3,621 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
Effect of Location Accuracy and Shadowing on the Probability of Non-Interfering Concurrent Transmissions in Cognitive Ad Hoc Networks
Cognitive radio ad hoc systems can coexist with a primary network in a scanning-free region, which can be dimensioned by location awareness. This coexistence of networks improves system throughput and increases the efficiency of radio spectrum utilization. However, the location accuracy of real positioning systems affects the right dimensioning of the concurrent transmission region. Moreover, an ad hoc connection may not be able to coexist with the primary link due to the shadowing effect. In this paper we investigate the impact of location accuracy on the concurrent transmission probability and analyze the reliability of concurrent transmissions when shadowing is taken into account. A new analytical model is proposed, which allows to estimate the resulting secure region when the localization uncertainty range is known. Computer simulations show the dependency between the location accuracy and the performance of the proposed topology, as well as the reliability of the resulting secure region
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Reconfigurable intelligent surfaces (RISs) have received considerable
attention as a key enabler for envisioned 6G networks, for the purpose of
improving the network capacity, coverage, efficiency, and security with low
energy consumption and low hardware cost. However, integrating RISs into the
existing infrastructure greatly increases the network management complexity,
especially for controlling a significant number of RIS elements. To unleash the
full potential of RISs, efficient optimization approaches are of great
importance. This work provides a comprehensive survey on optimization
techniques for RIS-aided wireless communications, including model-based,
heuristic, and machine learning (ML) algorithms. In particular, we first
summarize the problem formulations in the literature with diverse objectives
and constraints, e.g., sum-rate maximization, power minimization, and imperfect
channel state information constraints. Then, we introduce model-based
algorithms that have been used in the literature, such as alternating
optimization, the majorization-minimization method, and successive convex
approximation. Next, heuristic optimization is discussed, which applies
heuristic rules for obtaining low-complexity solutions. Moreover, we present
state-of-the-art ML algorithms and applications towards RISs, i.e., supervised
and unsupervised learning, reinforcement learning, federated learning, graph
learning, transfer learning, and hierarchical learning-based approaches.
Model-based, heuristic, and ML approaches are compared in terms of stability,
robustness, optimality and so on, providing a systematic understanding of these
techniques. Finally, we highlight RIS-aided applications towards 6G networks
and identify future challenges.Comment: This paper has been accepted by IEEE Communications Surveys and
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