119 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Sensor Path Planning for Emitter Localization
The localization of a radio frequency (RF) emitter is relevant in many military and civilian applications. The recent decade has seen a rapid progress in the development of small and mobile unmanned aerial vehicles (UAVs), which offer a way to perform emitter localization autonomously. The path a UAV travels influences the localization significantly, making path planning an important part of a mobile emitter localization system.
The topic of this thesis is path planning for a UAV that uses bearing measurements to localize a stationary emitter. Using a directional antenna, the direction towards the target can be determined by the UAV rotating around its own vertical axis. During this rotation the UAV is required to remain at the same position, which induces a trade-off between movement and measurement that influences the optimal trajectories.
This thesis derives a novel path planning algorithm for localizing an emitter with a UAV. It improves the current state of the art by providing a localization with defined accuracy in a shorter amount of time compared to other algorithms in simulations. The algorithm uses the policy rollout principle to perform a nonmyopic planning and to incorporate the uncertainty of the estimation process into its decision. The concept of an action selection algorithm for policy rollout is introduced, which allows the use of existing optimization algorithms to effectively search the action space. Multiple action selection algorithms are compared to optimize the speed of the path planning algorithm. Similarly, to reduce computational demand, an adaptive grid-based localizer has been developed.
To evaluate the algorithm an experimental system has been built and the algorithm was tested on this system. Based on initial experiments, the path planning algorithm has been modified, including a minimal distance to the emitter and an outlier detection step. The resulting algorithm shows promising results in experimental flights
Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming
Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path
In millimeter-wave channels, most of the received energy is carried by a few
paths. Traditional precoders sweep the angle-of-departure (AoD) and
angle-of-arrival (AoA) space with directional precoders to identify directions
with largest power. Such precoders are heuristic and lead to sub-optimal
AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao
bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the
transmitter and spatial filtering of a single path. The precoders are found by
solving a suitable convex optimization problem. We demonstrate that the
accuracy can be improved by at least a factor of two over traditional
precoders, and show that there is an optimal number of distinct precoders
beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9
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