5 research outputs found
4G/LTE channel quality reference signal trace data set
Mobile networks, especially LTE networks, are used more and more for high-bandwidth services like multimedia or video streams. The quality of the data connection plays a major role in the perceived quality of a service. Videos may be presented in a low quality or experience a lot of stalling events, when the connection is too slow to buffer the next frames for playback. So far, no publicly available data s
Leveraging intelligence from network CDR data for interference aware energy consumption minimization
Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo
RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design
A novel framework is proposed for the deployment and passive beamforming
design of a reconfigurable intelligent surface (RIS) with the aid of
non-orthogonal multiple access (NOMA) technology. The problem of joint
deployment, phase shift design, as well as power allocation is formulated for
maximizing the energy efficiency with considering users' particular data
requirements. To tackle this pertinent problem, machine learning approaches are
adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo
state network (ESN) algorithm is proposed to predict users' tele-traffic demand
by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN)
based position-acquisition and phase-control algorithm is proposed to solve the
joint problem of deployment and design of the RIS. In the proposed algorithm,
the base station, which controls the RIS by a controller, acts as an agent. The
agent periodically observes the state of the RIS-enhanced system for attaining
the optimal deployment and design policies of the RIS by learning from its
mistakes and the feedback of users. Additionally, it is proved that the
proposed D3QN based deployment and design algorithm is capable of converging
within mild conditions. Simulation results are provided for illustrating that
the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between
the prediction accuracy and computational complexity. Finally, it is
demonstrated that the proposed D3QN based algorithm outperforms the benchmarks,
while the NOMA-enhanced RIS system is capable of achieving higher energy
efficiency than orthogonal multiple access (OMA) enabled RIS system