Spectral- and energy-efficient antenna tilting in a HetNet using reinforcement learning

In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm

Energy Efficiency of Ultra-Dense Small Cell Radio Access Networks for 5G and Beyond

Small cell base station (BS) densification in the radio access network (RAN) is an effective solution to improve the RAN capacity. However, small cell BS densification by adding more non-zero energy-consuming BSs increases energy consumption, compromising energy efficiency, which can be mitigated by adopting sleep mode. A comprehensive evaluation framework is applied in this research to analyse the capacity, energy consumption, and energy efficiency performance of the ultra-dense small cell RANs as a complete energy efficiency assessment, which is lacking in the literature. The impact of advanced techniques millimetre wave (mmWave), antenna array beamforming, and integrated access and backhaul (IAB) on RAN energy efficiency are also investigated. MATLAB- based simulation results show that the ultra-dense small cell RANs, where the number of BSs greatly exceeds the number of active user equipment (UEs), can only be energy efficient if all the empty cells without UE association are turned off completely. Energy efficiency enhancement comes from capacity improvement and energy consumption constraint. Specifically, the ultra-dense small cell RANs can achieve maximum performance improvement of 7.56-fold and 2.35-fold regarding capacity, 3780.11-fold and 32.38-fold regarding energy consumption using the current power model, and 28591.53-fold and 75.97-fold regarding energy efficiency in homogeneous and heterogeneous infrastructures, respectively, comparing the cases with and without the sleep mode. In addition, mmWave and IAB trade energy consumption and energy efficiency for capacity improvement and backhaul cost reduction. With mmWave and IAB, dense small cell RAN can achieve a maximum of 2.55-fold and 1.70-fold for capacity improvement, 2.46-fold and 2.89-fold for energy consumption reduction using the current power model, and 6.27-fold and 8.34-fold energy efficiency enhancement for UE densities of 900 and 300 UEs/km2, respectively, comparing the cases with and without the sleep mode