Traffic-aware cell management for green ultra-dense small cell networks

To reduce the power consumption of fifth-generation ultradense small-cell networks, base stations can be switched to low-power sleep modes when local traffic levels are low. In this paper, a novel sleep mode control algorithm is proposed to control such sleep modes. The algorithm innovates a concept called traffic-aware cell management (TACM). It involves cell division, cell death, and cell migration to represent adaptations of networks, where the state transitions of base stations are controlled. Direction of arrival (DOA) is adopted for distributed decision making. The TACM algorithm aims at reducing the network power consumption while alleviating the impacts of applying sleep modes, such as mitigating system overheads and reducing user transmission power. The TACM algorithm is compared with a recent consolidated baseline scheme by simulation on networks with unbalanced traffic distributions and with base stations at random locations. In contrast, the TACM algorithm shows a significant improvement in mitigating system overheads due to the absence of load information exchange overhead and up to 72 times less switching frequency. Up to 81% network power consumption can be reduced compared with the baseline scheme if considering high energy consumption of switching transient states. In addition, at a low traffic level, average uplink transmission power is reduced by 79% comparatively. Furthermore, the impact of important performance-governing parameters of the TACM algorithm is analyzed. The insensitivity to the estimation accuracy of DOA is also demonstrated. The results show that the proposed TACM algorithm has a comprehensive advantage of power reduction and overhead mitigation over the baseline scheme

Non-orthogonal Multiple Access (NOMA) with Asynchronous Interference Cancellation

Non-orthogonal multiple access (NOMA) allows allocating one carrier to more than one user at the same time in one cell. It is a promising technology to provide high throughput due to carrier reuse within a cell. In this thesis, a novel interference cancellation (IC) technique is proposed for asynchronous NOMA systems, which uses multiple symbols from each interfering user to carry out IC. With the multiple symbol information from each interfering user the IC performance can be improved substantially. The proposed technique creates and processes so called "IC Triangles". That is, the order of symbol detection is based on detecting all the overlapping symbols of a stonger user before detecting a symbol of a weak user. Also, successive IC (SIC) is employed in the proposed technique. Employing IC Triangles together with the SIC suppresses co-channel interference from strong (earlier detected) signals for relatively weak (yet to be detected) signals and make it possible to achieve low bit error rate (BER) for all users. Further, iterative signal processing is used to improve the system performance. Employing multiple iterations of symbol detection which is based on exploiting a priori estimate obtained from the previous iteration can improve the detection and IC performances. The BER and capacity performance analyses of an uplink NOMA system with the proposed IC technique are presented, along with the comparison to orthogonal frequency division multiple access (OFDMA) systems. Performance analyses validate the requirement for a novel IC technique that addresses asynchronism at NOMA uplink transmissions. Also, numerical and simulation results show that NOMA with the proposed IC technique outperforms OFDMA for uplink transmissions. It is also concluded from the research that, in the NOMA system, users are required to have large received power ratio to satisfy BER requirements and the required received power ratio increases with increasing the modulation level. Also, employing iterative IC provides significant performance gain in NOMA and the number of required iterations depend on the modulation level and detection method. Further, at uplink transmissions, users' BER and capacity performances strongly depend on the relative time offset between interfering users, besides the received power ratio

Pré-distorção neuronal analógica de amplificadores de potência

Mestrado em Engenharia Electrónica e TelecomunicaçõesAs especificações das redes de telecomunicações de quinta geração ultrapassam largamente as capacidades das técnicas mais modernas de linearização de amplificadores de potência como a pré-distorção digital. Por esta razão, esta tese propõe um método de linearização alternativo: um prédistorçor analógico, à banda base, constituído por uma rede neuronal artificial. A rede foi treinada usando três métodos distintos: avaliação de política através de TD(λ), otimização por estratégias de evolução como CMA-ES, e um algoritmo original de aproximações sucessivas. Apesar do TD(λ) não ter produzido resultados de simulação satisfatórios, os resultados dos outros dois métodos foram excelentes: um NMSE entre as funções de transferência pretendida e efetiva do amplificador pré-distorcido até -70 dB, e uma redução total das componentes de distorção do espetro de frequência de um sinal GSM de teste. Apesar das estratégias de evolução terem alcançado este nível de linearização após cerca de 4 horas de execução contínua, o algoritmo original consegue fazê-lo numa questão de segundos. Desta forma, esta tese abre caminho para que se cumpram as exigências das redes de nova geração.Fifth-generation telecommunications networks are expected to have technical requirements which far outpace the capabilities of modern power amplifier (PA) linearization techniques such as digital predistortion. For this reason, this thesis proposes an alternative linearization method: a base band analog predistorter consisting of an artificial neural network. The network was trained through three very distinct methods: policy evaluation using TD(λ), optimization using evolution strategies such as CMA-ES, and an original algorithm of successive approximations. While TD(λ) proved to be unsuccessful, the other two methods produced excellent simulation results: an NMSE between the target and the predistorted PA transfer functions up to -70 dB, and the complete elimination of distortion components in the frequency spectrum of a GSM test signal. While the evolution strategies achieved this level of linearization after about 4 hours of continuous work, the original algorithm consistently does so in a matter of seconds. In effect, this thesis outlines a way towards the meeting of the specifications of next-generation networks

