47 research outputs found
Adaptive Beam-Frequency Allocation Algorithm with Position Uncertainty for Millimeter-Wave MIMO Systems
Envisioned for fifth generation (5G) systems, millimeter-wave (mmWave)
communications are under very active research worldwide. Although pencil beams
with accurate beamtracking may boost the throughput of mmWave systems, this
poses great challenges in the design of radio resource allocation for highly
mobile users. In this paper, we propose a joint adaptive beam-frequency
allocation algorithm that takes into account the position uncertainty inherent
to high mobility and/or unstable users as, e.g., Unmanned Aerial Vehicles
(UAV), for whom this is a major problem. Our proposed method provides an
optimized beamwidth selection under quality of service (QoS) requirements for
maximizing system proportional fairness, under user position uncertainty. The
rationale of our scheme is to adapt the beamwidth such that the best trade-off
among system performance (narrower beam) and robustness to uncertainty (wider
beam) is achieved. Simulation results show that the proposed method largely
enhances the system performance compared to reference algorithms, by an
appropriate adaptation of the mmWave beamwidths, even under severe
uncertainties and imperfect channel state information (CSIs).Comment: 5 pages, 6 figures, 1 table, 1 algorith
Reinforcement Learning in Self Organizing Cellular Networks
Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs.
In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions.
In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks
Five Facets of 6G: Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the
globe, researchers have turned their attention to the exploration of radical
next-generation solutions. At this early evolutionary stage we survey five main
research facets of this field, namely {\em Facet~1: next-generation
architectures, spectrum and services, Facet~2: next-generation networking,
Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing,
as well as Facet~5: applications of deep learning in 6G networks.} In this
paper, we have provided a critical appraisal of the literature of promising
techniques ranging from the associated architectures, networking, applications
as well as designs. We have portrayed a plethora of heterogeneous architectures
relying on cooperative hybrid networks supported by diverse access and
transmission mechanisms. The vulnerabilities of these techniques are also
addressed and carefully considered for highlighting the most of promising
future research directions. Additionally, we have listed a rich suite of
learning-driven optimization techniques. We conclude by observing the
evolutionary paradigm-shift that has taken place from pure single-component
bandwidth-efficiency, power-efficiency or delay-optimization towards
multi-component designs, as exemplified by the twin-component ultra-reliable
low-latency mode of the 5G system. We advocate a further evolutionary step
towards multi-component Pareto optimization, which requires the exploration of
the entire Pareto front of all optiomal solutions, where none of the components
of the objective function may be improved without degrading at least one of the
other components
MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems
In this paper, we consider a prospective receiving hybrid beamforming
structure consisting of several radio frequency (RF) chains and abundant
antenna elements in multi-input multi-output (MIMO) systems. Due to
conventional costly full connections, we design an enhanced partially-connected
beamformer employing low-density parity-check (LDPC) based structure. As a
benefit of LDPC-based structure, information can be exchanged among clustered
RF/antenna groups, which results in a low computational complexity order.
Advanced message passing (MP) capable of inferring and transferring data among
different paths is designed to support LDPC-based hybrid beamformer. We propose
a message passing enhanced antenna and RF chain selection (MARS) scheme to
minimize the operational power of antennas and RF chains of the receiver.
Furthermore, sequential and parallel MP for MARS are respectively designed as
MARS-S and MARS-P schemes to address convergence speed issue. Simulations have
validated the convergence of both the MARS-P and the MARS-S algorithms. Owing
to asynchronous information transfer of MARS-P, it reveals that higher power is
required than that of MARS-S, which strikes a compelling balance between power
consumption, convergence, and computational complexity. It is also demonstrated
that the proposed MARS scheme outperforms the existing benchmarks using
heuristic method of fully-/partially-connected architectures in open literature
in terms of the lowest power and highest energy efficiency