.e., most of the operation time. Such over-provisioning strategy incurs high capital investment on infrastructure (CAPEX) as well as operating cost (OPEX) for operators. Therefore, optimal would be a network with flexible capacity accommodation by following the dynamics of the traffic situation and evading the inefficiencies and the high cost of the fixed deployment approach.
The advent of a revolutionizing base station antenna technology called Active Antenna Systems (AAS) is promising to deliver the required flexibility and dynamic deployment solution desired for adaptive capacity provisioning. Having the active radio frequency (RF) components integrated with the radiating elements, AAS supports advanced beamforming features. With AAS-equipped base station, multiple cell-specific beams can be simultaneously created to densify the cell layout by means of an enhanced form of sectorization. The radiation pattern of each cell-beam can be dynamically adjusted so that a conventional cell, for instance, can be split into two distinct cells, if a high traffic concentration is detected. The traffic in such an area is shared among the new cells and by spatially reusing the frequency spectrum, the cell-splitting (sectorization) doubles the total available radio resources at the cost of an increased co-channel interference between the cells.
Despite the AAS capability, the realization of flexible sectorization for dynamic cell layout adaptation poses several challenges. One of the challenges is that the expected performance gain from cell densification can be offset by the ensuing co-channel interference in the system. It is also obvious that a self-organized autonomous management and configuration is needed, if cell deployment must follow the variation of the user traffic over time and space by means of a sectorization procedure. The automated mechanism is desired to enhance the system performance and optimize the user experience by automatically controlling the sectorization process. With such a dynamic adaptation scheme, the self-organizing network (SON) facilities are getting a new dimension in terms of controlling the flexible cell layout changes as the environment including the radio propagation characteristics cannot be assumed stationary any longer. To fully exploit the flexible sectorization feature in three-dimensional space, reliable and realistic propagation models are required which are able to incorporate the dependency of the radio channel characteristics in the elevation domain. Analysis of the complex relationship among various system parameters entails a comprehensive model that properly describes the AAS-sectorization for conducting detailed investigation and carrying out precise evaluation of the ensuing system performance.
A novel SON algorithm that automates the AAS-sectorization procedure is developed. The algorithm controls the activation/deactivation of cell-beams enabling the sectorization based cell layout adjustment adaptively. In order to effectively meet the dynamically varying network capacity demand that varies according to the spatial user distribution, the developed SON algorithm monitors the load of the cell, the spatial traffic concentrations and adapts the underlying cell coverage layout by autonomously executing the sectorization either in the horizontal or vertical plane. The SON algorithm specifies various procedures which rely on real time network information collected using actual signal measurement reports from users. The particular capability of the algorithm is evading unforeseen system performance degradation by properly executing the sectorization not only where in the network and when it is needed, but also only if the ensuing co-channel interference does not have adverse impact on the user experience. To guarantee the optimality of the network performance after sectorization, a performance metric that takes both the expectable gain from radio resource and impact of the co-channel interference into account is developed. In order to combat the severity of the inter-cell interference problem that arises with AAS-sectorization between the co-channel operated cells, an interference mitigation scheme is developed in this thesis. The proposed scheme coordinates the data transmission between the co-sited cells by the transmission muting principle. To ensure that the transmission muting is not degrading the overall system performance by blanking more data transmission, a new SON algorithm that controls the optimal usage the proposed scheme is developed.
To appropriately characterize the spatial separation of the cell beams being activated with sectorization, a novel propagation shadowing model that incorporates elevation tilt parameter is developed. The new model addresses the deficiencies of the existing tilt-independent shadowing model which inherently assumes a stationary propagation characteristics in the elevation domain. The tilt-dependent shadowing model is able to statistically characterize the elevation channel variability with respect to the tilt configuration settings. Simplified 3D beamforming models and beam pattern synthesis approaches required for fast cell layout adaptation and dynamic configuration of the AAS parameters are developed for the realization of various forms of AAS-based sectorization. Horizontal and vertical sectorization are the two forms of AAS-based sectorization considered in this thesis where two beams are simultaneously created from a single AAS to split the underlying coverage layout in horizontal or vertical domain, respectively.