Coverage and throughput analysis with a non-uniform small cell deployment

Small cell network (SCN) offers, for the first time, a low-cost and scalable mechanism to meet the forecast data-traffic demand. In this paper, we propose a non-uniform SCN deployment scheme. The small cell base stations (BSs) in this scheme will not be utilized in the region within a prescribed distance away from any macrocell BSs, defined as the inner region. Based upon the analytical framework provided in this work, the downlink coverage and single user throughput are precisely characterized. Provided that the inner region size is appropriately chosen, we find that the proposed non-uniform SCN deployment scheme can maintain the same level of cellular coverage performance even with 50% less small cell BSs used than the uniform SCN deployment, which is commonly considered in the literature. Furthermore, both the coverage and the single user throughput performance will significantly benefit from the proposed scheme, if its average small cell density is kept identical to the uniform SCN deployment. This work demonstrates the benefits obtained from a simple non-uniform SCN deployment, thus highlighting the importance of deploying small cells selectively

Stochastic geometric analysis of energy efficiency in two-tier heterogeneous networks

The exponential growth in the number of users of cellular mobile networks (and their requirements) has created a massive challenge for network operators to cope with demands for coverage and data rates. Among the possible solutions for the ever increasing user needs, the deployment of Heterogeneous Networks (HetNets) constitutes both a practical and an economical solution. Moreover, while the typical approach for network operators has been to consider the coverage and data rates as design parameters in a network, a major concern for next generation networks is the efficiency in the power usage of the network. Therefore, in recent years the energy efficiency parameter has gathered a great deal of attention in the design of next generation networks. In the context of HetNets, while the densification of the network in terms of the number of base stations deployed can potentially increase the coverage and boost the data rates, it can also lead to a huge power consumption as the energy used escalates with the number of base stations deployed. To this end, the purpose of this thesis is to investigate the energy efficiency performance of different deployment strategies in a HetNet consisting of macro- and femtocells. We make use of well established tools from stochastic geometry to model the different strategies, as it provides a theoretical framework from which the scalability of the network in terms of the design parameters can be taken into account. Those strategies consisted first, on the analysis of the effect of using multiple antennas and diversity schemes on both, the throughput and the energy efficiency of the network. The optimum diversity schemes and antenna configurations were found for an optimal energy efficiency while keeping constraints on the quality of Service of both tiers. Then, the effect of the vertical antenna tilt was analyzed for both, a traditional macrocell only network and a two-tier network. The optimum antenna tilt in terms of energy efficiency was found while keeping constraints on the Quality of Service required. Finally, an energy efficient deployment of femtocells was proposed where the smart positioning of femtocells derived into improvements of coverage probability, effective throughput and energy efficiency of the network. The proposed model also improved in general the performance of the cell edge user which in turn resulted in a more balanced network in terms of the overall performance

Intelligent on-demand radio resource provisioning for green ultra-small cell networks

This thesis studies intelligent on-demand radio resource provisioning involving sleep mode operation in ultra Small Cell Networks (SCNs). Sleep modes are low power states of base stations. The purpose of the research is to investigate how appropriate traffic information can be adopted in sleep mode operation schemes for SCNs with different architectures. A novel protocol-friendly sleep mode operation algorithm based on Adaptive Traffic Perception is proposed for distributed SCN architectures. It is proved robust to different SCN layouts with the reduction in the average power consumption of base stations being more than 35% while maintaining the Quality of Service. The Traffic-aware Cell Management scheme adopting Direction of Arrival information is particularly designed to eliminate the necessity of computation for sleeping base stations. This scheme is shown to significantly reduce the side effects associated with the sleep mode operation, including system overheads and the increasing user transmission power. For SCNs using centralised architectures, such as Cloud Radio Access Networks, Hotspot-oriented Green Frameworks are proposed for different information availabilities, which achieve almost 80% reduction in power consumption of Remote Radio Heads at low traffic levels. A clustering technique is utilised for the optimisation of the placement of active Remote Radio Heads, lowering the average user transmission power. The amount of reduction depends on the completeness of the information and can exceed 70% compared with the state-of-the-art. A type II Matern Hard-core Point Process is used for modelling SCNs. The derivation and approximation of its distance distributions are also proposed. The distance distributions are used for the probabilistic theoretical analysis of some metrics of the sleep mode operation