Beam Management Driven by Radio Environment Maps in O-RAN Architecture
- URL: http://arxiv.org/abs/2303.11742v1
- Date: Tue, 21 Mar 2023 11:09:31 GMT
- Title: Beam Management Driven by Radio Environment Maps in O-RAN Architecture
- Authors: Marcin Hoffmann, Pawel Kryszkiewicz
- Abstract summary: M-MIMO is considered as one of the key technologies in 5G, and future 6G networks.
It is easier to implement an M-MIMO network exploiting a static set of beams, i.e., Grid of Beams (GoB)
Beam Management (BM) can be enhanced by taking into account historical knowledge about the radio environment.
The proposed solution is compliant with the Open Radio Access Network (O-RAN) architecture.
- Score: 2.0305676256390934
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Massive Multiple-Input Multiple-Output (M-MIMO) is considered as one of
the key technologies in 5G, and future 6G networks. From the perspective of,
e.g., channel estimation, especially for high-speed users it is easier to
implement an M-MIMO network exploiting a static set of beams, i.e., Grid of
Beams (GoB). While considering GoB it is important to properly assign users to
the beams, i.e., to perform Beam Management (BM). BM can be enhanced by taking
into account historical knowledge about the radio environment, e.g., to avoid
radio link failures. The aim of this paper is to propose such a BM algorithm,
that utilizes location-dependent data stored in a Radio Environment Map (REM).
It utilizes received power maps, and user mobility patterns to optimize the BM
process in terms of Reinforcement Learning (RL) by using the Policy Iteration
method under different goal functions, e.g., maximization of received power or
minimization of beam reselections while avoiding radio link failures. The
proposed solution is compliant with the Open Radio Access Network (O-RAN)
architecture, enabling its practical implementation. Simulation studies have
shown that the proposed BM algorithm can significantly reduce the number of
beam reselections or radio link failures compared to the baseline algorithm.
Related papers
- Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave
Distributed Multiple Input Multiple Output (D-MIMO) systems [0.5079602839359522]
This paper investigates whether the best AP/beam can be reliably inferred from sounding only a small subset of beams.
We use Random Forest (RF), MissForest (MF) and conditional Generative Adversarial Networks (c-GAN) for demonstrating the performance benefits of inference.
arXiv Detail & Related papers (2023-12-30T09:24:19Z) - Flexible Payload Configuration for Satellites using Machine Learning [33.269035910233704]
Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse.
Recent research reveals the limitations of this approach in heterogeneous traffic scenarios, leading to inefficiencies.
This paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM)
arXiv Detail & Related papers (2023-10-18T13:45:17Z) - DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through
Multi-Agent Deep Reinforcement Learning [0.5818726765408144]
Opportunistic routing relies on the broadcast capability of wireless networks.
To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead.
Common MPR selection algorithms are, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties.
In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm.
arXiv Detail & Related papers (2023-06-16T05:53:42Z) - Deep Learning and Image Super-Resolution-Guided Beam and Power
Allocation for mmWave Networks [80.37827344656048]
We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks.
We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
arXiv Detail & Related papers (2023-05-08T05:40:54Z) - Beam Management in Ultra-dense mmWave Network via Federated
Reinforcement Learning: An Intelligent and Secure Approach [19.01563068819449]
Key challenge of ultra-dense mmWave network (UDmmWave) is beam management due to high propagation delay limited beam coverage.
In this paper, a novel beam management scheme is presented which can theoretically protect user privacy while reducing handoff cost.
arXiv Detail & Related papers (2022-10-04T01:47:33Z) - Artificial Intelligence Empowered Multiple Access for Ultra Reliable and
Low Latency THz Wireless Networks [76.89730672544216]
Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era.
To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required.
This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
arXiv Detail & Related papers (2022-08-17T03:00:24Z) - State-Augmented Learnable Algorithms for Resource Management in Wireless
Networks [124.89036526192268]
We propose a state-augmented algorithm for solving resource management problems in wireless networks.
We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
arXiv Detail & Related papers (2022-07-05T18:02:54Z) - Deep Reinforcement Learning Based Multidimensional Resource Management
for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency.
In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
arXiv Detail & Related papers (2021-09-17T08:55:48Z) - Increasing Energy Efficiency of Massive-MIMO Network via Base Stations
Switching using Reinforcement Learning and Radio Environment Maps [3.781421673607642]
M-MIMO transmission results in high energy consumption growing with the number of antennas.
This paper investigates EE improvement through switching on/off underutilized BSs.
arXiv Detail & Related papers (2021-03-08T21:57:13Z) - Path Design and Resource Management for NOMA enhanced Indoor Intelligent
Robots [58.980293789967575]
A communication enabled indoor intelligent robots (IRs) service framework is proposed.
Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state.
The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
arXiv Detail & Related papers (2020-11-23T21:45:01Z) - Learning Centric Power Allocation for Edge Intelligence [84.16832516799289]
Edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge.
This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model.
Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.
arXiv Detail & Related papers (2020-07-21T07:02:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.