Concurrent Load-Aware Adjustment of User Association and Antenna Tilts in Self-Organizing Radio Networks

Fehske, Albrecht; Klessig, Henrik; Voigt, Jens; Fettweis, Gerhard

doi:10.1109/tvt.2013.2252474

Cited by 83 publications

(84 citation statements)

References 19 publications

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“…In particular, CCO is usually performed by changing antenna bearings [6,19,20,[24][25][26][27][28][29][30] or power settings [11-13, 15, 16]. Strategies to modify antenna bearings include both self-planning [6,19,[24][25][26] and self-optimization methods [20,[27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Buenestado

Toril

Luna-Ramírez

et al. 2017

Mobile Information Systems

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A computationally efficient self-planning algorithm for adjusting base station transmit power in a LTE system on a cell-by-cell basis is presented. The aim of the algorithm is to improve the overall network spectral efficiency in the downlink by reducing the transmit power of specific cells to eliminate interference problems. The main driver of the algorithm is a new indicator that predicts the impact of changes in the transmit power of individual cells on the overall network Signal to Interference plus Noise Ratio (SINR) for the downlink. Algorithm assessment is carried out over a static system-level simulator implementing a live LTE network scenario. During assessment, the proposed algorithm is compared with a state-of-the-art self-planning algorithm based on the modification of antenna tilt angles. Results show that the proposed algorithm can improve both network coverage and capacity significantly compared to other automatic planning methods.

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Section: Related Workmentioning

confidence: 99%

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Buenestado

Toril

Luna-Ramírez

et al. 2017

Mobile Information Systems

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“…Step 2, we use the fact that the load ρ induced by a given assignment X is a fixed point of the following standard interference mapping (see [36,37] and the references therein for further details):…”

Section: Regarding the Load Computation Inmentioning

confidence: 99%

Traffic demand-aware topology control for enhanced energy-efficiency of cellular networks

Pollakis¹,

Cavalcante²,

Stańczak³

2016

J Wireless Com Network

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The service provided by current mobile networks is not adapted to spatio-temporal fluctuations in traffic demand, but such fluctuations offer opportunities for energy savings. In particular, significant gains in energy efficiency are realizable by disengaging temporarily redundant hardware components of base stations. We therefore propose a novel optimization framework that considers both the load-dependent energy radiated by the antennas and the remaining forms of energy needed for operating the base stations. The objective is to reduce the energy consumption of mobile networks, while ensuring that the data rate requirements of the users are met throughout the coverage area. Building upon sparse optimization techniques, we develop a majorization-minimization algorithm with the ability to identify energy-efficient network configurations. The iterative algorithm is load-aware, has low computational complexity, and can be implemented in an online fashion to exploit load fluctuations on a short time scale. Simulations show that the algorithm can find network configurations with the energy consumption similar to that obtained with global optimization tools, which cannot be applied to real large networks. Although we consider only one currently deployed cellular technology, the optimization framework is general, potentially applicable to a large class of access technologies.

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“…Load aware user associations have been studied extensively for different generations of the cellular wireless systems and there are various approaches [3], [5], [6], [8], [9]. In general, it can be divided into two categories: channel borrowing and traffic transfer.…”

Section: Lte User Association and Load Balancingmentioning

confidence: 99%

User association and load balancing in long term evolution network in Addis Ababa, Ethiopia

Bulti

Woldegebreal

Gonzalez³

et al. 2015

Africon 2015

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The exponential growth in mobile data demand is increasing the need for high capacity provisioning. To satisfy this demand, mobile operators are deploying latest generation of cellular technologies, such as Long Term Evolution. Moreover, the non-uniformity in service demand distribution is creating load imbalance on wireless networks. This imbalance reduces the capacity of the networks and various load balancing strategies are being implemented to circumvent it. This paper investigates a geographic load balancing algorithm, called Bubble Oscillation Algorithm in LTE networks and beyond. The BOA is customized to make it feasible in a real LTE network. A realistic test case network in Addis Ababa, Ethiopia, is used to validate the algorithm and compared it with the default LTE user association criteria, the best Reference Signal Received Power. The numerical results show that, BOA improves users throughput at the celledge up to 67.1% as compared to the best RSRP. Considering the number of unassociated users as a second criteria for comparison, service outage for BOA is 7.20% while that of the best RSRP is 40.93% when the service demand is equal to total system capacity, which is a significant improvement.

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Concurrent Load-Aware Adjustment of User Association and Antenna Tilts in Self-Organizing Radio Networks

Cited by 83 publications

References 19 publications

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Traffic demand-aware topology control for enhanced energy-efficiency of cellular networks

User association and load balancing in long term evolution network in Addis Ababa, Ethiopia

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