104 research outputs found

    A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

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    The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments

    LTE-Advanced radio access enhancements: A survey

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    Long Term Evolution Advanced (LTE-Advanced) is the next step in LTE evolution and allows operators to improve network performance and service capabilities through smooth deployment of new techniques and technologies. LTE-Advanced uses some new features on top of the existing LTE standards to provide better user experience and higher throughputs. Some of the most significant features introduced in LTE-Advanced are carrier aggregation, enhancements in heterogeneous networks, coordinated multipoint transmission and reception, enhanced multiple input multiple output usage and deployment of relay nodes in the radio network. Mentioned features are mainly aimed to enhance the radio access part of the cellular networks. This survey article presents an overview of the key radio access features and functionalities of the LTE-Advanced radio access network, supported by the simulation results. We also provide a detailed review of the literature together with a very rich list of the references for each of the features. An LTE-Advanced roadmap and the latest updates and trends in LTE markets are also presented

    Spectral and Energy Efficiency in Cellular Mobile Radio Access Networks

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    Driven by the widespread use of smartphones and the release of a wide range of online packet data services, an unprecedented growth in the mobile data usage has been observed over the last decade. Network operators recently realised that the traditional approach of deploying more macrocells could not cope with this continuous growth in mobile data traffic and if no actions are taken, the energy demand to run the networks, which are able to support such traffic volumes risks to become unmanageable. In this context, comprehensive investigations of different cellular network deployments, and various algorithms have been evaluated and compared against each other in this thesis, to determine the best deployment options which are able to deliver the required capacity at a minimum level of energy consumption. A new scalable base station power consumption model was proposed and a joint evaluation framework for the relative improvements in throughput, energy consumption,and energy efficiency is adopted to avoid the inherent ambiguity of using only the bit/J energy efficiency metric. This framework was applied to many cellular network cases studies including macro only, small cell only and heterogeneous networks to show that pure small cell deployments outperform the macro and heterogeneous networks in terms of the energy consumption even if the backhaul power consumption is included in the analysis. Interestingly, picocell only deployments can attain up to 3 times increase in the throughput and 2.27 times reduction in the energy consumed when compared with macro only RANs at high target capacities, while it offers 2 times more throughput and reduces the energy consumption by 12% when compared with the macro/pico HetNet deployments. Further investigations have focused on improving the macrocell RAN by adding more sectors and more antennas. Importantly, the results have shown that adding small cells to the macrocell RAN is more energy efficient than adding more sectors even if adaptive sectorisation techniques are employed. While dimensioning the network by using MIMO base stations results in less consumed energy than using SISO base stations. The impact of traffic offloading to small cell, sleep mode, and inter-cell interference coordination techniques on the throughput and energy consumption in dense heterogeneous network deployments have been investigated. Significant improvements in the throughput and energy efficiency in bit/J were observed. However, a decrease in the energy consumption is obtained only in heterogeneous networks with small cells deployed to service clusters of users. Finally, the same framework is used to evaluate the throughput and energy consumption of massive MIMO deployments to show the superiority of massive MIMOs versus macrocell RANs, small cell deployments and heterogeneous networks in terms of achieving the target capacity with a minimum level of energy consumption. 1.6 times reduction in the energy consumption is achieved by massive MIMOs when compared with picocell only RAN at the same target capacity and when the backhaul power consumption is included in the analysis

    Reducing the power consumption in wireless access networks: overview and recommendations

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    Due to growing importance of wireless access and the steeply growing data volumes being transported, the power consumption of wireless access networks will become an important issue in the coming years. This paper presents a model for this power consumption and investigates three base station types: macrocell, microcell, and femtocell base stations. Based on these models, the coverage effectiveness of the three base station types is compared and the influence of some power reducing techniques such as sleep modes and MIMO (Multiple Input Multiple Output) is evaluated

    User Association and Enabling Technologies in Next Generation 5G Ultra-Dense Networks – A Review

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    Embedding small cells and relay nodes in a macro-cellular network is a promising method for achieving substantial gains in coverage and capacity compared to traditional macro only networks. These new types of base-stations can operate on the same wireless channel as the macro-cellular network, providing higher spatial reuse via cell splitting. However, these base-stations are deployed in an unplanned manner, can have very different transmit powers, and may not have traffic aggregation among many users. This could potentially result in much higher interference magnitude and variability. Hence, such deployments require the use of innovative cell association and inter-cell interference coordination techniques in order to realize the promised capacity and coverage gains. In this paper, we review various techniques for user association and interference mitigation which are required to meet increased data demand in next generation 5G ultra-dense networ
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