Name
Title | ||
|---|---|---|
Spectral- and energy-efficient antenna tilting in a HetNet using reinforcement learning |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WCNC.2013.6554660 | WCNC |
Keywords | Field | DocType |
network-wide knowledge,dynamic user distribution,optimal down-tilt angle,capacity saturation,radiofrequency interference,fixed tilt strategy,cellular radio,mobile antennas,bs antennas,learning (artificial intelligence),quality of service,energy-efficient antenna tilting,irregular antenna patterns,homogeneous networks,tilt dependent performance,transmission techniques,stochastic channel effects,multicell interference-limited network,access-points,user throughput fairness,spectral-efficient antenna tilting,uniform quality-of-service,telecommunication computing,qos,optimal solution convergence rate,hetnet,base-station antennas,heterogeneous network,reinforcement learning algorithm,cellular networks,efficiency 21 percent to 47 percent,self-learning algorithm,cross-layer techniques,throughput,convergence,learning artificial intelligence,interference | Convergence (routing),Computer science,Efficient energy use,Computer network,Communication channel,Rate of convergence,Cellular network,Heterogeneous network,Throughput,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
1525-3511 E-ISBN : 978-1-4673-5937-5 | 978-1-4673-5937-5 | 6 |
PageRank | References | Authors |
0.43 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weisi Guo | 1 | 550 | 60.46 |
| Siyi Wang | 2 | 329 | 25.98 |
| Y. Wu | 3 | 1178 | 139.36 |
| Jonathan Rigelsford | 4 | 8 | 1.91 |
| Xiaoli Chu | 5 | 1289 | 80.99 |
| Tim O'Farrell | 6 | 312 | 31.99 |