The performance of the developed theoretical AAS-sectorization concepts and models are examined by means of system level simulations considering the Long Term Evolution-Advanced (LTE-A) macro-site deployment within exemplifying scenarios. Simulation results have demonstrated that the SON mechanism is able to follow the different conditions when and where the sectorization delivers superior performance or adversely affects the user experience. Impacts on the performance of existing SON operations, like Mobility Robustness Optimization (MRO), which are relying on stationary cell layout conditions have been studied. Further investigations are carried out in combination with the cell layout changes triggered by the dynamic AAS-based sectorization. The observed results have confirmed that proper coordination is needed between the SON scheme developed for AAS sectorization and the MRO operation to evade unforeseen performance degradation and to ensure a seamless user experience.
The technical concepts developed in this thesis further have impacted the Generation Partnership Project (3GPP) SON for AAS Work Item (WI) discussed in the Radio Access Network (RAN)-3 Work Group (WG). In particular, the observed study results dealing with the interworking of the existing SON features and AAS sectorization have been noted in the standardization work
Long Term Evolution (LTE) Network Planning in Ipoh City
Long Term Evolution (LTE) is an evolutionary step towards improving
telecommunication to higher step. Long Term Evolution (LTE) or also known as 4G is a
radio platform technology which provides minimum latency with maximum data rates
and speed. LTE have been implemented in latest phones and still some places does not
have this facility due to transformation process. Another issue that we can look through
is the capability of the network to support users in the area. The coverage and capacity
that have been produced may not satisfy user demand and it might be the factor of a
network planning. This project will investigate the impact of the antenna parameters on
network planning as well identify range of coverage and capacity that can be provided
with a LTE network architecture for Ipoh city. In addition to that, transmission power
will be investigated with 2 type of sector which is 3 sector and 6 sector
PROPAGATION AND NETWORK ANALYSIS FOR A DIPOLE BASED MASSIVE MIMO ANTENNA FOR 5G BASE STATIONS
In today’s fast-paced world, where everyone/everything is moving towards an online platform, the need to provide high-speed data to all is inevitable. Hence, introducing the emerging 5G technology with orthogonal frequency division multiplexing integrated with massive MIMO technology is the need of the hour. A 640 port Massive MIMO (m-MIMO) antenna with high evenly spread gain and very low delay, along with a practically possible data rate operating in the mm waveband, is proposed for a 5G base station. The individual antenna element consists of a dipole (λ=0.5cm) designed to operate at 57GHz. Placing the cylindrical MIMO antenna array (8x20) facing the four directions forming the m-MIMO antenna (160x4) at the height of 3m from ground level for simulation. Achievement of a maximum gain of 23.14dBi (θ=90▫) and a minimum data rate of 1.44Gbps with -10dB bandwidth of 2.1GHz (256-QAM) approximately a distance of 478m from the 5G Base station. The m-MIMO structure gives an Envelope Correlation Coefficient of 0.015. The propagation analysis is carried out to substantiate the performance of the proposed system based on field strength and received power. Network Analysis for better reception performance is carried out by changing the antenna height placement, altering the down tilt of the antenna array, and sweeping the polarization angle of the antenna array
Coverage measurements of NB-IoT technology
Abstract. The narrowband internet of things (NB-IoT) is a cellular radio access technology that provides seamless connectivity to wireless IoT devices with low latency, low power consumption, and long-range coverage. For long-range coverage, NB-IoT offers a coverage enhancement (CE) mechanism that is achieved by repeating the transmission of signals. Good network coverage is essential to reduce the battery usage and power consumption of IoT devices, while poor network coverage increases the number of repetitions in transmission, which causes high power consumption of IoT devices. The primary objective of this work is to determine the network coverage of NB-IoT technology under the University of Oulu’s 5G test network (5GTN) base station. In this thesis work, measurement results on key performance indicators such as reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal to noise plus interference (SINR) have been reported. The goal of the measurement is to find out the NB-IoT signal strength at different locations, which are served by the 5GTN cells configured with different parameters, e.g., Tx power levels, antenna tilt angles.
The signal strength of NB-IoT technology has been measured at different places under the 5GTN base station in Oulu, Finland. Drive tests have been conducted to measure the signal strength of NB-IoT technology by using the Quectel BG96 module, Qualcomm kDC-5737 dongle and Keysight Nemo Outdoor software. The results have shown the values of RSRP, RSRQ, RSSI, and SINR at different locations within several kilometres of the 5GTN base stations. These values indicate the performance of the network and are used to assess the performance of network services to the end-users.
In this work, the overall performance of the network has been checked to verify if network performance meets good signal levels and good network coverage. Relevant details of the NB-IoT technology, the theory behind the signal coverage and comparisons with the measurement results have also been discussed to check the relevance of the measurement results
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